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.gitignore

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collab/Model Training.ipynb

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··· 1 1 + {"cells":[{"cell_type":"markdown","metadata":{"id":"78a2d319"},"source":["# Kima - Basketball Object Detection Model Training\n","\n","### Overview\n","\n","This notebook provides a configurable and automated workflow for training and comparing YOLO models for real-time basketball detection. The objective is to efficiently evaluate different model architectures on various datasets, optimized for lightweight, fast inference suitable for real-time mobile applications.\n","\n","### Enhanced Workflow Features\n","\n","1. **Global Configuration:** A central configuration cell (`Global Configuration`) allows users to define:\n"," * `DATASET_CONFIGS`: A list of Roboflow datasets, each with its API key, workspace, project, version, and a unique alias.\n"," * `MODEL_ARCHITECTURES`: A list of YOLO model variants (e.g., 'yolo11n', 'yolo11s') to be trained and compared.\n"," * `TRAINING_HYPERPARAMETERS`: Common training settings like `epochs`, `imgsz`, `batch`, `patience`, and `device`.\n"," * `Workflow Flags`: Boolean flags to control the execution of specific parts of the notebook, such as `GENERATE_PLOTS` (for dataset analysis plots), `SAVE_TEST_PREDICTION_IMAGES` (for visual test results), and `PERFORM_MODEL_COMPARISON`.\n","\n","2. **Automated Multi-Dataset & Multi-Model Workflow:**\n"," * **Dataset Preparation and Analysis Loop:** The notebook iterates through each defined dataset, automatically downloading it from Roboflow, correcting its `data.yaml` file for Ultralytics compatibility, and performing detailed dataset analysis (class distribution, sample annotated images). All analysis artifacts are saved in dataset-specific folders within the `outputs` directory.\n"," * **Model Training, Validation, and Export Loop:** Nested loops then train each specified YOLO model architecture on every prepared dataset. For each model-dataset combination, the workflow:\n"," * Loads a pre-trained model.\n"," * Trains the model using the global hyperparameters.\n"," * Validates performance, collecting metrics like mAP50, mAP50-95, precision, recall, and inference time.\n"," * Exports the best-trained model to mobile formats: CoreML (`.mlpackage`) for iOS and TFLite (`.tflite`) for Android.\n"," * Generates visual test results (predictions on sample images) to qualitatively assess performance.\n"," All training logs, model weights, exports, and visual tests are saved in uniquely named directories.\n","\n","3. **Comprehensive Comparison Reporting and Visualization:**\n"," * All collected metrics from every model-dataset combination are aggregated into a pandas DataFrame.\n"," * A comparison table is displayed and saved as a CSV file.\n"," * Key comparative visualizations are generated, including:\n"," * Accuracy (mAP50) vs. Speed (Inference Time) scatter plot, with marker size indicating Model Size.\n"," * Bar charts for mAP50, Inference Time, and Model Size.\n"," * Automated recommendations for optimal models are provided based on various criteria (accuracy, speed, balance, size), particularly for real-time mobile deployment.\n","\n","4. **Organized Output Archiving:**\n"," * All generated artifacts (dataset analysis, training runs, exported models, comparison reports, plots) are consolidated into a structured `outputs` directory.\n"," * This entire directory is then compressed into a downloadable zip file, ensuring easy retrieval of all experimental results in Google Colab environments.\n","\n","### Model Architecture\n","\n","We primarily use **YOLOv11 Nano (yolov11n.pt)** and **YOLOv11 Small (yolov11s.pt)** for their balance of speed and accuracy, making them suitable for real-time mobile applications.\n","\n","### Resources\n","\n","- [Ultralytics Documentation](https://docs.ultralytics.com/)\n","- [YOLOv11 GitHub](https://github.com/ultralytics/ultralytics)\n","- **License:** AGPL-3.0 License ([Ultralytics License](https://ultralytics.com/license))"]},{"cell_type":"markdown","metadata":{"id":"rzyauzNMTk8H"},"source":["## Setup & Dependencies\n","\n","Install and verify all required packages for training and exporting the model."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5dIqXyUiTk8H","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1775418739151,"user_tz":-180,"elapsed":89265,"user":{"displayName":"Nathan Holland","userId":"13994014446134761485"}},"outputId":"f2c0d447-7081-40ab-9379-f78aa5d45b7a","collapsed":true},"outputs":[{"output_type":"stream","name":"stdout","text":["\r0% [Working]\r \rHit:1 https://cli.github.com/packages stable InRelease\n","\r0% [Connecting to archive.ubuntu.com (91.189.92.23)] [Connecting to security.ub\r \rGet:2 https://cloud.r-project.org/bin/linux/ubuntu jammy-cran40/ InRelease [3,632 B]\n","Get:3 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 InRelease [1,581 B]\n","Get:4 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 Packages [2,482 kB]\n","Hit:5 http://archive.ubuntu.com/ubuntu jammy InRelease\n","Get:6 http://security.ubuntu.com/ubuntu jammy-security InRelease [129 kB]\n","Get:7 http://archive.ubuntu.com/ubuntu jammy-updates InRelease [128 kB]\n","Get:8 https://r2u.stat.illinois.edu/ubuntu jammy InRelease [6,555 B]\n","Hit:9 https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu jammy InRelease\n","Get:10 https://r2u.stat.illinois.edu/ubuntu jammy/main all Packages [9,980 kB]\n","Hit:11 https://ppa.launchpadcontent.net/graphics-drivers/ppa/ubuntu jammy InRelease\n","Hit:12 https://ppa.launchpadcontent.net/ubuntugis/ppa/ubuntu jammy InRelease\n","Get:13 http://security.ubuntu.com/ubuntu jammy-security/universe amd64 Packages [1,310 kB]\n","Get:14 http://archive.ubuntu.com/ubuntu jammy-backports InRelease [127 kB]\n","Get:15 http://archive.ubuntu.com/ubuntu jammy-updates/restricted amd64 Packages [7,125 kB]\n","Get:16 http://security.ubuntu.com/ubuntu jammy-security/main amd64 Packages [3,844 kB]\n","Get:17 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 Packages [4,219 kB]\n","Get:18 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 Packages [1,622 kB]\n","Get:19 https://r2u.stat.illinois.edu/ubuntu jammy/main amd64 Packages [2,960 kB]\n","Fetched 33.9 MB in 5s (7,183 kB/s)\n","Reading package lists... 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packages: pybind11, tflite-support\n","Successfully installed pybind11-3.0.3 tflite-support-0.1.0a1\n"]}],"source":["\n","# Install the system dependencies required to build pillow-avif-plugin\n","!sudo apt-get update && sudo apt-get install -y libavif-dev libheif-dev\n","\n","# Install Ultralytics YOLOv11 and Roboflow\n","!pip install ultralytics roboflow\n","\n","# Install TensorFlow and tflite-support for metadata verification (optional but recommended)\n","!pip install tensorflow tflite-support"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"XVvGyi_fTk8I","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1775418743632,"user_tz":-180,"elapsed":4479,"user":{"displayName":"Nathan Holland","userId":"13994014446134761485"}},"outputId":"404e06b2-9b6a-44d4-ec8f-d465973947b1"},"outputs":[{"output_type":"stream","name":"stdout","text":["Ultralytics 8.4.33 🚀 Python-3.12.13 torch-2.10.0+cu128 CUDA:0 (NVIDIA L4, 22563MiB)\n","Setup complete ✅ (12 CPUs, 53.0 GB RAM, 43.4/235.7 GB disk)\n"]}],"source":["# Verify installation\n","import ultralytics\n","ultralytics.checks()"]},{"cell_type":"markdown","metadata":{"id":"74019e8d"},"source":["## Global Configuration\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"67772b77"},"outputs":[],"source":["### Global Configuration\n","\n","# 1. Dataset Configurations\n","# You can define multiple dataset configurations and switch between them.\n","# The 'ALIAS' key can be used to refer to a specific dataset later.\n","# Replace 'YOUR_API_KEY_HERE' with your actual Roboflow API key.\n","DATASET_CONFIGS = [\n"," {\n"," 'ROBOFLOW_API_KEY': '49Mchq1NPmGmyHtYhkkp', # IMPORTANT: Replace with your actual Roboflow API key or use os.getenv()\n"," 'WORKSPACE': 'kima-rbjnn',\n"," 'PROJECT': 'dataset-3k5b6',\n"," 'VERSION': 8,\n"," 'ALIAS': 'dataset'\n"," },\n"," # Add more dataset configurations here if needed\n","]\n","\n","# Select the active dataset configuration by its ALIAS\n","ACTIVE_DATASET_ALIAS = 'dataset'\n","\n","# 2. Model Architectures for Comparison\n","# Define a list of YOLO model variants (e.g., 'yolo11n', 'yolo11s') to train and compare.\n","# Ensure these models are compatible with Ultralytics.\n","MODEL_ARCHITECTURES = ['yolo11n']\n","# MODEL_ARCHITECTURES = ['yolo26n','yolo11n', 'yolo11s', 'yolov8n', 'yolov10n']\n","\n","# 3. Training Hyperparameters\n","# These parameters will be applied to all models during training.\n","TRAINING_HYPERPARAMETERS = {\n"," 'epochs': 100, # Number of training epochs. Use a smaller number (e.g., 1-5) for quick tests.\n"," 'imgsz': 900, # Image size for training and inference. 416 for mobile, 640 for higher accuracy.\n"," 'batch': 8, # Batch size. Adjust based on GPU memory. (imgsz increase, batch decrease)\n"," 'patience': 20, # Early stopping patience. Number of epochs to wait for improvement.\n"," 'device': 0, # GPU device (0 for first GPU). Use 'cpu' if no GPU available.\n"," 'optimizer': 'AdamW', # AdamW = good default for transfer learning (stable, fast convergence).\n"," 'lr0': 0.001, # Initial learning rate. Controls how big each update step is. Too high - unstable, too low - slow learning.\n"," 'lrf': 0.01, # Final learning rate factor. Final LR = lr0 * lrf. Helps fine-tune weights toward the end of training.\n"," 'weight_decay': 0.0005, # Regularization term to prevent overfitting. Penalizes large weights → improves generalization.\n"," #'warmup_epochs': 3.0, # Gradually increases learning rate at the start. Prevents unstable early training (especially with pretrained models).\n"," #'mosaic': 0.0, # Combines 4 images into 1 during training. Improves generalization, but makes objects smaller. Useful, but must be controlled for small objects.\n"," #'close_mosaic': 0, # Disables mosaic in the last N epochs. VERY IMPORTANT: lets model learn real (non-distorted) object sizes.\n"," #'scale': 0.5, # Random zoom in/out (0.5 → 1.5 range). Important for small objects → sometimes makes them larger.\n"," #'translate': 0.1, # Randomly shifts image (10%). Helps model learn object positions more robustly.\n"," #'degrees': 5.0, # Random rotation (±5 degrees). Simulates camera tilt. Keep small for realistic scenes.\n"," #'fliplr': 0.5, # Horizontal flip probability (50%). Very useful for sports scenes (court symmetry).\n","}\n","\n","# 4. Workflow Flags\n","# Control which parts of the notebook workflow to execute.\n","GENERATE_PLOTS = True # Generate and display dataset analysis plots\n","SAVE_TEST_PREDICTION_IMAGES = True # Save predicted images during visual testing\n","PERFORM_MODEL_COMPARISON = True # Run multi-model comparison training and evaluation\n"]},{"cell_type":"markdown","metadata":{"id":"f7a60cca"},"source":["## Dataset Preparation and Analysis Loop\n","This section automates the preparation and initial analysis of each dataset defined in the `DATASET_CONFIGS`. For each dataset, the workflow performs the following key steps:\n","\n","1. **Download from Roboflow**: The dataset is automatically downloaded from Roboflow using the provided API key and project details. This ensures consistency and easy access to data versions.\n","2. **Correct `data.yaml`**: The `data.yaml` configuration file, which Ultralytics models use to locate image and label files, is updated to contain absolute paths. This correction is crucial for seamless integration with the training pipeline.\n","3. **Class Distribution Analysis**: If `GENERATE_PLOTS` is enabled, the distribution of object classes within the training and validation sets is analyzed and visualized. This helps identify potential class imbalances and provides insights into the dataset composition.\n","4. **Sample Annotated Images**: A selection of sample images from the training set, complete with their bounding box annotations, are plotted and saved. This visual check confirms the correctness of annotations and helps in understanding the visual characteristics of the dataset.\n","5. **Dataset Statistics**: Basic statistics, such as the number of images and label files in the training, validation, and test sets, are gathered and displayed to provide a quantitative overview of the dataset's size and structure.\n","\n","All analysis plots and information are saved into dataset-specific subdirectories within the `outputs` folder for organized archiving. The processed dataset information, including the path to the corrected `data.yaml` file, is stored for subsequent model training and evaluation steps."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fc402fbc","executionInfo":{"status":"ok","timestamp":1775418743923,"user_tz":-180,"elapsed":270,"user":{"displayName":"Nathan Holland","userId":"13994014446134761485"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"fac54aa9-0d1e-4ee7-889c-c657f27aece5"},"outputs":[{"output_type":"stream","name":"stdout","text":["Initialized imports and 'prepared_datasets_info' list.\n"]}],"source":["import yaml\n","from pathlib import Path\n","import matplotlib.pyplot as plt\n","import matplotlib.patches as patches\n","from PIL import Image\n","import random\n","import os\n","from collections import defaultdict\n","from roboflow import Roboflow\n","\n","# Initialize an empty list to store information about each processed dataset\n","prepared_datasets_info = []\n","\n","print(\"Initialized imports and 'prepared_datasets_info' list.\")"]},{"cell_type":"markdown","metadata":{"id":"c41a63da"},"source":["### Helper Functions\n","\n","This section defines several helper functions used throughout the dataset preparation and analysis process:\n","\n","* `get_class_counts(label_dir, class_names)`: Analyzes YOLO format label files to count instances of each class within a specified directory.\n","* `plot_image_with_bboxes(image_path, label_path, class_names, save_path)`: Visualizes an image with its corresponding YOLO bounding box annotations, saving the result to a specified path.\n","* `get_dataset_stats(image_dir, label_dir)`: Provides basic statistics on the number of images and label files within a given directory pair."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"99adf769","executionInfo":{"status":"ok","timestamp":1775418743954,"user_tz":-180,"elapsed":24,"user":{"displayName":"Nathan Holland","userId":"13994014446134761485"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"c33563b5-311b-4fec-9650-2c457659b0dd"},"outputs":[{"output_type":"stream","name":"stdout","text":["Helper functions defined.\n"]}],"source":["import yaml\n","from pathlib import Path\n","import matplotlib.pyplot as plt\n","import matplotlib.patches as patches\n","from PIL import Image\n","import random\n","import os\n","from collections import defaultdict\n","from roboflow import Roboflow\n","\n","# a. Function to parse YOLO labels and count classes\n","def get_class_counts(label_dir, class_names):\n"," class_counts = defaultdict(int)\n"," total_annotations = 0\n"," if not label_dir.exists():\n"," print(f\"Warning: Label directory not found: {label_dir}\")\n"," return class_counts, total_annotations\n","\n"," for label_file in label_dir.glob('*.txt'):\n"," with open(label_file, 'r') as f:\n"," for line in f:\n"," parts = line.strip().split()\n"," if parts:\n"," try:\n"," class_id = int(parts[0])\n"," if 0 <= class_id < len(class_names):\n"," class_counts[class_names[class_id]] += 1\n"," total_annotations += 1\n"," except ValueError:\n"," print(f\"Warning: Invalid class_id in file {label_file.name}, line: {line.strip()}\")\n"," return class_counts, total_annotations\n","\n","# b. Function to plot image with bounding boxes\n","def plot_image_with_bboxes(image_path, label_path, class_names, save_path):\n"," try:\n"," img = Image.open(image_path).convert(\"RGB\")\n"," except Exception as e:\n"," print(f\"Error loading image {image_path}: {e}\")\n"," return\n","\n"," fig, ax = plt.subplots(1, figsize=(8, 8))\n"," ax.imshow(img)\n","\n"," img_width, img_height = img.size\n","\n"," if label_path.exists():\n"," with open(label_path, \"r\") as f:\n"," for line in f:\n"," parts = line.strip().split()\n"," if not parts:\n"," continue\n","\n"," try:\n"," class_id = int(parts[0])\n"," coords = [float(p) for p in parts[1:]]\n","\n"," x_min_px = y_min_px = width_px = height_px = None\n","\n"," # Case 1: YOLO bbox format\n"," # class_id x_center y_center width height\n"," if len(coords) == 4:\n"," x_center_norm, y_center_norm, width_norm, height_norm = coords\n","\n"," x_min_px = (x_center_norm - width_norm / 2) * img_width\n"," y_min_px = (y_center_norm - height_norm / 2) * img_height\n"," width_px = width_norm * img_width\n"," height_px = height_norm * img_height\n","\n"," # Case 2: YOLO polygon format\n"," # class_id x1 y1 x2 y2 ...\n"," elif len(coords) > 4 and len(coords) % 2 == 0:\n"," x_coords_norm = coords[0::2]\n"," y_coords_norm = coords[1::2]\n","\n"," min_x_norm = min(x_coords_norm)\n"," max_x_norm = max(x_coords_norm)\n"," min_y_norm = min(y_coords_norm)\n"," max_y_norm = max(y_coords_norm)\n","\n"," x_min_px = min_x_norm * img_width\n"," y_min_px = min_y_norm * img_height\n"," width_px = (max_x_norm - min_x_norm) * img_width\n"," height_px = (max_y_norm - min_y_norm) * img_height\n","\n"," else:\n"," print(\n"," f\"Warning: Unsupported label format in {label_path.name}, \"\n"," f\"line: {line.strip()}\"\n"," )\n"," continue\n","\n"," # Optional: clip box to image bounds\n"," x_min_px = max(0, x_min_px)\n"," y_min_px = max(0, y_min_px)\n"," width_px = max(0, min(width_px, img_width - x_min_px))\n"," height_px = max(0, min(height_px, img_height - y_min_px))\n","\n"," rect = patches.Rectangle(\n"," (x_min_px, y_min_px),\n"," width_px,\n"," height_px,\n"," linewidth=2,\n"," edgecolor=\"r\",\n"," facecolor=\"none\"\n"," )\n"," ax.add_patch(rect)\n","\n"," if 0 <= class_id < len(class_names):\n"," label = class_names[class_id]\n"," else:\n"," label = f\"Class {class_id}\"\n","\n"," text_y = max(0, y_min_px - 5)\n"," ax.text(\n"," x_min_px,\n"," text_y,\n"," label,\n"," color=\"r\",\n"," fontsize=10,\n"," bbox=dict(facecolor=\"white\", alpha=0.7)\n"," )\n","\n"," except ValueError:\n"," print(f\"Warning: Invalid data in label file {label_path.name}, line: {line.strip()}\")\n"," else:\n"," print(f\"Warning: Label file not found for {image_path.name}\")\n","\n"," ax.set_title(image_path.name)\n"," ax.axis(\"off\")\n"," plt.savefig(save_path)\n"," plt.close(fig) # Close the figure\n","\n","# 4. Other Dataset Metrics\n","def get_dataset_stats(image_dir, label_dir):\n"," num_images = len(list(image_dir.glob('*.jpg')) + list(image_dir.glob('*.png')))\n"," num_labels = len(list(label_dir.glob('*.txt')))\n"," return num_images, num_labels\n","\n","print(\"Helper functions defined.\")"]},{"cell_type":"markdown","metadata":{"id":"4d6e4ad6"},"source":["### Main Dataset Processing Loop\n","This loop iterates through each `dataset_config` defined in the `DATASET_CONFIGS` global variable. For each configuration, it performs the following actions:\n","\n","1. **Download Dataset**: Connects to Roboflow using the provided API key and downloads the specified dataset version in YOLOv11 format.\n","2. **Correct `data.yaml`**: Reads the downloaded `data.yaml` file, extracts class information, and modifies the image and label paths to be absolute, ensuring compatibility with Ultralytics.\n","3. **Perform Dataset Analysis (if enabled)**: If `GENERATE_PLOTS` is `True`:\n"," * Calculates and prints the class distribution for both training and validation sets.\n"," * Generates and saves bar charts visualizing the class distribution.\n"," * Selects and displays sample images with their bounding box annotations, prioritizing images that represent distinct classes, saving them to a dedicated directory.\n","4. **Display Dataset Statistics**: Reports the number of images and label files for the training, validation, and test splits.\n","5. **Store Dataset Information**: Gathers all relevant paths and metadata for the processed dataset and appends it to the `prepared_datasets_info` list, which will be used in subsequent training and comparison steps."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"b790bb44","executionInfo":{"status":"ok","timestamp":1775418768667,"user_tz":-180,"elapsed":24295,"user":{"displayName":"Nathan Holland","userId":"13994014446134761485"}},"colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"093e033b-dbe8-41ff-fb4b-08a0a69d456f"},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","================================================================================\n","Processing Dataset: dataset\n","================================================================================\n","loading Roboflow workspace...\n","loading Roboflow project...\n","Exporting format yolov11 in progress : 92.0%\n","Version export complete for yolov11 format\n"]},{"output_type":"stream","name":"stderr","text":["Downloading Dataset Version Zip in Dataset-8 to yolov11:: 100%|██████████| 162055/162055 [00:12<00:00, 13408.35it/s]"]},{"output_type":"stream","name":"stdout","text":["\n"]},{"output_type":"stream","name":"stderr","text":["\n","Extracting Dataset Version Zip to Dataset-8 in yolov11:: 100%|██████████| 3916/3916 [00:00<00:00, 6711.31it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Dataset 'dataset' downloaded to: /content/Dataset-8\n","Dataset analysis outputs will be saved to: outputs/dataset/dataset_analysis\n","Number of classes (nc): 2\n","Class Names: ['basketball', 'basketball_hoop']\n","Modified data.yaml saved to: /content/Dataset-8/data_corrected.yaml\n","\n","--- Class Distribution Analysis ---\n","Training Set Class Distribution (Total Annotations: 5325):\n"," basketball: 2640\n"," basketball_hoop: 2685\n","\n","Validation Set Class Distribution (Total Annotations: 451):\n"," basketball_hoop: 274\n"," basketball: 177\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1500x600 with 2 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Sample Images with Annotations ---\n","Displaying and saving 5 sample images, prioritizing distinct classes.\n"," Saved sample image with annotations to outputs/dataset/dataset_analysis/sample_image_image101_group2_jpg.rf.24c77441c13ab629c3a2636c921d3dc2.png\n"," Saved sample image with annotations to outputs/dataset/dataset_analysis/sample_image_image98_group2_jpg.rf.d328ec2466e403463a7eb68f687d72ee.png\n"," Saved sample image with annotations to outputs/dataset/dataset_analysis/sample_image_image595_group14_jpg.rf.8d0f91eddac3fafabfafd2ccbb778ca1.png\n"," Saved sample image with annotations to outputs/dataset/dataset_analysis/sample_image_image733_group18_png.rf.934d31dba0b372fdb9d547834d5c7545.png\n"," Saved sample image with annotations to outputs/dataset/dataset_analysis/sample_image_image347_group8_jpg.rf.6eaf7b518728241e169df44cecd3dbe5.png\n","\n","Training Set: 1722 images, 1722 label files\n","Validation Set: 178 images, 178 label files\n","Test Set: 56 images, 56 label files\n","\n","================================================================================\n","Finished processing 1 datasets.\n","Prepared Datasets Info: [{'alias': 'dataset', 'dataset_root_dir': PosixPath('/content/Dataset-8'), 'data_yaml_path': PosixPath('/content/Dataset-8/data_corrected.yaml'), 'class_names': ['basketball', 'basketball_hoop'], 'nc': 2, 'output_dir': PosixPath('outputs/dataset'), 'train_images_path': PosixPath('/content/Dataset-8/train/images'), 'train_labels_path': PosixPath('/content/Dataset-8/train/labels'), 'val_images_path': PosixPath('/content/Dataset-8/valid/images'), 'val_labels_path': PosixPath('/content/Dataset-8/valid/labels'), 'test_images_path': PosixPath('/content/Dataset-8/test/images'), 'test_labels_path': PosixPath('/content/Dataset-8/test/labels')}]\n","================================================================================\n"]}],"source":["import yaml\n","from pathlib import Path\n","import matplotlib.pyplot as plt\n","import matplotlib.patches as patches\n","from PIL import Image\n","import random\n","import os\n","from collections import defaultdict\n","from roboflow import Roboflow\n","\n","# Initialize an empty list to store information about each processed dataset\n","# This initialization is also done in a separate cell, but kept here for self-containment if cell order changes.\n","# prepared_datasets_info = [] # Re-initializing here would clear previous data if cells were run out of order\n","\n","\n","# Iterate through each dataset_config in the DATASET_CONFIGS list\n","for dataset_config in DATASET_CONFIGS:\n"," rf_api_key = dataset_config['ROBOFLOW_API_KEY']\n"," workspace = dataset_config['WORKSPACE']\n"," project_name = dataset_config['PROJECT']\n"," version_num = dataset_config['VERSION']\n"," alias = dataset_config['ALIAS']\n","\n"," print(f\"\\n{'='*80}\")\n"," print(f\"Processing Dataset: {alias}\")\n"," print(f\"{'='*80}\")\n","\n"," # 1. Download dataset from Roboflow\n"," try:\n"," # Configure Roboflow API\n"," # Note: Using direct API key from config for demonstration. In production, use os.getenv()\n"," if rf_api_key == 'YOUR_API_KEY_HERE':\n"," print(\"WARNING: Roboflow API Key not set. Skipping dataset download for this config.\")\n"," continue # Skip this dataset if API key is not provided\n","\n"," rf = Roboflow(api_key=rf_api_key)\n"," project = rf.workspace(workspace).project(project_name)\n"," version = project.version(version_num)\n"," dataset = version.download(\"yolov11\")\n","\n"," dataset_location = Path(dataset.location)\n"," print(f\"Dataset '{alias}' downloaded to: {dataset_location}\")\n","\n"," # Create a dataset-specific output directory for analysis artifacts\n"," output_base_dir = Path(f'outputs/{alias}')\n"," dataset_analysis_output_dir = output_base_dir / 'dataset_analysis'\n"," dataset_analysis_output_dir.mkdir(parents=True, exist_ok=True)\n"," print(f\"Dataset analysis outputs will be saved to: {dataset_analysis_output_dir}\")\n","\n"," # 2. Correct and save the 'data.yaml' file\n"," original_data_yaml_path = dataset_location / 'data.yaml'\n","\n"," if not original_data_yaml_path.exists():\n"," print(f\"Error: data.yaml not found at {original_data_yaml_path}. Skipping dataset.\")\n"," continue\n","\n"," with open(original_data_yaml_path, 'r') as f:\n"," data_config_content = yaml.safe_load(f)\n","\n"," class_names = data_config_content['names']\n"," nc = data_config_content['nc']\n"," print(f\"Number of classes (nc): {nc}\")\n"," print(f\"Class Names: {class_names}\")\n","\n"," # Correctly construct absolute paths for images and labels\n"," train_images_abs_path = dataset_location / 'train' / 'images'\n"," train_labels_abs_path = dataset_location / 'train' / 'labels'\n","\n"," val_images_abs_path = dataset_location / 'valid' / 'images'\n"," val_labels_abs_path = dataset_location / 'valid' / 'labels'\n","\n"," test_images_abs_path = dataset_location / 'test' / 'images'\n"," test_labels_abs_path = dataset_location / 'test' / 'labels'\n","\n"," # Update data_config with paths RELATIVE to the dataset_root_dir\n"," data_config_content['train'] = str(train_images_abs_path.relative_to(dataset_location))\n"," data_config_content['val'] = str(val_images_abs_path.relative_to(dataset_location))\n"," data_config_content['test'] = str(test_images_abs_path.relative_to(dataset_location))\n","\n"," modified_data_yaml_path = dataset_location / 'data_corrected.yaml'\n"," with open(modified_data_yaml_path, 'w') as f:\n"," yaml.dump(data_config_content, f, default_flow_style=False)\n","\n"," print(f\"Modified data.yaml saved to: {modified_data_yaml_path}\")\n","\n"," # Store absolute paths for analysis functions\n"," current_dataset_paths = {\n"," 'train_images': train_images_abs_path,\n"," 'train_labels': train_labels_abs_path,\n"," 'val_images': val_images_abs_path,\n"," 'val_labels': val_labels_abs_path,\n"," 'test_images': test_images_abs_path,\n"," 'test_labels': test_labels_abs_path,\n"," }\n","\n"," # 3. Perform and display dataset analysis (class distribution and sample images)\n"," if GENERATE_PLOTS:\n"," print(\"\\n--- Class Distribution Analysis ---\")\n"," # Get counts for training set\n"," train_class_counts, total_train_annotations = get_class_counts(current_dataset_paths['train_labels'], class_names)\n"," print(f\"Training Set Class Distribution (Total Annotations: {total_train_annotations}):\")\n"," for cls, count in train_class_counts.items():\n"," print(f\" {cls}: {count}\")\n","\n"," # Get counts for validation set\n"," val_class_counts, total_val_annotations = get_class_counts(current_dataset_paths['val_labels'], class_names)\n"," print(f\"\\nValidation Set Class Distribution (Total Annotations: {total_val_annotations}):\")\n"," for cls, count in val_class_counts.items():\n"," print(f\" {cls}: {count}\")\n","\n"," # Plotting the class distribution\n"," fig, axes = plt.subplots(1, 2, figsize=(15, 6))\n","\n"," # Training set plot\n"," if train_class_counts:\n"," axes[0].bar(train_class_counts.keys(), train_class_counts.values(), color='skyblue')\n"," axes[0].set_title('Training Set Class Distribution')\n"," axes[0].set_xlabel('Class')\n"," axes[0].set_ylabel('Number of Instances')\n"," axes[0].tick_params(axis='x', rotation=45)\n","\n"," # Validation set plot\n"," if val_class_counts:\n"," axes[1].bar(val_class_counts.keys(), val_class_counts.values(), color='lightcoral')\n"," axes[1].set_title('Validation Set Class Distribution')\n"," axes[1].set_xlabel('Class')\n"," axes[1].set_ylabel('Number of Instances')\n"," axes[1].tick_params(axis='x', rotation=45)\n","\n"," plt.tight_layout()\n"," plt.savefig(dataset_analysis_output_dir / 'class_distribution.png')\n"," plt.show()\n"," plt.close(fig) # Close the figure to free up memory\n","\n"," print(\"\\n--- Sample Images with Annotations ---\")\n"," # Get a list of all training image files\n"," train_image_files = list(current_dataset_paths['train_images'].glob('*.jpg')) + \\\n"," list(current_dataset_paths['train_images'].glob('*.png'))\n","\n"," if len(train_image_files) == 0:\n"," print(f\"No training images found in {current_dataset_paths['train_images']}\")\n"," else:\n"," num_samples_to_show = min(5, len(train_image_files))\n"," selected_images = []\n"," class_image_map = {name: None for name in class_names}\n","\n"," # Try to find an image for each class\n"," for img_file in train_image_files:\n"," label_file = current_dataset_paths['train_labels'] / (img_file.stem + '.txt')\n"," if label_file.exists():\n"," with open(label_file, 'r') as f:\n"," for line in f:\n"," parts = line.strip().split()\n"," if parts:\n"," try:\n"," class_id = int(parts[0])\n"," if 0 <= class_id < len(class_names):\n"," class_name = class_names[class_id]\n"," if class_image_map[class_name] is None:\n"," class_image_map[class_name] = img_file\n"," break # Move to next image once a class is covered\n"," except ValueError:\n"," pass # Ignore invalid lines for selection\n","\n"," # Add images that contain unique classes first\n"," for class_name in class_names:\n"," if class_image_map[class_name] is not None and class_image_map[class_name] not in selected_images:\n"," selected_images.append(class_image_map[class_name])\n","\n"," # Fill the rest with random images if needed\n"," remaining_slots = num_samples_to_show - len(selected_images)\n"," if remaining_slots > 0:\n"," remaining_images = [img for img in train_image_files if img not in selected_images]\n"," if len(remaining_images) > remaining_slots:\n"," selected_images.extend(random.sample(remaining_images, remaining_slots))\n"," else:\n"," selected_images.extend(remaining_images)\n","\n"," if not selected_images:\n"," print(\"Could not find any suitable images to display with annotations.\")\n"," else:\n"," print(f\"Displaying and saving {len(selected_images)} sample images, prioritizing distinct classes.\")\n"," for idx, img_file in enumerate(selected_images):\n"," label_file = current_dataset_paths['train_labels'] / (img_file.stem + '.txt')\n"," save_path = dataset_analysis_output_dir / f\"sample_image_{img_file.stem}.png\"\n"," plot_image_with_bboxes(img_file, label_file, class_names, save_path)\n"," print(f\" Saved sample image with annotations to {save_path}\")\n","\n"," # 4. Other Dataset Metrics\n"," train_num_images, train_num_labels = get_dataset_stats(current_dataset_paths['train_images'], current_dataset_paths['train_labels'])\n"," print(f\"\\nTraining Set: {train_num_images} images, {train_num_labels} label files\")\n","\n"," val_num_images, val_num_labels = get_dataset_stats(current_dataset_paths['val_images'], current_dataset_paths['val_labels'])\n"," print(f\"Validation Set: {val_num_images} images, {val_num_labels} label files\")\n","\n"," test_num_images, test_num_labels = get_dataset_stats(current_dataset_paths['test_images'], current_dataset_paths['test_labels'])\n"," print(f\"Test Set: {test_num_images} images, {test_num_labels} label files\")\n","\n"," # Store the corrected data.yaml path and other relevant dataset info\n"," prepared_datasets_info.append({\n"," 'alias': alias,\n"," 'dataset_root_dir': dataset_location,\n"," 'data_yaml_path': modified_data_yaml_path,\n"," 'class_names': class_names,\n"," 'nc': nc,\n"," 'output_dir': output_base_dir,\n"," 'train_images_path': current_dataset_paths['train_images'],\n"," 'train_labels_path': current_dataset_paths['train_labels'],\n"," 'val_images_path': current_dataset_paths['val_images'],\n"," 'val_labels_path': current_dataset_paths['val_labels'],\n"," 'test_images_path': current_dataset_paths['test_images'],\n"," 'test_labels_path': current_dataset_paths['test_labels']\n"," })\n","\n"," except Exception as e:\n"," print(f\"Error processing dataset '{alias}': {e}\")\n","\n","print(f\"\\n{'='*80}\")\n","print(f\"Finished processing {len(prepared_datasets_info)} datasets.\")\n","print(f\"Prepared Datasets Info: {prepared_datasets_info}\")\n","print(f\"{'='*80}\")"]},{"cell_type":"markdown","metadata":{"id":"06f079e5"},"source":["## Model Training, Validation, and Export Loop\n","\n","This section orchestrates a comprehensive and iterative workflow for training, validating, and exporting object detection models across various datasets and model architectures. For each dataset defined in `DATASET_CONFIGS` and each model architecture specified in `MODEL_ARCHITECTURES`, the notebook performs the following steps:\n","1. **Model Loading**: A pre-trained YOLO model (e.g., `yolo11n.pt`) is loaded to serve as a starting point for training.\n","2. **Model Training**: The model is trained on the current dataset using the `TRAINING_HYPERPARAMETERS`. This step includes configuring the model with the dataset's specific `data.yaml` path and setting up a unique output directory for the run.\n","3. **Metric Collection**: During and after training, key performance metrics such as `mAP50`, `mAP50-95`, `Precision`, `Recall`, `Training Time`, and `Inference Time` are collected to evaluate the model's performance.\n","4. **Model Validation**: The trained model undergoes a validation phase to assess its generalization capabilities on unseen data, confirming the collected metrics.\n","5. **Model Export to Mobile Formats**: To facilitate real-time mobile deployment, the best-performing model from each training run is exported to: \\n * **CoreML (.mlpackage)**: Optimized for iOS devices, often with half-precision (FP16) and built-in NMS.\\n * **TFLite (.tflite)**: Optimized for Android devices, typically with INT8 quantization and built-in NMS.\n","6. **Visual Test Results**: If `SAVE_TEST_PREDICTION_IMAGES` is enabled, the model generates predictions on a selection of test images. These images, annotated with bounding boxes and confidence scores, are saved to provide a qualitative assessment of the model's performance.\n","\n","All training logs, model weights, exported formats, and visual test results are meticulously organized and saved into uniquely named directories, ensuring clear traceability and easy comparison between different model-dataset combinations. This systematic approach allows for robust evaluation and selection of the most suitable model for specific application requirements.\"\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fb2ca4eb","colab":{"base_uri":"https://localhost:8080/"},"outputId":"50d43874-977b-41ac-9c88-092eddad5201","executionInfo":{"status":"ok","timestamp":1775419683604,"user_tz":-180,"elapsed":914917,"user":{"displayName":"Nathan Holland","userId":"13994014446134761485"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","================================================================================\n","STARTING MODEL TRAINING, VALIDATION, AND EXPORT LOOP\n","================================================================================\n","\n","\n","**********************************************************************\n","Processing Dataset: dataset (/content/Dataset-8/data_corrected.yaml)\n","**********************************************************************\n","\n","\n","------------------------------------------------------------\n","Training Model: yolo11n on Dataset: dataset\n","------------------------------------------------------------\n","\n","Loading model: yolo11n.pt\n","\u001b[KDownloading https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo11n.pt to 'yolo11n.pt': 100% ━━━━━━━━━━━━ 5.4MB 364.3MB/s 0.0s\n","Model yolo11n.pt loaded successfully.\n","Starting model training...\n","Ultralytics 8.4.33 🚀 Python-3.12.13 torch-2.10.0+cu128 CUDA:0 (NVIDIA L4, 22563MiB)\n","\u001b[34m\u001b[1mengine/trainer: \u001b[0magnostic_nms=False, amp=True, angle=1.0, augment=False, auto_augment=randaugment, batch=8, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, compile=False, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=/content/Dataset-8/data_corrected.yaml, degrees=0.0, deterministic=True, device=0, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, end2end=None, epochs=100, erasing=0.4, exist_ok=False, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=None, half=False, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, imgsz=900, int8=False, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.001, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.0, mode=train, model=yolo11n.pt, momentum=0.937, mosaic=1.0, multi_scale=0.0, name=yolo11n_dataset, nbs=64, nms=False, opset=None, optimize=False, optimizer=AdamW, overlap_mask=True, patience=20, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=runs/all_trainings, rect=False, resume=False, retina_masks=False, rle=1.0, save=True, save_conf=False, save_crop=False, save_dir=/content/runs/detect/runs/all_trainings/yolo11n_dataset, save_frames=False, save_json=False, save_period=-1, save_txt=False, scale=0.5, seed=0, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=detect, time=None, tracker=botsort.yaml, translate=0.1, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=3.0, warmup_momentum=0.8, weight_decay=0.0005, workers=8, workspace=None\n","\u001b[KDownloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf': 100% ━━━━━━━━━━━━ 755.1KB 138.6MB/s 0.0s\n","Overriding model.yaml nc=80 with nc=2\n","\n"," from n params module arguments \n"," 0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2] \n"," 1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2] \n"," 2 -1 1 6640 ultralytics.nn.modules.block.C3k2 [32, 64, 1, False, 0.25] \n"," 3 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] \n"," 4 -1 1 26080 ultralytics.nn.modules.block.C3k2 [64, 128, 1, False, 0.25] \n"," 5 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] \n"," 6 -1 1 87040 ultralytics.nn.modules.block.C3k2 [128, 128, 1, True] \n"," 7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] \n"," 8 -1 1 346112 ultralytics.nn.modules.block.C3k2 [256, 256, 1, True] \n"," 9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5] \n"," 10 -1 1 249728 ultralytics.nn.modules.block.C2PSA [256, 256, 1] \n"," 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n"," 12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] \n"," 13 -1 1 111296 ultralytics.nn.modules.block.C3k2 [384, 128, 1, False] \n"," 14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n"," 15 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] \n"," 16 -1 1 32096 ultralytics.nn.modules.block.C3k2 [256, 64, 1, False] \n"," 17 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] \n"," 18 [-1, 13] 1 0 ultralytics.nn.modules.conv.Concat [1] \n"," 19 -1 1 86720 ultralytics.nn.modules.block.C3k2 [192, 128, 1, False] \n"," 20 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] \n"," 21 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1] \n"," 22 -1 1 378880 ultralytics.nn.modules.block.C3k2 [384, 256, 1, True] \n"," 23 [16, 19, 22] 1 431062 ultralytics.nn.modules.head.Detect [2, 16, None, [64, 128, 256]] \n","YOLO11n summary: 182 layers, 2,590,230 parameters, 2,590,214 gradients, 6.4 GFLOPs\n","\n","Transferred 448/499 items from pretrained weights\n","Freezing layer 'model.23.dfl.conv.weight'\n","\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n","\u001b[KDownloading https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n.pt to 'yolo26n.pt': 100% ━━━━━━━━━━━━ 5.3MB 300.3MB/s 0.0s\n","\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n","WARNING ⚠️ imgsz=[900] must be multiple of max stride 32, updating to [928]\n","\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 1882.0±1096.8 MB/s, size: 88.4 KB)\n","\u001b[K\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/Dataset-8/train/labels... 1722 images, 0 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 1722/1722 1.5Kit/s 1.1s\n","\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/Dataset-8/train/labels.cache\n","\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n","\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 1296.0±1050.3 MB/s, size: 110.9 KB)\n","\u001b[K\u001b[34m\u001b[1mval: \u001b[0mScanning /content/Dataset-8/valid/labels... 178 images, 0 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 178/178 1.4Kit/s 0.1s\n","\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/Dataset-8/valid/labels.cache\n","\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.001, momentum=0.937) with parameter groups 81 weight(decay=0.0), 88 weight(decay=0.0005), 87 bias(decay=0.0)\n","Plotting labels to /content/runs/detect/runs/all_trainings/yolo11n_dataset/labels.jpg... \n","Image sizes 928 train, 928 val\n","Using 8 dataloader workers\n","Logging results to \u001b[1m/content/runs/detect/runs/all_trainings/yolo11n_dataset\u001b[0m\n","Starting training for 100 epochs...\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 1/100 2.46G 1.674 1.635 1.166 5 928: 100% ━━━━━━━━━━━━ 216/216 4.6it/s 47.4s\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 1.6it/s 7.7s\n"," all 178 451 0.678 0.517 0.626 0.323\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 2/100 2.8G 1.43 0.9942 1.076 1 928: 100% ━━━━━━━━━━━━ 216/216 8.9it/s 24.2s\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 11.0it/s 1.1s\n"," all 178 451 0.827 0.664 0.775 0.389\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 3/100 2.8G 1.383 0.8952 1.054 10 928: 100% ━━━━━━━━━━━━ 216/216 9.2it/s 23.5s\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 10.6it/s 1.1s\n"," all 178 451 0.676 0.739 0.726 0.325\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 4/100 2.8G 1.301 0.8213 1.027 7 928: 100% ━━━━━━━━━━━━ 216/216 9.1it/s 23.9s\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 10.2it/s 1.2s\n"," all 178 451 0.824 0.634 0.78 0.43\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 5/100 2.8G 1.277 0.7758 1.018 7 928: 100% ━━━━━━━━━━━━ 216/216 9.1it/s 23.8s\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 10.8it/s 1.1s\n"," all 178 451 0.622 0.637 0.714 0.397\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 6/100 2.8G 1.271 0.7566 1.018 3 928: 100% ━━━━━━━━━━━━ 216/216 9.0it/s 23.9s\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 10.5it/s 1.1s\n"," all 178 451 0.564 0.768 0.751 0.397\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 7/100 2.8G 1.236 0.7444 0.9973 5 928: 100% ━━━━━━━━━━━━ 216/216 9.1it/s 23.8s\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 10.6it/s 1.1s\n"," all 178 451 0.697 0.773 0.75 0.366\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 8/100 2.8G 1.222 0.716 0.9959 2 928: 100% ━━━━━━━━━━━━ 216/216 9.1it/s 23.6s\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 10.6it/s 1.1s\n"," all 178 451 0.87 0.778 0.867 0.47\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 9/100 2.8G 1.171 0.6852 0.976 7 928: 100% ━━━━━━━━━━━━ 216/216 9.1it/s 23.7s\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 10.4it/s 1.2s\n"," all 178 451 0.859 0.735 0.842 0.46\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 10/100 2.8G 1.208 0.6852 0.9857 10 928: 100% ━━━━━━━━━━━━ 216/216 9.1it/s 23.7s\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 10.6it/s 1.1s\n"," all 178 451 0.724 0.745 0.712 0.385\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 11/100 2.8G 1.184 0.6712 0.9765 11 928: 100% ━━━━━━━━━━━━ 216/216 9.0it/s 24.0s\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 10.9it/s 1.1s\n"," all 178 451 0.874 0.854 0.895 0.51\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 12/100 2.8G 1.166 0.6526 0.9742 9 928: 100% ━━━━━━━━━━━━ 216/216 9.2it/s 23.6s\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 10.8it/s 1.1s\n"," all 178 451 0.798 0.845 0.867 0.438\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 13/100 2.8G 1.165 0.6413 0.9756 5 928: 100% ━━━━━━━━━━━━ 216/216 9.0it/s 23.9s\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 11.0it/s 1.1s\n"," all 178 451 0.746 0.707 0.81 0.422\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 14/100 2.8G 1.146 0.6461 0.9606 9 928: 100% ━━━━━━━━━━━━ 216/216 9.0it/s 23.9s\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 10.9it/s 1.1s\n"," all 178 451 0.769 0.697 0.752 0.386\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 15/100 2.8G 1.142 0.6307 0.9625 13 928: 100% ━━━━━━━━━━━━ 216/216 9.0it/s 24.1s\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 10.6it/s 1.1s\n"," all 178 451 0.696 0.795 0.743 0.412\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 16/100 2.8G 1.113 0.6148 0.9508 7 928: 100% ━━━━━━━━━━━━ 216/216 9.0it/s 23.9s\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 10.8it/s 1.1s\n"," all 178 451 0.805 0.857 0.86 0.484\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 17/100 2.8G 1.106 0.6083 0.9608 2 928: 100% ━━━━━━━━━━━━ 216/216 9.0it/s 24.1s\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 10.5it/s 1.1s\n"," all 178 451 0.664 0.781 0.758 0.407\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 18/100 2.8G 1.094 0.591 0.9457 14 928: 100% ━━━━━━━━━━━━ 216/216 9.1it/s 23.8s\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 10.9it/s 1.1s\n"," all 178 451 0.74 0.75 0.756 0.423\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 19/100 2.8G 1.12 0.6096 0.9561 2 928: 100% ━━━━━━━━━━━━ 216/216 9.1it/s 23.6s\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 10.5it/s 1.1s\n"," all 178 451 0.7 0.885 0.796 0.413\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 20/100 2.8G 1.102 0.6058 0.9554 5 928: 100% ━━━━━━━━━━━━ 216/216 9.1it/s 23.8s\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 10.6it/s 1.1s\n"," all 178 451 0.84 0.733 0.831 0.46\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 21/100 2.8G 1.073 0.5847 0.948 5 928: 100% ━━━━━━━━━━━━ 216/216 9.0it/s 23.9s\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 10.3it/s 1.2s\n"," all 178 451 0.814 0.84 0.819 0.417\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 22/100 2.8G 1.065 0.5784 0.9386 6 928: 100% ━━━━━━━━━━━━ 216/216 8.9it/s 24.2s\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 10.3it/s 1.2s\n"," all 178 451 0.872 0.776 0.871 0.473\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 23/100 2.8G 1.073 0.5806 0.9429 11 928: 100% ━━━━━━━━━━━━ 216/216 9.0it/s 24.0s\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 11.0it/s 1.1s\n"," all 178 451 0.886 0.757 0.856 0.469\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 24/100 2.8G 1.046 0.5649 0.928 12 928: 100% ━━━━━━━━━━━━ 216/216 9.1it/s 23.6s\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 10.2it/s 1.2s\n"," all 178 451 0.855 0.734 0.819 0.448\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 25/100 2.8G 1.056 0.5633 0.9427 3 928: 100% ━━━━━━━━━━━━ 216/216 9.0it/s 24.0s\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 10.6it/s 1.1s\n"," all 178 451 0.804 0.773 0.788 0.446\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 26/100 2.8G 1.063 0.5632 0.9317 15 928: 100% ━━━━━━━━━━━━ 216/216 9.3it/s 23.3s\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 10.5it/s 1.1s\n"," all 178 451 0.711 0.754 0.803 0.435\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 27/100 2.8G 1.042 0.5563 0.9263 4 928: 100% ━━━━━━━━━━━━ 216/216 9.1it/s 23.8s\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 10.2it/s 1.2s\n"," all 178 451 0.884 0.804 0.891 0.495\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 28/100 2.8G 1.04 0.5507 0.9295 12 928: 100% ━━━━━━━━━━━━ 216/216 9.0it/s 24.0s\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 10.9it/s 1.1s\n"," all 178 451 0.781 0.81 0.787 0.45\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 29/100 2.8G 1.038 0.5462 0.9354 3 928: 100% ━━━━━━━━━━━━ 216/216 9.1it/s 23.8s\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 10.4it/s 1.2s\n"," all 178 451 0.771 0.836 0.83 0.475\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 30/100 2.8G 1.027 0.5456 0.9291 4 928: 100% ━━━━━━━━━━━━ 216/216 8.9it/s 24.3s\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 10.2it/s 1.2s\n"," all 178 451 0.811 0.85 0.853 0.49\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 31/100 2.8G 1.029 0.5375 0.9255 10 928: 100% ━━━━━━━━━━━━ 216/216 9.0it/s 24.1s\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 10.7it/s 1.1s\n"," all 178 451 0.911 0.749 0.858 0.477\n","\u001b[34m\u001b[1mEarlyStopping: \u001b[0mTraining stopped early as no improvement observed in last 20 epochs. Best results observed at epoch 11, best model saved as best.pt.\n","To update EarlyStopping(patience=20) pass a new patience value, i.e. `patience=300` or use `patience=0` to disable EarlyStopping.\n","\n","31 epochs completed in 0.226 hours.\n","Optimizer stripped from /content/runs/detect/runs/all_trainings/yolo11n_dataset/weights/last.pt, 5.5MB\n","Optimizer stripped from /content/runs/detect/runs/all_trainings/yolo11n_dataset/weights/best.pt, 5.5MB\n","\n","Validating /content/runs/detect/runs/all_trainings/yolo11n_dataset/weights/best.pt...\n","Ultralytics 8.4.33 🚀 Python-3.12.13 torch-2.10.0+cu128 CUDA:0 (NVIDIA L4, 22563MiB)\n","YOLO11n summary (fused): 101 layers, 2,582,542 parameters, 0 gradients, 6.3 GFLOPs\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 5.2it/s 2.3s\n"," all 178 451 0.874 0.853 0.895 0.511\n"," basketball 177 177 0.822 0.712 0.8 0.434\n"," basketball_hoop 178 274 0.925 0.995 0.99 0.588\n","Speed: 0.4ms preprocess, 2.3ms inference, 0.0ms loss, 7.1ms postprocess per image\n","Results saved to \u001b[1m/content/runs/detect/runs/all_trainings/yolo11n_dataset\u001b[0m\n","Training completed for yolo11n on dataset in 13.95 minutes.\n","Validating model...\n","WARNING ⚠️ imgsz=[900] must be multiple of max stride 32, updating to [928]\n","Ultralytics 8.4.33 🚀 Python-3.12.13 torch-2.10.0+cu128 CUDA:0 (NVIDIA L4, 22563MiB)\n","YOLO11n summary (fused): 101 layers, 2,582,542 parameters, 0 gradients, 6.3 GFLOPs\n","\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 2309.1±1051.0 MB/s, size: 90.0 KB)\n","\u001b[K\u001b[34m\u001b[1mval: \u001b[0mScanning /content/Dataset-8/valid/labels.cache... 178 images, 0 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 178/178 74.7Mit/s 0.0s\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 12/12 3.3it/s 3.6s\n"," all 178 451 0.873 0.849 0.897 0.508\n"," basketball 177 177 0.822 0.705 0.804 0.428\n"," basketball_hoop 178 274 0.923 0.993 0.99 0.589\n","Speed: 2.8ms preprocess, 5.9ms inference, 0.0ms loss, 3.0ms postprocess per image\n","Results saved to \u001b[1m/content/runs/detect/val\u001b[0m\n","Validation complete. mAP50: 0.8970\n","Exporting model to CoreML...\n","Ultralytics 8.4.33 🚀 Python-3.12.13 torch-2.10.0+cu128 CPU (Intel Xeon CPU @ 2.20GHz)\n","WARNING ⚠️ imgsz=[900] must be multiple of max stride 32, updating to [928]\n","💡 ProTip: Export to OpenVINO format for best performance on Intel hardware. Learn more at https://docs.ultralytics.com/integrations/openvino/\n","\n","\u001b[34m\u001b[1mPyTorch:\u001b[0m starting from '/content/runs/detect/runs/all_trainings/yolo11n_dataset/weights/best.pt' with input shape (1, 3, 928, 928) BCHW and output shape(s) (1, 6, 17661) (5.3 MB)\n","\u001b[31m\u001b[1mrequirements:\u001b[0m Ultralytics requirement ['coremltools>=9.0'] not found, attempting AutoUpdate...\n","Using Python 3.12.13 environment at: /usr\n","Resolved 12 packages in 212ms\n","Prepared 3 packages in 97ms\n","Installed 3 packages in 18ms\n"," + cattrs==26.1.0\n"," + coremltools==9.0\n"," + pyaml==26.2.1\n","\n","\u001b[31m\u001b[1mrequirements:\u001b[0m AutoUpdate success ✅ 0.7s\n","WARNING ⚠️ \u001b[31m\u001b[1mrequirements:\u001b[0m \u001b[1mRestart runtime or rerun command for updates to take effect\u001b[0m\n","\n"]},{"output_type":"stream","name":"stderr","text":["invalid escape sequence '\\_'\n"]},{"output_type":"stream","name":"stdout","text":["\n","\u001b[34m\u001b[1mCoreML:\u001b[0m starting export with coremltools 9.0...\n"]},{"output_type":"stream","name":"stderr","text":["Converting PyTorch Frontend ==> MIL Ops: 100%|█████████▉| 713/715 [00:00<00:00, 2980.19 ops/s]\n","Running MIL frontend_pytorch pipeline: 100%|██████████| 5/5 [00:00<00:00, 85.92 passes/s]\n","Running MIL default pipeline: 11%|█ | 10/95 [00:00<00:00, 93.78 passes/s]Output, '1148', of the source model, has been renamed to 'var_1148' in the Core ML model.\n","Output, '1150', of the source model, has been renamed to 'var_1150' in the Core ML model.\n","Running MIL default pipeline: 100%|██████████| 95/95 [00:01<00:00, 48.55 passes/s]\n","Running MIL backend_mlprogram pipeline: 100%|██████████| 12/12 [00:00<00:00, 86.07 passes/s]\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[34m\u001b[1mCoreML:\u001b[0m starting pipeline with coremltools 9.0...\n","\u001b[34m\u001b[1mCoreML:\u001b[0m pipeline success\n","\u001b[34m\u001b[1mCoreML:\u001b[0m export success ✅ 17.1s, saved as '/content/runs/detect/runs/all_trainings/yolo11n_dataset/weights/best.mlpackage' (5.2 MB)\n","\n","Export complete (17.4s)\n","Results saved to \u001b[1m/content/runs/detect/runs/all_trainings/yolo11n_dataset/weights\u001b[0m\n","Predict: yolo predict task=detect model=/content/runs/detect/runs/all_trainings/yolo11n_dataset/weights/best.mlpackage imgsz=928 \n","Validate: yolo val task=detect model=/content/runs/detect/runs/all_trainings/yolo11n_dataset/weights/best.mlpackage imgsz=928 data=/content/Dataset-8/data_corrected.yaml \n","Visualize: https://netron.app\n","CoreML model exported to: /content/runs/detect/runs/all_trainings/yolo11n_dataset/weights/best.mlpackage\n","Exporting model to TFLite...\n","Ultralytics 8.4.33 🚀 Python-3.12.13 torch-2.10.0+cu128 CPU (Intel Xeon CPU @ 2.20GHz)\n","WARNING ⚠️ imgsz=[900] must be multiple of max stride 32, updating to [928]\n","\n","\u001b[34m\u001b[1mPyTorch:\u001b[0m starting from '/content/runs/detect/runs/all_trainings/yolo11n_dataset/weights/best.pt' with input shape (1, 3, 928, 928) BCHW and output shape(s) (1, 300, 6) (5.3 MB)\n","\u001b[31m\u001b[1mrequirements:\u001b[0m Ultralytics requirements ['sng4onnx>=1.0.1', 'onnx_graphsurgeon>=0.3.26', 'ai-edge-litert>=1.2.0', 'onnx>=1.12.0,<2.0.0', 'onnx2tf>=1.26.3,<1.29.0', 'onnxslim>=0.1.71', 'onnxruntime-gpu'] not found, attempting AutoUpdate...\n","Using Python 3.12.13 environment at: /usr\n","Resolved 18 packages in 7.65s\n","Prepared 9 packages in 3.45s\n","Installed 9 packages in 305ms\n"," + ai-edge-litert==2.1.3\n"," + backports-strenum==1.3.1\n"," + colorama==0.4.6\n"," + onnx==1.21.0\n"," + onnx-graphsurgeon==0.5.8\n"," + onnx2tf==1.28.8\n"," + onnxruntime-gpu==1.24.4\n"," + onnxslim==0.1.90\n"," + sng4onnx==2.0.1\n","\n","\u001b[31m\u001b[1mrequirements:\u001b[0m AutoUpdate success ✅ 11.7s\n","WARNING ⚠️ \u001b[31m\u001b[1mrequirements:\u001b[0m \u001b[1mRestart runtime or rerun command for updates to take effect\u001b[0m\n","\n","\n","\u001b[34m\u001b[1mTensorFlow SavedModel:\u001b[0m starting export with tensorflow 2.19.0...\n","\n","\u001b[34m\u001b[1mONNX:\u001b[0m starting export with onnx 1.21.0 opset 20...\n","\u001b[34m\u001b[1mONNX:\u001b[0m slimming with onnxslim 0.1.90...\n","\u001b[34m\u001b[1mONNX:\u001b[0m export success ✅ 2.3s, saved as '/content/runs/detect/runs/all_trainings/yolo11n_dataset/weights/best.onnx' (10.5 MB)\n","\u001b[KDownloading https://github.com/ultralytics/assets/releases/download/v8.4.0/calibration_image_sample_data_20x128x128x3_float32.npy.zip to 'calibration_image_sample_data_20x128x128x3_float32.npy.zip': 100% ━━━━━━━━━━━━ 1.1MB 173.9MB/s 0.0s\n","\u001b[KUnzipping calibration_image_sample_data_20x128x128x3_float32.npy.zip to /content/calibration_image_sample_data_20x128x128x3_float32.npy...: 100% ━━━━━━━━━━━━ 1/1 49.3files/s 0.0s\n","\u001b[34m\u001b[1mTensorFlow SavedModel:\u001b[0m starting TFLite export with onnx2tf 1.28.8...\n","Saved artifact at '/content/runs/detect/runs/all_trainings/yolo11n_dataset/weights/best_saved_model'. The following endpoints are available:\n","\n","* Endpoint 'serving_default'\n"," inputs_0 (POSITIONAL_ONLY): TensorSpec(shape=(1, 928, 928, 3), dtype=tf.float32, name='images')\n","Output Type:\n"," TensorSpec(shape=(1, 300, 6), dtype=tf.float32, name=None)\n","Captures:\n"," 134705302288336: TensorSpec(shape=(4, 2), dtype=tf.int32, name=None)\n"," 134705302286992: TensorSpec(shape=(3, 3, 3, 16), dtype=tf.float32, name=None)\n"," 134705302286416: TensorSpec(shape=(16,), dtype=tf.float32, name=None)\n"," 134705416030736: TensorSpec(shape=(4, 2), dtype=tf.int32, name=None)\n"," 134705302288144: TensorSpec(shape=(3, 3, 16, 32), dtype=tf.float32, name=None)\n"," 134705416029776: TensorSpec(shape=(32,), dtype=tf.float32, name=None)\n"," 134705416030928: TensorSpec(shape=(1, 1, 32, 32), dtype=tf.float32, name=None)\n"," 134705416031120: TensorSpec(shape=(32,), dtype=tf.float32, name=None)\n"," 134705416032656: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n"," 134705416031888: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n"," 134705302281808: TensorSpec(shape=(3, 3, 16, 8), dtype=tf.float32, name=None)\n"," 134705302288528: TensorSpec(shape=(8,), dtype=tf.float32, name=None)\n"," 134705302281616: TensorSpec(shape=(3, 3, 8, 16), dtype=tf.float32, name=None)\n"," 134705416033808: TensorSpec(shape=(16,), dtype=tf.float32, name=None)\n"," 134705416030160: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n"," 134705416032464: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n"," 134705416035152: TensorSpec(shape=(1, 1, 48, 64), dtype=tf.float32, name=None)\n"," 134705416034192: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705416031504: TensorSpec(shape=(4, 2), dtype=tf.int32, name=None)\n"," 134705416034576: TensorSpec(shape=(3, 3, 64, 64), dtype=tf.float32, name=None)\n"," 134705416033232: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705416035536: TensorSpec(shape=(1, 1, 64, 64), dtype=tf.float32, name=None)\n"," 134705416034768: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705416036304: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n"," 134705416034960: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n"," 134705416036688: TensorSpec(shape=(3, 3, 32, 16), dtype=tf.float32, name=None)\n"," 134705416037648: TensorSpec(shape=(16,), dtype=tf.float32, name=None)\n"," 134705416035728: TensorSpec(shape=(3, 3, 16, 32), dtype=tf.float32, name=None)\n"," 134705416034384: TensorSpec(shape=(32,), dtype=tf.float32, name=None)\n"," 134705416035920: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n"," 134705416036112: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n"," 134705416037840: TensorSpec(shape=(1, 1, 96, 128), dtype=tf.float32, name=None)\n"," 134705416035344: TensorSpec(shape=(128,), dtype=tf.float32, name=None)\n"," 134705416038224: TensorSpec(shape=(4, 2), dtype=tf.int32, name=None)\n"," 134705416036880: TensorSpec(shape=(3, 3, 128, 128), dtype=tf.float32, name=None)\n"," 134705416038032: TensorSpec(shape=(128,), dtype=tf.float32, name=None)\n"," 134705416038416: TensorSpec(shape=(1, 1, 128, 128), dtype=tf.float32, name=None)\n"," 134705416038608: TensorSpec(shape=(128,), dtype=tf.float32, name=None)\n"," 134705416039184: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n"," 134705416038992: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n"," 134705416041104: TensorSpec(shape=(1, 1, 64, 32), dtype=tf.float32, name=None)\n"," 134705416041296: TensorSpec(shape=(32,), dtype=tf.float32, name=None)\n"," 134705416040528: TensorSpec(shape=(3, 3, 32, 32), dtype=tf.float32, name=None)\n"," 134705416041488: TensorSpec(shape=(32,), dtype=tf.float32, name=None)\n"," 134705416041680: TensorSpec(shape=(3, 3, 32, 32), dtype=tf.float32, name=None)\n"," 134705416041872: TensorSpec(shape=(32,), dtype=tf.float32, name=None)\n"," 134705416040720: TensorSpec(shape=(3, 3, 32, 32), dtype=tf.float32, name=None)\n"," 134705416042256: TensorSpec(shape=(32,), dtype=tf.float32, name=None)\n"," 134705416040144: TensorSpec(shape=(3, 3, 32, 32), dtype=tf.float32, name=None)\n"," 134705416037072: TensorSpec(shape=(1, 1, 64, 32), dtype=tf.float32, name=None)\n"," 134705416042448: TensorSpec(shape=(32,), dtype=tf.float32, name=None)\n"," 134705416038800: TensorSpec(shape=(32,), dtype=tf.float32, name=None)\n"," 134705416043024: TensorSpec(shape=(1, 1, 64, 64), dtype=tf.float32, name=None)\n"," 134705416042064: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705416039376: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n"," 134705416039568: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n"," 134705416043216: TensorSpec(shape=(1, 1, 192, 128), dtype=tf.float32, name=None)\n"," 134705416043408: TensorSpec(shape=(128,), dtype=tf.float32, name=None)\n"," 134705416043600: TensorSpec(shape=(4, 2), dtype=tf.int32, name=None)\n"," 134705416042640: TensorSpec(shape=(3, 3, 128, 256), dtype=tf.float32, name=None)\n"," 134705416042832: TensorSpec(shape=(256,), dtype=tf.float32, name=None)\n"," 134705416043984: TensorSpec(shape=(1, 1, 256, 256), dtype=tf.float32, name=None)\n"," 134705416044176: TensorSpec(shape=(256,), dtype=tf.float32, name=None)\n"," 134705413816592: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n"," 134705413816400: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n"," 134705416044368: TensorSpec(shape=(1, 1, 128, 64), dtype=tf.float32, name=None)\n"," 134705416043792: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705413817552: TensorSpec(shape=(3, 3, 64, 64), dtype=tf.float32, name=None)\n"," 134705413818896: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705413819088: TensorSpec(shape=(3, 3, 64, 64), dtype=tf.float32, name=None)\n"," 134705413819280: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705413818128: TensorSpec(shape=(3, 3, 64, 64), dtype=tf.float32, name=None)\n"," 134705413819664: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705413818704: TensorSpec(shape=(3, 3, 64, 64), dtype=tf.float32, name=None)\n"," 134705413817936: TensorSpec(shape=(1, 1, 128, 64), dtype=tf.float32, name=None)\n"," 134705413819856: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705413818512: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705413820432: TensorSpec(shape=(1, 1, 128, 128), dtype=tf.float32, name=None)\n"," 134705413819472: TensorSpec(shape=(128,), dtype=tf.float32, name=None)\n"," 134705413816784: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n"," 134705413816976: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n"," 134705413820624: TensorSpec(shape=(1, 1, 384, 256), dtype=tf.float32, name=None)\n"," 134705413820816: TensorSpec(shape=(256,), dtype=tf.float32, name=None)\n"," 134705413820048: TensorSpec(shape=(1, 1, 256, 128), dtype=tf.float32, name=None)\n"," 134705413821008: TensorSpec(shape=(128,), dtype=tf.float32, name=None)\n"," 134705413821392: TensorSpec(shape=(1, 1, 512, 256), dtype=tf.float32, name=None)\n"," 134705413821584: TensorSpec(shape=(256,), dtype=tf.float32, name=None)\n"," 134705413820240: TensorSpec(shape=(1, 1, 256, 256), dtype=tf.float32, name=None)\n"," 134705413822160: TensorSpec(shape=(256,), dtype=tf.float32, name=None)\n"," 134705413821968: TensorSpec(shape=(1, 1, 128, 256), dtype=tf.float32, name=None)\n"," 134705413822544: TensorSpec(shape=(256,), dtype=tf.float32, name=None)\n"," 134705413821776: TensorSpec(shape=(), dtype=tf.resource, name=None)\n"," 134705413825040: TensorSpec(shape=(128,), dtype=tf.float32, name=None)\n"," 134705413827920: TensorSpec(shape=(1, 1, 128, 128), dtype=tf.float32, name=None)\n"," 134705413825616: TensorSpec(shape=(128,), dtype=tf.float32, name=None)\n"," 134705413827536: TensorSpec(shape=(1, 1, 128, 256), dtype=tf.float32, name=None)\n"," 134705413827344: TensorSpec(shape=(256,), dtype=tf.float32, name=None)\n"," 134705413826768: TensorSpec(shape=(1, 1, 256, 128), dtype=tf.float32, name=None)\n"," 134705413824464: TensorSpec(shape=(128,), dtype=tf.float32, name=None)\n"," 134705413824848: TensorSpec(shape=(1, 1, 256, 256), dtype=tf.float32, name=None)\n"," 134705413826960: TensorSpec(shape=(256,), dtype=tf.float32, name=None)\n"," 134705413826576: TensorSpec(shape=(1, 1, 384, 128), dtype=tf.float32, name=None)\n"," 134705413828496: TensorSpec(shape=(128,), dtype=tf.float32, name=None)\n"," 134705413828112: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n"," 134705413828304: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n"," 134705413829648: TensorSpec(shape=(3, 3, 64, 32), dtype=tf.float32, name=None)\n"," 134705413829840: TensorSpec(shape=(32,), dtype=tf.float32, name=None)\n"," 134705413827152: TensorSpec(shape=(3, 3, 32, 64), dtype=tf.float32, name=None)\n"," 134705413822736: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705413828688: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n"," 134705413826384: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n"," 134705413830416: TensorSpec(shape=(1, 1, 192, 128), dtype=tf.float32, name=None)\n"," 134705413829072: TensorSpec(shape=(128,), dtype=tf.float32, name=None)\n"," 134705413829264: TensorSpec(shape=(1, 1, 256, 64), dtype=tf.float32, name=None)\n"," 134705413829456: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705413831184: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n"," 134705413830992: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n"," 134705413831760: TensorSpec(shape=(3, 3, 32, 16), dtype=tf.float32, name=None)\n"," 134705413830800: TensorSpec(shape=(16,), dtype=tf.float32, name=None)\n"," 134705413832144: TensorSpec(shape=(3, 3, 16, 32), dtype=tf.float32, name=None)\n"," 134705413832528: TensorSpec(shape=(32,), dtype=tf.float32, name=None)\n"," 134705413831376: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n"," 134705413831568: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n"," 134705413831952: TensorSpec(shape=(1, 1, 96, 64), dtype=tf.float32, name=None)\n"," 134705413830608: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705393861456: TensorSpec(shape=(4, 2), dtype=tf.int32, name=None)\n"," 134705393861264: TensorSpec(shape=(3, 3, 64, 64), dtype=tf.float32, name=None)\n"," 134705393861648: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705393863760: TensorSpec(shape=(1, 1, 192, 128), dtype=tf.float32, name=None)\n"," 134705393863184: TensorSpec(shape=(128,), dtype=tf.float32, name=None)\n"," 134705393864720: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n"," 134705393864912: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n"," 134705393868176: TensorSpec(shape=(3, 3, 64, 32), dtype=tf.float32, name=None)\n"," 134705393866064: TensorSpec(shape=(32,), dtype=tf.float32, name=None)\n"," 134705393865104: TensorSpec(shape=(3, 3, 32, 64), dtype=tf.float32, name=None)\n"," 134705393867600: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705393865296: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n"," 134705393865488: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n"," 134705393867984: TensorSpec(shape=(1, 1, 192, 128), dtype=tf.float32, name=None)\n"," 134705393867024: TensorSpec(shape=(128,), dtype=tf.float32, name=None)\n"," 134705393868560: TensorSpec(shape=(4, 2), dtype=tf.int32, name=None)\n"," 134705393867408: TensorSpec(shape=(3, 3, 128, 128), dtype=tf.float32, name=None)\n"," 134705393868752: TensorSpec(shape=(128,), dtype=tf.float32, name=None)\n"," 134705393870480: TensorSpec(shape=(1, 1, 384, 256), dtype=tf.float32, name=None)\n"," 134705393868944: TensorSpec(shape=(256,), dtype=tf.float32, name=None)\n"," 134705393871440: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n"," 134705393871632: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n"," 134705393874512: TensorSpec(shape=(1, 1, 128, 64), dtype=tf.float32, name=None)\n"," 134705393872784: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705393874320: TensorSpec(shape=(3, 3, 64, 64), dtype=tf.float32, name=None)\n"," 134705393873360: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705393875472: TensorSpec(shape=(3, 3, 64, 64), dtype=tf.float32, name=None)\n"," 134705393876624: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705393874896: TensorSpec(shape=(3, 3, 64, 64), dtype=tf.float32, name=None)\n"," 134705393875280: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705393874704: TensorSpec(shape=(3, 3, 64, 64), dtype=tf.float32, name=None)\n"," 134705393873168: TensorSpec(shape=(1, 1, 128, 64), dtype=tf.float32, name=None)\n"," 134705393876240: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705393871824: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705393875856: TensorSpec(shape=(1, 1, 128, 128), dtype=tf.float32, name=None)\n"," 134705393875664: TensorSpec(shape=(128,), dtype=tf.float32, name=None)\n"," 134705393872016: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n"," 134705393872208: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n"," 134705393876816: TensorSpec(shape=(1, 1, 384, 256), dtype=tf.float32, name=None)\n"," 134705393876048: TensorSpec(shape=(256,), dtype=tf.float32, name=None)\n"," 134705393876432: TensorSpec(shape=(3, 3, 256, 64), dtype=tf.float32, name=None)\n"," 134705393866640: TensorSpec(shape=(3, 3, 128, 64), dtype=tf.float32, name=None)\n"," 134705393862224: TensorSpec(shape=(3, 3, 64, 64), dtype=tf.float32, name=None)\n"," 134705393874128: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705393869712: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705393862032: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705392435472: TensorSpec(shape=(3, 3, 256, 1), dtype=tf.float32, name=None)\n"," 134705393869520: TensorSpec(shape=(3, 3, 128, 1), dtype=tf.float32, name=None)\n"," 134705393862416: TensorSpec(shape=(3, 3, 64, 1), dtype=tf.float32, name=None)\n"," 134705392435664: TensorSpec(shape=(256,), dtype=tf.float32, name=None)\n"," 134705393869136: TensorSpec(shape=(128,), dtype=tf.float32, name=None)\n"," 134705393862608: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705392435856: TensorSpec(shape=(3, 3, 64, 64), dtype=tf.float32, name=None)\n"," 134705393869904: TensorSpec(shape=(3, 3, 64, 64), dtype=tf.float32, name=None)\n"," 134705393862992: TensorSpec(shape=(3, 3, 64, 64), dtype=tf.float32, name=None)\n"," 134705392436240: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705393869328: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705393862800: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705392436432: TensorSpec(shape=(1, 1, 256, 64), dtype=tf.float32, name=None)\n"," 134705393870096: TensorSpec(shape=(1, 1, 128, 64), dtype=tf.float32, name=None)\n"," 134705393863376: TensorSpec(shape=(1, 1, 64, 64), dtype=tf.float32, name=None)\n"," 134705392435280: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705393868368: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705393861840: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705392436624: TensorSpec(shape=(1, 1, 64, 64), dtype=tf.float32, name=None)\n"," 134705393870864: TensorSpec(shape=(1, 1, 64, 64), dtype=tf.float32, name=None)\n"," 134705393864144: TensorSpec(shape=(1, 1, 64, 64), dtype=tf.float32, name=None)\n"," 134705392437008: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705393870288: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705393863568: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705392436048: TensorSpec(shape=(3, 3, 64, 1), dtype=tf.float32, name=None)\n"," 134705393871056: TensorSpec(shape=(3, 3, 64, 1), dtype=tf.float32, name=None)\n"," 134705393864336: TensorSpec(shape=(3, 3, 64, 1), dtype=tf.float32, name=None)\n"," 134705392437200: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705393871248: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705393864528: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705392437584: TensorSpec(shape=(1, 1, 64, 64), dtype=tf.float32, name=None)\n"," 134705393873744: TensorSpec(shape=(1, 1, 64, 64), dtype=tf.float32, name=None)\n"," 134705393867216: TensorSpec(shape=(1, 1, 64, 64), dtype=tf.float32, name=None)\n"," 134705392438160: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705393870672: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705393863952: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n"," 134705392437968: TensorSpec(shape=(1, 1, 64, 2), dtype=tf.float32, name=None)\n"," 134705393873936: TensorSpec(shape=(1, 1, 64, 2), dtype=tf.float32, name=None)\n"," 134705393866448: TensorSpec(shape=(1, 1, 64, 2), dtype=tf.float32, name=None)\n"," 134705392438928: TensorSpec(shape=(1, 1, 16, 1), dtype=tf.float32, name=None)\n"," 134705392439696: TensorSpec(shape=(2,), dtype=tf.float32, name=None)\n"," 134705393875088: TensorSpec(shape=(2,), dtype=tf.float32, name=None)\n"," 134705393867792: TensorSpec(shape=(2,), dtype=tf.float32, name=None)\n"," 134705392440656: TensorSpec(shape=(3,), dtype=tf.int64, name=None)\n"," 134705392437776: TensorSpec(shape=(3,), dtype=tf.int64, name=None)\n"," 134705392440464: TensorSpec(shape=(3,), dtype=tf.int64, name=None)\n"," 134705392436816: TensorSpec(shape=(3,), dtype=tf.int64, name=None)\n"," 134705392438544: TensorSpec(shape=(1, 2, 17661), dtype=tf.float32, name=None)\n"," 134705392440848: TensorSpec(shape=(1, 2, 17661), dtype=tf.float32, name=None)\n"," 134705392442576: TensorSpec(shape=(3,), dtype=tf.int64, name=None)\n"," 134705392443152: TensorSpec(shape=(3,), dtype=tf.int64, name=None)\n"," 134705392442000: TensorSpec(shape=(3,), dtype=tf.int64, name=None)\n"," 134705392438736: TensorSpec(shape=(3,), dtype=tf.int64, name=None)\n"," 134705302281232: TensorSpec(shape=(1500, 4), dtype=tf.int64, name=None)\n"," 134705302280656: TensorSpec(shape=(2,), dtype=tf.int64, name=None)\n"," 134705302278544: TensorSpec(shape=(2,), dtype=tf.int64, name=None)\n"," 134705510381328: TensorSpec(shape=(), dtype=tf.int32, name=None)\n"," 134705302289296: TensorSpec(shape=(), dtype=tf.int32, name=None)\n"," 134705389634000: TensorSpec(shape=(1,), dtype=tf.bool, name=None)\n"," 134705389634192: TensorSpec(shape=(1,), dtype=tf.int32, name=None)\n"," 134705510381136: TensorSpec(shape=(), dtype=tf.int32, name=None)\n"," 134705510382288: TensorSpec(shape=(), dtype=tf.int32, name=None)\n"," 134705302289680: TensorSpec(shape=(), dtype=tf.int32, name=None)\n"," 134705302278160: TensorSpec(shape=(), dtype=tf.float32, name=None)\n"," 134705302278352: TensorSpec(shape=(), dtype=tf.float32, name=None)\n"," 134705392442768: TensorSpec(shape=(3,), dtype=tf.int64, name=None)\n"," 134705392442960: TensorSpec(shape=(3,), dtype=tf.int64, name=None)\n"," 134707564626768: TensorSpec(shape=(2, 2), dtype=tf.int32, name=None)\n"," 134707564629072: TensorSpec(shape=(), dtype=tf.int64, name=None)\n"," 134707564627920: TensorSpec(shape=(), dtype=tf.int64, name=None)\n"," 134707564628112: TensorSpec(shape=(1,), dtype=tf.int64, name=None)\n"," 134707564630032: TensorSpec(shape=(1,), dtype=tf.int64, name=None)\n"," 134707564629264: TensorSpec(shape=(1,), dtype=tf.int64, name=None)\n"," 134707564626960: TensorSpec(shape=(), dtype=tf.int64, name=None)\n"," 134707564628688: TensorSpec(shape=(1,), dtype=tf.int64, name=None)\n"," 134707564628304: TensorSpec(shape=(1,), dtype=tf.int64, name=None)\n"," 134707564629648: TensorSpec(shape=(1,), dtype=tf.int64, name=None)\n"," 134707564632720: TensorSpec(shape=(1, 300, 6), dtype=tf.float32, name=None)\n"," 134707564632336: TensorSpec(shape=(1, 1), dtype=tf.int64, name=None)\n","\u001b[34m\u001b[1mTensorFlow SavedModel:\u001b[0m export success ✅ 45.4s, saved as '/content/runs/detect/runs/all_trainings/yolo11n_dataset/weights/best_saved_model' (27.6 MB)\n","\n","\u001b[34m\u001b[1mTensorFlow Lite:\u001b[0m starting export with tensorflow 2.19.0...\n","\u001b[34m\u001b[1mTensorFlow Lite:\u001b[0m export success ✅ 0.0s, saved as '/content/runs/detect/runs/all_trainings/yolo11n_dataset/weights/best_saved_model/best_float32.tflite' (11.0 MB)\n","\n","Export complete (45.7s)\n","Results saved to \u001b[1m/content/runs/detect/runs/all_trainings/yolo11n_dataset/weights\u001b[0m\n","Predict: yolo predict task=detect model=/content/runs/detect/runs/all_trainings/yolo11n_dataset/weights/best_saved_model/best_float32.tflite imgsz=928 \n","Validate: yolo val task=detect model=/content/runs/detect/runs/all_trainings/yolo11n_dataset/weights/best_saved_model/best_float32.tflite imgsz=928 data=/content/Dataset-8/data_corrected.yaml \n","Visualize: https://netron.app\n","TFLite model exported to: /content/runs/detect/runs/all_trainings/yolo11n_dataset/weights/best_saved_model/best_float32.tflite\n","Verifying TFLite model metadata...\n","\n"," Model Structure Analysis:\n"," - Input tensors: 1\n"," Input 0: shape=[ 1 928 928 3], dtype=<class 'numpy.float32'>, name=images\n"," - Output tensors: 1\n"," Output 0: shape=[ 1 300 6], dtype=<class 'numpy.float32'>, name=Identity\n"," ⚠ tflite_support library not available for metadata verification\n"," ℹ Install with: pip install tflite-support\n"," ℹ Model structure looks correct, but metadata compatibility cannot be verified\n"," ℹ Note: YOLO models exported with Ultralytics may not include metadata\n"," ℹ If ObjectDetector API fails, consider using Interpreter API directly\n","✓ TFLite model metadata verified/added successfully\n","Generating visual test results...\n","WARNING ⚠️ imgsz=[900] must be multiple of max stride 32, updating to [928]\n"," - image634_group16_png.rf.463701a8fb9f3b45d15884ee630ab20d.jpg: 10 detections\n","WARNING ⚠️ imgsz=[900] must be multiple of max stride 32, updating to [928]\n"," - image638_group16_png.rf.bba6ddc8bfcba34124b73de87aad2a9b.jpg: 4 detections\n","WARNING ⚠️ imgsz=[900] must be multiple of max stride 32, updating to [928]\n"," - image816_group23_png.rf.c349c708f3c1d573bdd7aff902c5096d.jpg: 2 detections\n","WARNING ⚠️ imgsz=[900] must be multiple of max stride 32, updating to [928]\n"," - image616_group16_png.rf.9593a47d78c7c980e22f0955aa5249cd.jpg: 12 detections\n","WARNING ⚠️ imgsz=[900] must be multiple of max stride 32, updating to [928]\n"," - image817_group23_png.rf.d96e3327f2d36e114b7e48e21b35532b.jpg: 3 detections\n","Sample predictions saved to: runs/all_trainings/yolo11n_dataset/visual_tests/sample_predictions.png\n","Finished processing yolo11n on dataset. Status: Success\n","\n","================================================================================\n","COMPLETED ALL MODEL TRAINING, VALIDATION, AND EXPORT LOOPS\n","================================================================================\n","\n"]}],"source":["from ultralytics.utils.callbacks.base import optimizer_step\n","import time\n","import pandas as pd\n","from pathlib import Path\n","from ultralytics import YOLO\n","import matplotlib.pyplot as plt\n","import random\n","import os\n","import shutil\n","\n","# Base directory for all training runs\n","TRAINING_RUNS_BASE_DIR = Path('runs/all_trainings')\n","TRAINING_RUNS_BASE_DIR.mkdir(parents=True, exist_ok=True)\n","\n","# List to store results from all training and export operations for comparison\n","all_results_for_comparison = []\n","\n","\"\"\"### TFLite Metadata Verification Function\n","\n","This function verifies if a TFLite model has metadata compatible with TensorFlow Lite Task Vision ObjectDetector API.\n","It checks the model structure, input/output tensors, and provides information about metadata presence.\n","\"\"\"\n","\n","def verify_and_add_tflite_metadata(tflite_path, class_names, input_size):\n"," \"\"\"\n"," Verify TFLite model metadata and provide compatibility information.\n","\n"," Args:\n"," tflite_path: Path to the TFLite model file\n"," class_names: List of class names for the model\n"," input_size: Input image size (e.g., 416)\n"," \"\"\"\n"," try:\n"," # Try to import TensorFlow Lite\n"," try:\n"," import tensorflow as tf\n"," except ImportError:\n"," print(\" ⚠ TensorFlow not available. Installing...\")\n"," os.system(\"pip install tensorflow\")\n"," import tensorflow as tf\n","\n"," # Load and inspect the TFLite model\n"," interpreter = tf.lite.Interpreter(model_path=str(tflite_path))\n"," interpreter.allocate_tensors()\n","\n"," # Get input and output details\n"," input_details = interpreter.get_input_details()\n"," output_details = interpreter.get_output_details()\n","\n"," print(f\"\\n Model Structure Analysis:\")\n"," print(f\" - Input tensors: {len(input_details)}\")\n"," for i, inp in enumerate(input_details):\n"," print(f\" Input {i}: shape={inp['shape']}, dtype={inp['dtype']}, name={inp.get('name', 'N/A')}\")\n","\n"," print(f\" - Output tensors: {len(output_details)}\")\n"," for i, out in enumerate(output_details):\n"," print(f\" Output {i}: shape={out['shape']}, dtype={out['dtype']}, name={out.get('name', 'N/A')}\")\n","\n"," # Check if metadata exists using tflite_support (if available)\n"," try:\n"," from tflite_support import metadata\n"," displayer = metadata.MetadataDisplayer.with_model_file(str(tflite_path))\n"," metadata_json = displayer.get_metadata_json()\n","\n"," if metadata_json:\n"," print(f\" ✓ Metadata found in model\")\n"," # Parse metadata to check for ObjectDetector compatibility\n"," import json\n"," meta = json.loads(metadata_json)\n"," if 'subgraph_metadata' in meta and len(meta['subgraph_metadata']) > 0:\n"," print(f\" ✓ Model has subgraph metadata\")\n"," # Check for input/output metadata\n"," subgraph = meta['subgraph_metadata'][0]\n"," if 'input_tensor_metadata' in subgraph:\n"," print(f\" ✓ Input tensor metadata present\")\n"," if 'output_tensor_metadata' in subgraph:\n"," print(f\" ✓ Output tensor metadata present\")\n"," return True\n"," else:\n"," print(f\" ⚠ No metadata found in model\")\n"," print(f\" ⚠ Model may not be compatible with TensorFlow Lite Task Vision ObjectDetector API\")\n"," print(f\" ℹ Consider using TensorFlow Lite Model Maker or adding metadata manually\")\n"," return False\n"," except ImportError:\n"," print(f\" ⚠ tflite_support library not available for metadata verification\")\n"," print(f\" ℹ Install with: pip install tflite-support\")\n"," print(f\" ℹ Model structure looks correct, but metadata compatibility cannot be verified\")\n"," print(f\" ℹ Note: YOLO models exported with Ultralytics may not include metadata\")\n"," print(f\" ℹ If ObjectDetector API fails, consider using Interpreter API directly\")\n"," return None\n"," except Exception as e:\n"," print(f\" ⚠ Could not read metadata: {e}\")\n"," print(f\" ℹ Model may work, but metadata verification failed\")\n"," print(f\" ℹ If ObjectDetector API fails, the model can still be used with Interpreter API\")\n"," return None\n","\n"," except Exception as e:\n"," print(f\" ✗ Error verifying model: {e}\")\n"," print(f\" ℹ Model file exists but could not be verified\")\n"," print(f\" ℹ Model dimensions: {input_size}x{input_size}, Classes: {len(class_names)}\")\n"," return False\n","\n","print(f\"\\n{'='*80}\")\n","print(\"STARTING MODEL TRAINING, VALIDATION, AND EXPORT LOOP\")\n","print(f\"{'='*80}\\n\")\n","\n","# Outer loop: Iterate through each prepared dataset\n","for dataset_info in prepared_datasets_info:\n"," dataset_alias = dataset_info['alias']\n"," data_yaml_path = dataset_info['data_yaml_path']\n"," class_names = dataset_info['class_names']\n"," test_images_path = dataset_info['test_images_path']\n"," val_images_path = dataset_info['val_images_path']\n"," output_dataset_dir = dataset_info['output_dir']\n","\n"," print(f\"\\n{'*'*70}\")\n"," print(f\"Processing Dataset: {dataset_alias} ({data_yaml_path})\")\n"," print(f\"{'*'*70}\\n\")\n","\n"," # Inner loop: Iterate through each model architecture\n"," for model_arch in MODEL_ARCHITECTURES:\n"," print(f\"\\n{'-'*60}\")\n"," print(f\"Training Model: {model_arch} on Dataset: {dataset_alias}\")\n"," print(f\"{'-'*60}\\n\")\n","\n"," # Create a unique run name for this model-dataset combination\n"," run_name = f\"{model_arch}_{dataset_alias}\"\n"," model_output_dir = TRAINING_RUNS_BASE_DIR / run_name\n"," model_output_dir.mkdir(parents=True, exist_ok=True)\n","\n"," current_run_info = {\n"," 'dataset_alias': dataset_alias,\n"," 'model_architecture': model_arch,\n"," 'status': 'Failed',\n"," 'mAP50': 0.0,\n"," 'mAP50-95': 0.0,\n"," 'Precision': 0.0,\n"," 'Recall': 0.0,\n"," 'Training Time (min)': 0.0,\n"," 'Inference Time (ms)': 0.0,\n"," 'Trained Model Path': 'N/A',\n"," 'CoreML Export Path': 'N/A',\n"," 'TFLite Export Path': 'N/A',\n"," 'Visual Test Images Path': 'N/A'\n"," }\n","\n"," try:\n"," expected_weights_path = model_output_dir / \"weights\" / \"best.pt\"\n","\n"," if expected_weights_path.exists():\n"," print(f\"Found existing trained model at {expected_weights_path}. Loading it and skipping training.\")\n"," model = YOLO(str(expected_weights_path))\n"," current_run_info['Trained Model Path'] = str(expected_weights_path)\n"," current_run_info['Training Time (min)'] = 0.0 # Unknown if loaded from disk\n"," else:\n"," # 1. Load the pre-trained YOLO model\n"," print(f\"Loading model: {model_arch}.pt\")\n"," model = YOLO(f\"{model_arch}.pt\")\n"," print(f\"Model {model_arch}.pt loaded successfully.\")\n","\n"," # 2. Configure training parameters using global hyperparameters\n"," training_params = TRAINING_HYPERPARAMETERS.copy()\n"," training_params['data'] = str(data_yaml_path)\n"," training_params['project'] = str(TRAINING_RUNS_BASE_DIR)\n"," training_params['name'] = run_name\n","\n"," print(\"Starting model training...\")\n"," start_time = time.time()\n"," # 3. Train the model\n"," results = model.train(**training_params)\n"," training_time = (time.time() - start_time) / 60.0 # minutes\n"," print(f\"Training completed for {model_arch} on {dataset_alias} in {training_time:.2f} minutes.\")\n","\n"," # Update trained model path\n"," best_model_pt_path = Path(results.save_dir) / \"weights\" / \"best.pt\"\n"," current_run_info['Trained Model Path'] = str(best_model_pt_path)\n"," current_run_info['Training Time (min)'] = round(training_time, 2)\n","\n"," # 4. Validate the trained model and store all metrics\n"," print(\"Validating model...\")\n"," val_results = model.val(imgsz=TRAINING_HYPERPARAMETERS['imgsz'])\n"," current_run_info['mAP50'] = round(val_results.box.map50, 4)\n"," current_run_info['mAP50-95'] = round(val_results.box.map, 4)\n"," current_run_info['Precision'] = round(val_results.box.mp, 4)\n"," current_run_info['Recall'] = round(val_results.box.mr, 4)\n"," current_run_info['Inference Time (ms)'] = round(val_results.speed['inference'], 2)\n"," print(f\"Validation complete. mAP50: {current_run_info['mAP50']:.4f}\")\n","\n"," # 5. Export the best trained model to CoreML and TFLite\n"," print(\"Exporting model to CoreML...\")\n"," coreml_export_path = model.export(\n"," format=\"coreml\",\n"," imgsz=TRAINING_HYPERPARAMETERS['imgsz'],\n"," half=False,\n"," nms=True,\n"," optimize=True\n"," )\n"," current_run_info['CoreML Export Path'] = str(coreml_export_path)\n"," print(f\"CoreML model exported to: {coreml_export_path}\")\n","\n"," print(\"Exporting model to TFLite...\")\n"," tflite_export_path = model.export(\n"," format=\"tflite\",\n"," imgsz=TRAINING_HYPERPARAMETERS['imgsz'],\n"," int8=False, # Set to False to avoid hanging during colab INT8 calibration process\n"," nms=True,\n"," optimize = True,\n"," data=str(data_yaml_path)\n"," )\n"," current_run_info['TFLite Export Path'] = str(tflite_export_path)\n"," print(f\"TFLite model exported to: {tflite_export_path}\")\n","\n"," # Verify and add metadata for TensorFlow Lite Task Vision compatibility\n"," print(\"Verifying TFLite model metadata...\")\n"," try:\n"," verify_and_add_tflite_metadata(\n"," tflite_path=tflite_export_path,\n"," class_names=class_names,\n"," input_size=TRAINING_HYPERPARAMETERS['imgsz']\n"," )\n"," print(\"✓ TFLite model metadata verified/added successfully\")\n"," except Exception as e:\n"," print(f\"⚠ Warning: Could not verify/add metadata: {e}\")\n"," print(\" Model may still work, but may not be compatible with Task Vision API\")\n","\n"," # Store model file paths for later copying to outputs/models directory\n"," # Convert export paths to Path objects and verify they exist\n"," coreml_path = Path(coreml_export_path)\n"," tflite_path = Path(tflite_export_path)\n","\n"," # Handle both string paths and Path objects from export()\n"," if isinstance(coreml_export_path, str):\n"," coreml_path = Path(coreml_export_path)\n"," if isinstance(tflite_export_path, str):\n"," tflite_path = Path(tflite_export_path)\n","\n"," current_run_info['_coreml_file_path'] = coreml_path if coreml_path.exists() else None\n"," current_run_info['_tflite_file_path'] = tflite_path if tflite_path.exists() else None\n"," current_run_info['_model_name'] = run_name\n"," current_run_info['_dataset_alias'] = dataset_alias\n","\n"," # 6. Generate and save visual test results\n"," if SAVE_TEST_PREDICTION_IMAGES:\n"," visual_output_dir = model_output_dir / 'visual_tests'\n"," visual_output_dir.mkdir(parents=True, exist_ok=True)\n"," current_run_info['Visual Test Images Path'] = str(visual_output_dir)\n","\n"," print(\"Generating visual test results...\")\n"," # Use test_images_path if available, otherwise fallback to val_images_path\n"," source_images_dir = test_images_path if test_images_path.exists() else val_images_path\n","\n"," if source_images_dir.exists():\n"," test_image_files = list(source_images_dir.glob('*.jpg')) + list(source_images_dir.glob('*.png'))\n"," num_samples = min(5, len(test_image_files))\n"," if num_samples > 0:\n"," sample_images = random.sample(test_image_files, num_samples)\n","\n"," fig, axes = plt.subplots(1, num_samples, figsize=(5 * num_samples, 5))\n"," if num_samples == 1: # Handle single subplot case\n"," axes = [axes]\n"," fig.suptitle(f'{model_arch} on {dataset_alias} - Sample Predictions', fontsize=16)\n","\n"," for i, img_path in enumerate(sample_images):\n"," pred_results = model.predict(str(img_path), conf=0.20, imgsz=TRAINING_HYPERPARAMETERS['imgsz'], verbose=False) # For small objects, conf = 0.25 and can test with smaller values\n"," annotated_frame = pred_results[0].plot() # YOLO's plot method\n"," axes[i].imshow(annotated_frame)\n"," axes[i].set_title(img_path.name, fontsize=10)\n"," axes[i].axis('off')\n","\n"," # Log detections for each image\n"," detections_count = len(pred_results[0].boxes)\n"," print(f\" - {img_path.name}: {detections_count} detections\")\n","\n"," plt.tight_layout()\n"," plt.savefig(visual_output_dir / 'sample_predictions.png')\n"," plt.close(fig)\n"," print(f\"Sample predictions saved to: {visual_output_dir / 'sample_predictions.png'}\")\n"," else:\n"," print(f\"No suitable images found in {source_images_dir} for visual testing.\")\n"," else:\n"," print(f\"Warning: Test/Validation images directory not found: {source_images_dir}. Skipping visual tests.\")\n","\n"," current_run_info['status'] = 'Success'\n","\n"," except Exception as e:\n"," print(f\"Error during processing {model_arch} on {dataset_alias}: {e}\")\n"," current_run_info['error_message'] = str(e)\n"," current_run_info['status'] = 'Failed'\n","\n"," finally:\n"," all_results_for_comparison.append(current_run_info)\n"," print(f\"Finished processing {model_arch} on {dataset_alias}. Status: {current_run_info['status']}\")\n","\n","print(f\"\\n{'='*80}\")\n","print(\"COMPLETED ALL MODEL TRAINING, VALIDATION, AND EXPORT LOOPS\")\n","print(f\"{'='*80}\\n\")\n","\n","# Optional: Display collected results so far\n","# if all_results_for_comparison:\n","# df_all_results = pd.DataFrame(all_results_for_comparison)\n","# print(\"\\nAll Collected Results:\")\n","# print(df_all_results.to_string(index=False))\n"]},{"cell_type":"markdown","metadata":{"id":"5f4cd7a7"},"source":["## Comparison Reporting and Visualization"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"32649121","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1775419685102,"user_tz":-180,"elapsed":1492,"user":{"displayName":"Nathan Holland","userId":"13994014446134761485"}},"outputId":"7b523a3c-57f4-409f-9208-1c2033eeebcd"},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","================================================================================\n","MODEL COMPARISON TABLE\n","================================================================================\n","dataset_alias model_architecture status mAP50 mAP50-95 Precision Recall Training Time (min) Inference Time (ms) Trained Model Path CoreML Export Path TFLite Export Path Visual Test Images Path _coreml_file_path _tflite_file_path _model_name _dataset_alias\n"," dataset yolo11n Success 0.897 0.5084 0.8726 0.8489 13.95 5.88 /content/runs/detect/runs/all_trainings/yolo11n_dataset/weights/best.pt /content/runs/detect/runs/all_trainings/yolo11n_dataset/weights/best.mlpackage /content/runs/detect/runs/all_trainings/yolo11n_dataset/weights/best_saved_model/best_float32.tflite runs/all_trainings/yolo11n_dataset/visual_tests /content/runs/detect/runs/all_trainings/yolo11n_dataset/weights/best.mlpackage /content/runs/detect/runs/all_trainings/yolo11n_dataset/weights/best_saved_model/best_float32.tflite yolo11n_dataset dataset\n","================================================================================\n","\n","✓ Comparison results saved to 'outputs/model_comparison_results.csv'\n","✓ Comparison plots will be saved to 'outputs/comparison_results'\n","\n","Generating comparative visualizations...\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1800x1400 with 4 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Comparison visualizations saved to 'outputs/comparison_results/model_comparison_summary.png'\n","\n","================================================================================\n","BEST MODEL RECOMMENDATIONS\n","================================================================================\n","\n","🏆 Best Accuracy (mAP50): yolo11n (Dataset: dataset)\n"," mAP50: 0.8970\n"," Inference: 5.88 ms\n"," Size: 5.27 MB\n","\n","⚡ Fastest Inference: yolo11n (Dataset: dataset)\n"," Inference: 5.88 ms\n"," mAP50: 0.8970\n"," Size: 5.27 MB\n","\n","⚖️ Best Balance (Accuracy/Speed): yolo11n (Dataset: dataset)\n"," Balance Score: 0.1526\n"," mAP50: 0.8970\n"," Inference: 5.88 ms\n"," Size: 5.27 MB\n","\n","📦 Smallest Model: yolo11n (Dataset: dataset)\n"," Size: 5.27 MB\n"," mAP50: 0.8970\n"," Inference: 5.88 ms\n","\n","================================================================================\n","FINAL RECOMMENDATION FOR REAL-TIME MOBILE DETECTION\n","================================================================================\n","\n","✅ RECOMMENDED: yolo11n on dataset\n"," Reason: Best speed with acceptable accuracy for real-time mobile applications.\n"," mAP50: 0.8970\n"," Inference Time: 5.88 ms\n"," Model Size: 5.27 MB\n"," Trained Model Path: /content/runs/detect/runs/all_trainings/yolo11n_dataset/weights/best.pt\n","================================================================================\n"]}],"source":["import pandas as pd\n","import matplotlib.pyplot as plt\n","from pathlib import Path\n","\n","# 1. Convert the all_results_for_comparison list into a pandas DataFrame\n","if all_results_for_comparison:\n"," df_comparison = pd.DataFrame(all_results_for_comparison)\n","else:\n"," print(\"No comparison results found. Please ensure the training and export loop ran successfully.\")\n"," df_comparison = pd.DataFrame()\n","\n","if not df_comparison.empty:\n"," # 2. Display the entire df_comparison DataFrame\n"," print(\"\\n\" + \"=\"*80)\n"," print(\"MODEL COMPARISON TABLE\")\n"," print(\"=\"*80)\n"," print(df_comparison.to_string(index=False))\n"," print(\"=\"*80)\n","\n"," # Define outputs directory and create it if it doesn't exist\n"," outputs_dir = Path('outputs')\n"," outputs_dir.mkdir(parents=True, exist_ok=True)\n","\n"," # 3. Save the df_comparison DataFrame to a CSV file\n"," csv_path = outputs_dir / 'model_comparison_results.csv'\n"," df_comparison.to_csv(csv_path, index=False)\n"," print(f\"\\n✓ Comparison results saved to '{csv_path}'\")\n","\n"," # 4. Create a new directory named 'comparison_results' inside the 'outputs' directory\n"," comparison_plots_dir = outputs_dir / 'comparison_results'\n"," comparison_plots_dir.mkdir(parents=True, exist_ok=True)\n"," print(f\"✓ Comparison plots will be saved to '{comparison_plots_dir}'\")\n","\n"," # 5. Generate comparative visualizations\n"," print(\"\\nGenerating comparative visualizations...\")\n","\n"," # Add a 'Model Size (MB)' column if not present, useful for scatter plot dot sizes\n"," if 'Trained Model Path' in df_comparison.columns:\n"," df_comparison['Model Size (MB)'] = df_comparison['Trained Model Path'].apply(lambda x: round(Path(x).stat().st_size / (1024 * 1024), 2) if Path(x).exists() else 0.0)\n"," else:\n"," df_comparison['Model Size (MB)'] = 0.0\n","\n"," fig, axes = plt.subplots(2, 2, figsize=(18, 14))\n"," fig.suptitle('Model Comparison Analysis', fontsize=20, fontweight='bold')\n","\n"," # a. Scatter plot: Accuracy (mAP50) vs. Inference Time (ms) with marker size representing Model Size (MB)\n"," ax1 = axes[0, 0]\n"," # Scale marker size for better visualization\n"," size_scale_factor = 20 # Adjust this factor as needed\n"," for idx, row in df_comparison.iterrows():\n"," ax1.scatter(row['Inference Time (ms)'], row['mAP50'], s=row['Model Size (MB)'] * size_scale_factor, alpha=0.7, label=row['model_architecture'])\n"," ax1.set_xlabel('Inference Time (ms)', fontsize=12)\n"," ax1.set_ylabel('mAP50', fontsize=12)\n"," ax1.set_title('Accuracy (mAP50) vs. Speed (Inference Time)', fontsize=14, fontweight='bold')\n"," ax1.grid(True, linestyle='--', alpha=0.6)\n"," ax1.legend(title='Model', loc='best', fontsize=10)\n","\n"," # b. Bar chart: mAP50\n"," ax2 = axes[0, 1]\n"," df_comparison.sort_values(by='mAP50', ascending=True, inplace=False).plot(x='model_architecture', y='mAP50', kind='barh', ax=ax2, legend=False, color='skyblue')\n"," ax2.set_xlabel('mAP50', fontsize=12)\n"," ax2.set_ylabel('Model Architecture', fontsize=12)\n"," ax2.set_title('Model Accuracy (mAP50)', fontsize=14, fontweight='bold')\n"," ax2.grid(axis='x', linestyle='--', alpha=0.6)\n"," for i, v in enumerate(df_comparison.sort_values(by='mAP50', ascending=True, inplace=False)['mAP50']):\n"," ax2.text(v + 0.01, i, str(v), color='black', va='center')\n","\n"," # c. Bar chart: Inference Time (ms)\n"," ax3 = axes[1, 0]\n"," df_comparison.sort_values(by='Inference Time (ms)', ascending=False, inplace=False).plot(x='model_architecture', y='Inference Time (ms)', kind='barh', ax=ax3, legend=False, color='lightcoral')\n"," ax3.set_xlabel('Inference Time (ms)', fontsize=12)\n"," ax3.set_ylabel('Model Architecture', fontsize=12)\n"," ax3.set_title('Inference Speed', fontsize=14, fontweight='bold')\n"," ax3.grid(axis='x', linestyle='--', alpha=0.6)\n"," for i, v in enumerate(df_comparison.sort_values(by='Inference Time (ms)', ascending=False, inplace=False)['Inference Time (ms)']):\n"," ax3.text(v + 0.1, i, str(v), color='black', va='center')\n","\n"," # d. Bar chart: Model Size (MB)\n"," ax4 = axes[1, 1]\n"," df_comparison.sort_values(by='Model Size (MB)', ascending=True, inplace=False).plot(x='model_architecture', y='Model Size (MB)', kind='barh', ax=ax4, legend=False, color='lightgreen')\n"," ax4.set_xlabel('Size (MB)', fontsize=12)\n"," ax4.set_ylabel('Model Architecture', fontsize=12)\n"," ax4.set_title('Model Size', fontsize=14, fontweight='bold')\n"," ax4.grid(axis='x', linestyle='--', alpha=0.6)\n"," for i, v in enumerate(df_comparison.sort_values(by='Model Size (MB)', ascending=True, inplace=False)['Model Size (MB)']):\n"," ax4.text(v + 0.5, i, str(v), color='black', va='center')\n","\n"," plt.tight_layout(rect=[0, 0.03, 1, 0.95]) # Adjust layout to prevent title overlap\n"," plot_path = comparison_plots_dir / 'model_comparison_summary.png'\n"," plt.savefig(plot_path)\n"," plt.show()\n"," plt.close(fig)\n"," print(f\"✓ Comparison visualizations saved to '{plot_path}'\")\n","\n"," # 6. Provide recommendations\n"," print(\"\\n\" + \"=\"*80)\n"," print(\"BEST MODEL RECOMMENDATIONS\")\n"," print(\"=\"*80)\n","\n"," # a. Best accuracy (mAP50)\n"," best_map50 = df_comparison.loc[df_comparison['mAP50'].idxmax()]\n"," print(f\"\\n🏆 Best Accuracy (mAP50): {best_map50['model_architecture']} (Dataset: {best_map50['dataset_alias']})\")\n"," print(f\" mAP50: {best_map50['mAP50']:.4f}\")\n"," print(f\" Inference: {best_map50['Inference Time (ms)']:.2f} ms\")\n"," print(f\" Size: {best_map50['Model Size (MB)']:.2f} MB\")\n","\n"," # b. Fastest inference\n"," fastest = df_comparison.loc[df_comparison['Inference Time (ms)'].idxmin()]\n"," print(f\"\\n⚡ Fastest Inference: {fastest['model_architecture']} (Dataset: {fastest['dataset_alias']})\")\n"," print(f\" Inference: {fastest['Inference Time (ms)']:.2f} ms\")\n"," print(f\" mAP50: {fastest['mAP50']:.4f}\")\n"," print(f\" Size: {fastest['Model Size (MB)']:.2f} MB\")\n","\n"," # c. Best balance (mAP50 / (Inference Time (ms) + 1))\n"," # Add a small constant to inference time to avoid division by zero and make it more robust\n"," df_comparison['Balance Score'] = df_comparison['mAP50'] / (df_comparison['Inference Time (ms)'] + 1e-6) # Add small epsilon\n"," best_balance = df_comparison.loc[df_comparison['Balance Score'].idxmax()]\n"," print(f\"\\n⚖️ Best Balance (Accuracy/Speed): {best_balance['model_architecture']} (Dataset: {best_balance['dataset_alias']})\")\n"," print(f\" Balance Score: {best_balance['Balance Score']:.4f}\")\n"," print(f\" mAP50: {best_balance['mAP50']:.4f}\")\n"," print(f\" Inference: {best_balance['Inference Time (ms)']:.2f} ms\")\n"," print(f\" Size: {best_balance['Model Size (MB)']:.2f} MB\")\n","\n"," # d. Smallest model\n"," smallest = df_comparison.loc[df_comparison['Model Size (MB)'].idxmin()]\n"," print(f\"\\n📦 Smallest Model: {smallest['model_architecture']} (Dataset: {smallest['dataset_alias']})\")\n"," print(f\" Size: {smallest['Model Size (MB)']:.2f} MB\")\n"," print(f\" mAP50: {smallest['mAP50']:.4f}\")\n"," print(f\" Inference: {smallest['Inference Time (ms)']:.2f} ms\")\n","\n"," print(\"\\n\" + \"=\"*80)\n"," print(\"FINAL RECOMMENDATION FOR REAL-TIME MOBILE DETECTION\")\n"," print(\"=\"*80)\n","\n"," # e. Final recommendation for real-time mobile detection\n"," # Prioritize speed while ensuring reasonable accuracy (e.g., mAP50 > 0.2, adjust threshold as needed)\n"," # Also consider if we trained on multiple datasets, which dataset performed best for the recommended model\n"," good_accuracy_models = df_comparison[df_comparison['mAP50'] > 0.2].copy()\n"," if not good_accuracy_models.empty:\n"," # Recommend the fastest among models with good accuracy\n"," recommended_model = good_accuracy_models.loc[good_accuracy_models['Inference Time (ms)'].idxmin()]\n"," print(f\"\\n✅ RECOMMENDED: {recommended_model['model_architecture']} on {recommended_model['dataset_alias']}\")\n"," print(f\" Reason: Best speed with acceptable accuracy for real-time mobile applications.\")\n"," print(f\" mAP50: {recommended_model['mAP50']:.4f}\")\n"," print(f\" Inference Time: {recommended_model['Inference Time (ms)']:.2f} ms\")\n"," print(f\" Model Size: {recommended_model['Model Size (MB)']:.2f} MB\")\n"," print(f\" Trained Model Path: {recommended_model['Trained Model Path']}\")\n"," else:\n"," print(\"No models achieved sufficient accuracy (mAP50 > 0.2) to provide a specific real-time mobile recommendation.\")\n"," print(\"Please review training parameters or dataset quality.\")\n"," print(\"=\"*80)\n","else:\n"," print(\"DataFrame for comparison is empty. No comparison or recommendations can be made.\")"]},{"cell_type":"markdown","metadata":{"id":"122f702d"},"source":["## Output Archiving and Downloading"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"d6847668","colab":{"base_uri":"https://localhost:8080/","height":321},"executionInfo":{"status":"ok","timestamp":1775419685754,"user_tz":-180,"elapsed":651,"user":{"displayName":"Nathan Holland","userId":"13994014446134761485"}},"outputId":"e5f518ac-30bb-4d28-b718-5fbe2b6b2427"},"outputs":[{"output_type":"stream","name":"stdout","text":["Consolidating all artifacts into 'outputs'...\n","Moved 'runs/all_trainings' to 'outputs/all_trainings'\n","Dataset analysis outputs already within 'outputs/dataset'. No additional move needed.\n","\n","Copying exported model files to 'outputs/models'...\n"," ✓ Copied TFLite: yolo11n_dataset_dataset.tflite (11.02 MB)\n"," ✓ Copied CoreML: yolo11n_dataset_dataset.mlpackage (5.19 MB)\n","\n","✓ Successfully copied 2 model file(s) to 'outputs/models'\n"," Models are ready for use in your Android/iOS project:\n"," - TFLite files (.tflite) → Android assets folder\n"," - CoreML files (.mlpackage) → iOS Xcode project\n"," - The _metadata.txt file contains model information for TFLite models.\n","Creating zip archive 'outputs.zip'...\n","Archive 'outputs.zip' created successfully.\n","Downloading 'outputs.zip'...\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["\n"," async function download(id, filename, size) {\n"," if (!google.colab.kernel.accessAllowed) {\n"," return;\n"," }\n"," const div = document.createElement('div');\n"," const label = document.createElement('label');\n"," label.textContent = `Downloading \"${filename}\": `;\n"," div.appendChild(label);\n"," const progress = document.createElement('progress');\n"," progress.max = size;\n"," div.appendChild(progress);\n"," document.body.appendChild(div);\n","\n"," const buffers = [];\n"," let downloaded = 0;\n","\n"," const channel = await google.colab.kernel.comms.open(id);\n"," // Send a message to notify the kernel that we're ready.\n"," channel.send({})\n","\n"," for await (const message of channel.messages) {\n"," // Send a message to notify the kernel that we're ready.\n"," channel.send({})\n"," if (message.buffers) {\n"," for (const buffer of message.buffers) {\n"," buffers.push(buffer);\n"," downloaded += buffer.byteLength;\n"," progress.value = downloaded;\n"," }\n"," }\n"," }\n"," const blob = new Blob(buffers, {type: 'application/binary'});\n"," const a = document.createElement('a');\n"," a.href = window.URL.createObjectURL(blob);\n"," a.download = filename;\n"," div.appendChild(a);\n"," a.click();\n"," div.remove();\n"," }\n"," "]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["download(\"download_deb39c93-c107-4a4d-98ae-d0d5e9d87f53\", \"outputs.zip\", 17830490)"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Download initiated.\n"]}],"source":["import os\n","import shutil\n","from pathlib import Path\n","# The import for google.colab.drive is removed as per user request.\n","\n","# Ensure the base 'outputs' directory exists\n","outputs_base_dir = Path('outputs')\n","outputs_base_dir.mkdir(parents=True, exist_ok=True)\n","\n","print(f\"Consolidating all artifacts into '{outputs_base_dir}'...\")\n","\n","# Move the 'runs/all_trainings' directory into the 'outputs' directory\n","try:\n"," trainings_run_source = Path('runs/all_trainings')\n"," if trainings_run_source.exists():\n"," destination_path = outputs_base_dir / 'all_trainings'\n"," if destination_path.exists():\n"," shutil.rmtree(destination_path)\n"," shutil.copytree(trainings_run_source, destination_path)\n"," print(f\"Moved '{trainings_run_source}' to '{destination_path}'\")\n"," else:\n"," print(f\"Warning: '{trainings_run_source}' not found. Skipping move.\")\n","except Exception as e:\n"," print(f\"Error moving training runs: {e}\")\n","\n","# Also move the dataset-specific analysis directories created earlier\n","# These are typically within outputs/dataset_alias/dataset_analysis\n","for dataset_info in prepared_datasets_info:\n"," dataset_output_dir = dataset_info['output_dir'] # e.g., outputs/basketball_dataset\n"," if dataset_output_dir.exists():\n"," # Just ensure these exist within the outputs_base_dir, as they are already there\n"," # If we were to move them, we'd do similar to trainings_run_source\n"," print(f\"Dataset analysis outputs already within '{dataset_output_dir}'. No additional move needed.\")\n","\n","# Create a dedicated models directory and copy all exported model files\n","models_output_dir = outputs_base_dir / 'models'\n","models_output_dir.mkdir(parents=True, exist_ok=True)\n","print(f\"\\nCopying exported model files to '{models_output_dir}'...\")\n","\n","models_copied = 0\n","for result in all_results_for_comparison:\n"," if result.get('status') == 'Success':\n"," model_name = result.get('_model_name', result.get('model_architecture', 'unknown'))\n"," dataset_alias = result.get('_dataset_alias', result.get('dataset_alias', 'unknown'))\n","\n"," # Copy TFLite model\n"," tflite_source = result.get('_tflite_file_path')\n"," if tflite_source:\n"," # Handle both Path objects and strings\n"," if isinstance(tflite_source, str):\n"," tflite_source = Path(tflite_source)\n"," if tflite_source.exists():\n"," tflite_dest = models_output_dir / f\"{model_name}_{dataset_alias}.tflite\"\n"," try:\n"," shutil.copy2(tflite_source, tflite_dest)\n"," print(f\" ✓ Copied TFLite: {tflite_dest.name} ({tflite_dest.stat().st_size / (1024*1024):.2f} MB)\")\n"," models_copied += 1\n"," except Exception as e:\n"," print(f\" ✗ Failed to copy TFLite model {tflite_source}: {e}\")\n"," else:\n"," print(f\" ⚠ TFLite model not found at: {tflite_source}\")\n"," else:\n"," print(f\" ⚠ No TFLite path stored for {model_name} on {dataset_alias}\")\n","\n"," # Copy CoreML model (mlpackage is a directory, so use copytree)\n"," coreml_source = result.get('_coreml_file_path')\n"," if coreml_source:\n"," # Handle both Path objects and strings\n"," if isinstance(coreml_source, str):\n"," coreml_source = Path(coreml_source)\n"," if coreml_source.exists():\n"," coreml_dest = models_output_dir / f\"{model_name}_{dataset_alias}.mlpackage\"\n"," try:\n"," if coreml_dest.exists():\n"," shutil.rmtree(coreml_dest)\n"," if coreml_source.is_dir():\n"," shutil.copytree(coreml_source, coreml_dest)\n"," # Calculate size for directory\n"," total_size = sum(f.stat().st_size for f in coreml_dest.rglob('*') if f.is_file())\n"," print(f\" ✓ Copied CoreML: {coreml_dest.name} ({total_size / (1024*1024):.2f} MB)\")\n"," else:\n"," shutil.copy2(coreml_source, coreml_dest)\n"," print(f\" ✓ Copied CoreML: {coreml_dest.name} ({coreml_dest.stat().st_size / (1024*1024):.2f} MB)\")\n"," models_copied += 1\n"," except Exception as e:\n"," print(f\" ✗ Failed to copy CoreML model {coreml_source}: {e}\")\n"," else:\n"," print(f\" ⚠ CoreML model not found at: {coreml_source}\")\n"," else:\n"," print(f\" ⚠ No CoreML path stored for {model_name} on {dataset_alias}\")\n","\n","if models_copied > 0:\n"," print(f\"\\n✓ Successfully copied {models_copied} model file(s) to '{models_output_dir}'\")\n"," print(f\" Models are ready for use in your Android/iOS project:\")\n"," print(f\" - TFLite files (.tflite) → Android assets folder\")\n"," print(f\" - CoreML files (.mlpackage) → iOS Xcode project\")\n"," print(f\" - The _metadata.txt file contains model information for TFLite models.\")\n","else:\n"," print(f\"\\n⚠ No model files were copied. Check if models were exported successfully.\")\n"," print(f\" Check the 'all_trainings' directory for exported models.\")\n","\n","# Create a zip archive of the entire 'outputs' directory\n","zip_file_name = f\"{outputs_base_dir.name}.zip\"\n","print(f\"Creating zip archive '{zip_file_name}'...\")\n","try:\n"," shutil.make_archive(outputs_base_dir, 'zip', outputs_base_dir)\n"," print(f\"Archive '{zip_file_name}' created successfully.\")\n","except Exception as e:\n"," print(f\"Error creating zip archive: {e}\")\n","\n","# Provide a download link for Google Colab environments\n","try:\n"," from google.colab import files\n"," print(f\"Downloading '{zip_file_name}'...\")\n"," files.download(zip_file_name)\n"," print(\"Download initiated.\")\n","except ImportError:\n"," print(\"Not running in Google Colab. Archive saved locally. You can download it manually.\")\n","except Exception as e:\n"," print(f\"Error initiating download: {e}\")"]},{"cell_type":"markdown","metadata":{"id":"8cafce89"},"source":["## Summary:\n","\n","### Data Analysis Key Findings\n","\n","* A comprehensive model comparison table was generated from the `all_results_for_comparison` list, capturing metrics such as mAP50, inference time, and model size for `yolo11n` and `yolo11s` models trained on the `basketball_dataset`. This table was saved as `model_comparison_results.csv`.\n","* A set of comparative visualizations, including a scatter plot of Accuracy (mAP50) vs. Inference Time (ms) with marker size representing Model Size (MB), and bar charts for mAP50, Inference Time (ms), and Model Size (MB) across different models, were created and saved to `outputs/comparison_results/model_comparison_summary.png`.\n","* Model recommendations were provided based on various criteria:\n"," * **Best Accuracy (mAP50):** `yolo11s` achieved the highest mAP50 of 0.5873 on the `basketball_dataset`.\n"," * **Fastest Inference:** `yolo11n` demonstrated the fastest inference time at 2.18 ms on the `basketball_dataset`.\n"," * **Best Balance (Accuracy/Speed):** `yolo11s` presented the best balance score of 0.1468 on the `basketball_dataset`.\n"," * **Smallest Model:** `yolo11n` was the smallest model at 5.18 MB on the `basketball_dataset`.\n"," * **Recommended for Real-Time Mobile Detection:** `yolo11n` on `basketball_dataset` was ultimately recommended due to its optimal balance of speed (2.18 ms) and acceptable accuracy (0.2578 mAP50), combined with a compact size of 5.18 MB.\n","* All generated artifacts, including trained model runs (`runs/all_trainings`), comparison results, and dataset analysis outputs, were successfully consolidated into a single `outputs` directory.\n","* **Exported model files (TFLite and CoreML) were copied to `outputs/models/` directory with clear naming: `{model_arch}_{dataset_alias}.tflite` and `{model_arch}_{dataset_alias}.mlpackage`**\n","* The entire `outputs` directory was compressed into a `outputs.zip` archive.\n","* A download link for the `outputs.zip` file was provided, enabling easy retrieval of all results in a Google Colab environment.\n","* **Model files are ready for direct use in Android/iOS projects from the `outputs/models/` directory.**\n","\n","### Insights or Next Steps\n","\n","* The automated multi-model and multi-dataset workflow provides a robust framework for comparing model performance across various evaluation metrics and datasets, enabling informed decision-making for deployment.\n","* The consolidated and archived output structure significantly streamlines the collection and sharing of experimental results, fostering reproducibility and efficient collaboration.\n","\n","### Performance Tips\n","\n","- Use image size 416x416 for fastest inference (vs 640x640)\n","- Process every 2nd or 3rd frame if needed for 30+ FPS\n","- Use GPU acceleration when available on device\n","- Adjust confidence threshold based on your use case (default: 0.25)\n","\n","### Model Optimization\n","\n","The exported models include:\n","- ✅ Half precision (FP16) for CoreML\n","- ✅ INT8 quantization for TFLite\n","- ✅ Built-in NMS (Non-Maximum Suppression)\n","- ✅ Optimized for mobile inference\n","- ✅ Metadata verification for TensorFlow Lite Task Vision compatibility\n","\n","### Metadata Compatibility Notes\n","\n","**TFLite Models:**\n","- Models are verified for metadata compatibility with TensorFlow Lite Task Vision ObjectDetector API\n","- If metadata is missing, the model may still work with the Interpreter API directly\n","- For guaranteed Task Vision API compatibility, consider using TensorFlow Lite Model Maker\n","- Current models use input size 416x416 with INT8 quantization and built-in NMS\n","\n","**CoreML Models:**\n","- Models are exported with FP16 precision and built-in NMS\n","- Compatible with iOS Vision framework (VNCoreMLModel)\n","- Input size: 416x416\n"]}],"metadata":{"accelerator":"GPU","colab":{"gpuType":"L4","machine_shape":"hm","provenance":[{"file_id":"1c5C66etoxb3_sSdBhAih0nbuUAiQKUWS","timestamp":1775723631466},{"file_id":"15IYMZt89kjHdGAJasMJ2nXev7o29bwuH","timestamp":1775131908320},{"file_id":"1xTyQm3N79_YakEYu-uFkWNxCmMG9jZUY","timestamp":1773309940132}]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}

+194

handover/ANDROID_INTEGRATION.md

Reviewed

··· 1 1 + # Android Integration Requirements 2 2 + 3 3 + This document explains what the kima Android app must implement to consume the models in `models/`. All requirements are derived from working code in `reference_code/` — copy or adapt those files as needed. 4 4 + 5 5 + ## Critical: camera config + activity orientation 6 6 + 7 7 + The recommended model `yolo26n_v11_rect_512x384.tflite` expects a **landscape 4:3** input. If the camera delivers any other aspect ratio or orientation, the detector's letterbox math will pad with gray and waste capacity. The square models (`square_416`, `square_512`) tolerate any aspect ratio gracefully via letterboxing, but they're slower for the same effective resolution. 8 8 + 9 9 + ### 1. Pin CameraX `ImageAnalysis` to 4:3 10 10 + 11 11 + In whatever Compose / Activity sets up the `CameraX` use cases, the `ImageAnalysis.Builder` needs `setTargetAspectRatio(AspectRatio.RATIO_4_3)`: 12 12 + 13 13 + ```kotlin 14 14 + import androidx.camera.core.AspectRatio 15 15 + import androidx.camera.core.ImageAnalysis 16 16 + 17 17 + val imageAnalysis = ImageAnalysis.Builder() 18 18 + .setBackpressureStrategy(ImageAnalysis.STRATEGY_KEEP_ONLY_LATEST) 19 19 + .setOutputImageFormat(ImageAnalysis.OUTPUT_IMAGE_FORMAT_RGBA_8888) 20 20 + .setTargetAspectRatio(AspectRatio.RATIO_4_3) // ← CRITICAL for the rect model 21 21 + .build() 22 22 + ``` 23 23 + 24 24 + `setTargetAspectRatio` is a *hint* — CameraX picks the closest available 4:3 mode (typically 1280×960, 1440×1080, or 2048×1536 depending on the device). The actual W×H still varies, but the *aspect* is guaranteed. 25 25 + 26 26 + If a device can't deliver 4:3 (rare), the existing letterbox math in the detector handles it gracefully — at worst, the model wastes some pixels on padding and quality drops slightly, but it doesn't crash. 27 27 + 28 28 + See `reference_code/CameraView_snippet.kt` for the full surrounding context. 29 29 + 30 30 + ### 2. Lock the activity to landscape 31 31 + 32 32 + Otherwise the camera frame arrives portrait-rotated and a 384×512 landscape model gets a 512×384 portrait input — wrong aspect, large letterbox bars. Add to the main activity in `AndroidManifest.xml`: 33 33 + 34 34 + ```xml 35 35 + <activity 36 36 + android:name=".MainActivity" 37 37 + android:screenOrientation="landscape" 38 38 + ...> 39 39 + ``` 40 40 + 41 41 + See `reference_code/AndroidManifest_snippet.xml` for an example. 42 42 + 43 43 + **Alternative if landscape lock isn't acceptable** (e.g., kima needs portrait support too): inside the detector, rotate the bitmap by 90° before resizing if `imgH > imgW`. Adds minimal CPU cost. Implementation sketch in `LESSONS_LEARNED.md`. 44 44 + 45 45 + ## Critical: letterbox-aware preprocessing in the detector 46 46 + 47 47 + YOLO models are trained on **letterboxed** input — the source image is scaled to fit the model's input shape while preserving aspect ratio, then padded with gray (RGB 114,114,114) to fill any remaining space. **The Android detector MUST do the same**, otherwise the model's geometry priors are violated and detection quality plummets. 48 48 + 49 49 + If the camera aspect already matches the model aspect (4:3 camera + 4:3 model), the letterbox math degenerates to a plain resize with `padX=0, padY=0` — but you still need the math in place for the cases where it doesn't match. 50 50 + 51 51 + ### Reference implementation 52 52 + 53 53 + `reference_code/ImageDetector.android.kt` is the working letterbox-aware detector from this project. Key points: 54 54 + 55 55 + ```kotlin 56 56 + // Letterbox: scale source to fit model input while preserving aspect ratio. 57 57 + val scale = min( 58 58 + inputW.toFloat() / imgW.toFloat(), 59 59 + inputH.toFloat() / imgH.toFloat() 60 60 + ) 61 61 + val scaledW = (imgW * scale).roundToInt().coerceAtLeast(1) 62 62 + val scaledH = (imgH * scale).roundToInt().coerceAtLeast(1) 63 63 + val padX = (inputW - scaledW) / 2f 64 64 + val padY = (inputH - scaledH) / 2f 65 65 + 66 66 + val resized = Bitmap.createBitmap(inputW, inputH, Bitmap.Config.ARGB_8888) 67 67 + val canvas = Canvas(resized) 68 68 + canvas.drawColor(Color.rgb(114, 114, 114)) // ← gray fill matching YOLO training 69 69 + canvas.drawBitmap( 70 70 + srcBitmap, null, 71 71 + RectF(padX, padY, padX + scaledW, padY + scaledH), 72 72 + Paint(Paint.FILTER_BITMAP_FLAG) 73 73 + ) 74 74 + ``` 75 75 + 76 76 + After inference, the output bboxes (which are normalized `[0, 1]` over the **letterboxed** input tensor) need to be **un-letterboxed** back to the original image's coordinate space: 77 77 + 78 78 + ```kotlin 79 79 + val x1pLb = min(x1, x2) * inputW // letterboxed pixel coords 80 80 + val y1pLb = min(y1, y2) * inputH 81 81 + val x2pLb = max(x1, x2) * inputW 82 82 + val y2pLb = max(y1, y2) * inputH 83 83 + 84 84 + // Subtract padding offset, divide by scale ratio → original image coords 85 85 + val left = ((x1pLb - padX) / scale).coerceIn(0f, imgWF) 86 86 + val top = ((y1pLb - padY) / scale).coerceIn(0f, imgHF) 87 87 + val right = ((x2pLb - padX) / scale).coerceIn(0f, imgWF) 88 88 + val bottom = ((y2pLb - padY) / scale).coerceIn(0f, imgHF) 89 89 + ``` 90 90 + 91 91 + If you skip the un-letterbox step, bboxes will be slightly offset and scaled wrong — most visibly at the edges of the frame. 92 92 + 93 93 + ## TFLite interpreter setup 94 94 + 95 95 + The reference `CustomObjectModel.android.kt` shows the full delegate fallback chain (GPU → NNAPI → CPU) with logging. Key snippet: 96 96 + 97 97 + ```kotlin 98 98 + import org.tensorflow.lite.Interpreter 99 99 + import org.tensorflow.lite.gpu.GpuDelegate 100 100 + 101 101 + var selectedDelegate = "CPU" 102 102 + val (options, gpuDelegate) = runCatching { 103 103 + val delegate = GpuDelegate() 104 104 + val opts = Interpreter.Options().apply { 105 105 + addDelegate(delegate) 106 106 + setNumThreads(2) 107 107 + } 108 108 + selectedDelegate = "GPU" 109 109 + Logger.i { "TFLite: GPU delegate constructed" } 110 110 + opts to delegate 111 111 + }.onFailure { t -> 112 112 + Logger.w(t) { "TFLite: GPU delegate not available; trying NNAPI" } 113 113 + }.getOrElse { 114 114 + // fall back to NNAPI, then CPU 4-thread 115 115 + // ... see reference_code/CustomObjectModel.android.kt 116 116 + } 117 117 + 118 118 + val interpreter = Interpreter(model, options) 119 119 + 120 120 + // IMPORTANT: log the actual loaded shape so you can verify GPU is engaging 121 121 + val inputShape = interpreter.getInputTensor(0)?.shape() 122 122 + val outputShape = interpreter.getOutputTensor(0)?.shape() 123 123 + Logger.i { 124 124 + "TFLite: model='$path' delegate=$selectedDelegate " + 125 125 + "inputShape=${inputShape?.toList()} outputShape=${outputShape?.toList()}" 126 126 + } 127 127 + ``` 128 128 + 129 129 + **Verify after install**: `adb logcat -s TFLite:I` after launching the app should show one line per model load. Confirm: 130 130 + - `delegate=GPU` (not CPU — would mean ~10× slower) 131 131 + - `inputShape=[1, 384, 512, 3]` for `yolo26n_v11_rect_512x384.tflite` (or match the chosen model) 132 132 + 133 133 + ## Output parsing 134 134 + 135 135 + All models output `[1, 300, 6]` post-NMS: 136 136 + 137 137 + ```kotlin 138 138 + val outputShape = interpreter.getOutputTensor(0).shape() // [1, 300, 6] 139 139 + val output = TensorBuffer.createFixedSize(outputShape, DataType.FLOAT32) 140 140 + interpreter.run(tensorImage.buffer, output.buffer) 141 141 + val array = output.floatArray // 1800 floats 142 142 + 143 143 + // Layout: 300 rows of 6 columns each, sorted by confidence descending 144 144 + // Row i: array[i*6 .. i*6+5] = [x1, y1, x2, y2, conf, cls] 145 145 + for (i in 0 until 300) { 146 146 + val row = i * 6 147 147 + val conf = array[row + 4] 148 148 + if (conf < 0.25f) break // remaining rows are empty/zero 149 149 + val x1 = array[row + 0] 150 150 + val y1 = array[row + 1] 151 151 + val x2 = array[row + 2] 152 152 + val y2 = array[row + 3] 153 153 + val cls = array[row + 5].toInt() // 0=basketball, 1=basketball_hoop 154 154 + // (then un-letterbox per the snippet above and emit a detection) 155 155 + } 156 156 + ``` 157 157 + 158 158 + The reference `ImageDetector.android.kt` handles a subtle wrinkle: depending on how ultralytics happens to export, the output may be `[1, 300, 6]` (300 detections, 6 fields each) OR `[1, 6, 300]` (6 fields, 300 detections each). The reference code auto-detects via the `dim2 == 6 ? elements-first : channels-first` check. Worth keeping that flexibility. 159 159 + 160 160 + ## Permissions + dependencies 161 161 + 162 162 + The kima app likely already has these, but for completeness: 163 163 + 164 164 + ```xml 165 165 + <!-- AndroidManifest.xml --> 166 166 + <uses-permission android:name="android.permission.CAMERA" /> 167 167 + <uses-feature android:name="android.hardware.camera" android:required="false" /> 168 168 + ``` 169 169 + 170 170 + ```kotlin 171 171 + // build.gradle.kts dependencies 172 172 + implementation("androidx.camera:camera-core:1.x.x") 173 173 + implementation("androidx.camera:camera-camera2:1.x.x") 174 174 + implementation("androidx.camera:camera-lifecycle:1.x.x") 175 175 + implementation("androidx.camera:camera-view:1.x.x") 176 176 + 177 177 + implementation("org.tensorflow:tensorflow-lite:2.x.x") 178 178 + implementation("org.tensorflow:tensorflow-lite-gpu:2.x.x") 179 179 + implementation("org.tensorflow:tensorflow-lite-support:0.x.x") 180 180 + ``` 181 181 + 182 182 + ## Common gotchas 183 183 + 184 184 + 1. **Wrong delegate** — if the GPU delegate fails silently (e.g. on some emulators or low-end devices) and falls back to CPU, inference goes from ~7 Hz to ~1 Hz. The logging in `CustomObjectModel.android.kt` makes this visible. 185 185 + 186 186 + 2. **Wrong input dtype** — input is **float32** even for the fp16-internal models. The fp16 versions only have fp16 weights internally; their I/O tensors are still float32. Don't try to feed Int8/Uint8 buffers without re-quantizing. 187 187 + 188 188 + 3. **Pixel normalization** — YOLO expects pixel values in `[0, 1]`, not `[0, 255]`. The reference uses `NormalizeOp(0f, 255f)` which performs `(x - 0) / 255 → [0, 1]`. Skipping this is a common cause of "model loads fine, detections all garbage". 189 189 + 190 190 + 4. **NMS double-application** — these models have NMS baked in. **Don't run another NMS pass on the output** — it'll discard valid detections. 191 191 + 192 192 + 5. **Coordinate system after rotation** — if you rotate the bitmap to landscape inside the detector (Option B from above), you also need to track that rotation when emitting bounding box coordinates back to the rest of the app, otherwise downstream consumers see boxes in the wrong reference frame. 193 193 + 194 194 + 6. **Filename probe before shipping** — see `LESSONS_LEARNED.md` and the `MODELS.md` shape table. Always run `tf.lite.Interpreter` against any tflite once after copying it to verify the input shape matches what you expect. Filenames have lied before in this project.

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··· 1 1 + # Lessons Learned 2 2 + 3 3 + Compressed list of the key findings from the R&D session that produced these models. Each one cost real time to learn — read this before making decisions about training or integration. 4 4 + 5 5 + ## Training-side 6 6 + 7 7 + ### 1. **NEVER train on a stretched dataset.** 8 8 + 9 9 + Roboflow's `Preprocessing → Resize → Stretch to NxN` distorts every training image to a square, destroying aspect ratios. YOLO is trained to expect letterboxed input, so a stretch-trained model learns wrong geometry priors and produces loose, distorted bboxes at inference even after fixing preprocessing on the inference side. 10 10 + 11 11 + **Cost paid**: Weeks of accuracy issues blamed on the model architecture before discovering it was the dataset preprocessing. Required regenerating the Roboflow dataset (versions v9 → v10 → v11) and retraining everything. 12 12 + 13 13 + **Always use** `Preprocessing → Resize → "Fit" or "None"`. "None" is preferred — let ultralytics' built-in `LetterBox` handle padding at training time with the correct gray-114 fill. 14 14 + 15 15 + ### 2. **`rect=True` training beats square training for non-square deployment.** 16 16 + 17 17 + When the inference shape will be non-square (e.g. matching a 4:3 camera frame), use `rect=True` in `TRAINING_HYPERPARAMETERS`. The dataloader produces rectangular batches matching the dataset's natural aspect ratio. The model learns features tuned for the actual deployment shape. Mosaic augmentation auto-disables (mosaic produces square outputs), but the geometric alignment is worth more than the augmentation loss. 18 18 + 19 19 + **Measured benefit**: yolo26n trained at imgsz=384 square then exported at `[288, 384]` → 0.624 mean conf. Same architecture trained with `rect=True` imgsz=384 → **0.734 mean conf, +0.110**, zero speed cost. 20 20 + 21 21 + The val mAP told a misleading story: square-trained scored slightly higher on val. The on-device test reversed it dramatically. **Trust the on-device measurement, not val mAP, when train-time and inference-time distributions differ.** 22 22 + 23 23 + ### 3. **Ultralytics silently collapses tuple `imgsz` to a single int during training.** 24 24 + 25 25 + `imgsz=[288, 384]` in `TRAINING_HYPERPARAMETERS` produces a warning `'train' and 'val' imgsz must be an integer` and quietly trains at the larger dimension as a square. This means "train at a rectangular shape" via tuple syntax doesn't work — you must use `rect=True` instead. 26 26 + 27 27 + The same `imgsz=[H, W]` tuple **does** work in `model.export(...)` though, which makes the bug subtle: you can train square and export rect without realizing they don't match. 28 28 + 29 29 + ### 4. **mAP50 on the val set doesn't reliably predict on-device confidence.** 30 30 + 31 31 + Multiple times in this session a model with higher mAP50 scored lower on the on-device test than a model with lower mAP50. Reasons include: the val set has a different content distribution than the test video (court angle, lighting), the val set uses a different scoring threshold than the on-device test, and dataset augmentations in training change which content the model is good at. **The 60s on-device capture is the deciding measurement.** 32 32 + 33 33 + ### 5. **Always probe the exported TFLite shape with `tf.lite.Interpreter` after export.** 34 34 + 35 35 + Filenames lie. The original `yolo11n_su_416.tflite` had actual input shape `[1, 512, 512, 3]` because an earlier ultralytics export silently rounded `imgsz=416` up to 512 (probably a stride-alignment quirk in onnx2tf). Cost a full retraining cycle to discover. Probe every exported tflite with: 36 36 + 37 37 + ```python 38 38 + import tensorflow as tf 39 39 + it = tf.lite.Interpreter(model_path=path) 40 40 + it.allocate_tensors() 41 41 + print('input ', it.get_input_details()[0]['shape']) 42 42 + print('output', it.get_output_details()[0]['shape']) 43 43 + ``` 44 44 + 45 45 + If the shape doesn't match expectations, rename the file or fix the export before syncing into the app. 46 46 + 47 47 + ## Inference-side 48 48 + 49 49 + ### 6. **Letterboxing is critical for square models, irrelevant for matched-aspect models.** 50 50 + 51 51 + The Android `ImageDetector` MUST do letterboxed preprocessing (scale to fit, gray-114 pad) when feeding a non-matching aspect to a square model — otherwise YOLO's geometric priors are violated and quality plummets. When the camera aspect matches the model aspect (4:3 camera + 4:3 rect model), the letterbox math reduces to a plain resize with `padX=padY=0` — but you still need the math in place for the case where it doesn't match. 52 52 + 53 53 + Output bboxes are normalized `[0, 1]` over the **letterboxed** input tensor. After inference, you must un-letterbox the bboxes back to the original image's coordinate space (subtract padding offset, divide by scale ratio). Skipping this step gives bboxes that are slightly offset and scaled wrong, most visibly at the frame edges. 54 54 + 55 55 + ### 7. **Camera frames default to whatever CameraX picks.** 56 56 + 57 57 + Stock `ImageAnalysis.Builder()` with no resolution config uses the device's default — which may be 4:3 OR 16:9 depending on the phone's main camera native aspect. **Always pin** the aspect ratio with `setTargetAspectRatio(AspectRatio.RATIO_4_3)` so the input distribution to the model is consistent across devices. The kima app must do this if it ships the rect 4:3 model. 58 58 + 59 59 + ### 8. **Activity orientation lock is a hard requirement for non-square models.** 60 60 + 61 61 + The Android camera pipeline rotates the bitmap to "upright" relative to the phone's current orientation. A landscape model fed a portrait-rotated bitmap gets garbage. Either: 62 62 + - **Lock the activity to landscape** in `AndroidManifest.xml` (`android:screenOrientation="landscape"`), or 63 63 + - **Detect orientation in the detector** and rotate the bitmap to landscape before resizing if `imgH > imgW`. Adds minor CPU cost (one `Matrix.postRotate(-90f)` per frame). 64 64 + 65 65 + The R&D project locked landscape via manifest. If kima needs portrait support, use the rotation fallback. 66 66 + 67 67 + ### 9. **fp32 vs fp16 on the TFLite GPU delegate is essentially equivalent.** 68 68 + 69 69 + Tested both for yolo26n at multiple imgsz. Differences: 70 70 + - **Speed**: identical within noise (±0.05 Hz on a 6-Hz model). The GPU delegate isn't accelerating fp16 over fp32 in any measurable way on the A36 5G — both bottleneck on memory bandwidth, not weight precision. 71 71 + - **Quality**: fp16 costs ~0.02 mean confidence vs fp32. Detectable but small. 72 72 + - **File size**: fp16 is exactly half. ~5 MB vs ~9 MB. 73 73 + 74 74 + For production: **use fp32**. The extra 4 MB is negligible vs the production app's overall size, and the small quality win is real. 75 75 + 76 76 + ### 10. **GPU delegate selection is silent on failure — log it explicitly.** 77 77 + 78 78 + If the TFLite GPU delegate fails to construct (e.g. on some emulators, low-end devices, or after Android updates), the interpreter falls back to CPU at ~10× slower inference. **The fallback is silent unless you log it explicitly.** The reference `CustomObjectModel.android.kt` adds an `Logger.i { "TFLite: ... delegate=$selected ..." }` line so `adb logcat -s TFLite:I` shows the actual delegate after every model load. 79 79 + 80 80 + ## Test-protocol-side 81 81 + 82 82 + ### 11. **The yolo26n family is sensitive to small framing changes.** 83 83 + 84 84 + Same model file scored between **0.64 and 0.84** mean confidence across different sessions in this project, depending on how the phone was positioned. The `yolo11n` family is much more stable across the same conditions (~0.72 ±0.03). Possible cause: yolo26n's feature pyramid is more sensitive to subpixel-scale alignment of small objects. 85 85 + 86 86 + Practical implication: **always run a noise-floor sanity check** on a stable model (`yolo11n_noise_floor_baseline.tflite`) before any A/B test, and **only compare yolo26n models within the same back-to-back capture session**. Cross-session yolo26n comparisons are unreliable. 87 87 + 88 88 + ### 12. **60-second test clips are the minimum for stable measurements.** 89 89 + 90 90 + 10-second clips have a noise floor of ~±0.10 mean conf, which is too high to detect real model improvements (most candidate-vs-candidate differences are in the 0.03–0.15 range). 60-second clips drop the noise floor to ~±0.05 for stable models and ~±0.10 for the noise-prone yolo26n family. Bigger sample → tighter measurement. 91 91 + 92 92 + ### 13. **Run-to-run variance on the SAME model is real and significant.** 93 93 + 94 94 + The same `yolo11n_dataset_dataset.tflite` file measured between 0.597 and 0.753 mean conf across different sessions on the same test setup. Same file. Same video. Different absolute numbers because of phone positioning, lighting, video timing, thermal state, etc. 95 95 + 96 96 + **Within-session relative ordering is the only reliable signal.** Don't quote absolute numbers across sessions. 97 97 + 98 98 + ## Pipeline-side 99 99 + 100 100 + ### 14. **Mount Drive at the start of every Colab session, not the end.** 101 101 + 102 102 + `drive.mount('/content/drive')` may pop a "Connect to Google Drive" card requiring a click. If you defer this to the post-training sync cell, the user is unlikely to be at their keyboard 30 minutes later when training finishes — and the whole pipeline stalls. Mount Drive in the first ~5 cells, before triggering the long-running training, so any auth happens while the user is present. 103 103 + 104 104 + ### 15. **`google.colab.files.download()` is unreliable.** 105 105 + 106 106 + The JS download trigger gets silently blocked by browsers (popup blockers, unfocused Colab tab, etc.). The cell completes successfully and the file never arrives on the Mac — no error. **Always use Drive sync** for moving files Colab → Mac. `drive.mount` + `shutil.copy2` to `/content/drive/MyDrive/...` reliably mirrors to Drive Desktop within ~10 seconds. 107 107 + 108 108 + ### 16. **Colab cold-restart loses Python state but Drive mount auth is browser-cached.** 109 109 + 110 110 + If you switch the runtime accelerator (CPU ↔ GPU) or hit the idle timeout, the Python runtime resets and you have to re-run cells 2 (apt/pip), 3 (verify), 5 (config), 7/9/11 (dataset), 13 (training). But `drive.mount()` is cached by the browser session, so a re-mount on a cold runtime is silent — no second auth click required, as long as the same browser tab is still open. 111 111 + 112 112 + ### 17. **The auto-suffix gotcha when re-running ultralytics on the same dataset alias.** 113 113 + 114 114 + Running the training cell twice with the same `dataset_alias` produces run dirs `yolo26n_dataset_v11`, `yolo26n_dataset_v112`, `yolo26n_dataset_v113`, etc. Any cell that hardcodes the path will silently use the OLD weights. Always use a glob like `glob.glob('/content/runs/.../yolo26n_dataset_v11*')` sorted by mtime descending to pick the most recent. 115 115 + 116 116 + ## Architecture / model-choice-side 117 117 + 118 118 + ### 18. **yolo26n is a strict improvement over yolo11n on this task.** 119 119 + 120 120 + In the multi-arch comparison (V5.4), yolo26n at 416² beat yolo11n at 416² on both speed AND quality (within the same session). The newer architecture's improvements seem to actually deliver. **Default to yolo26n** for any new experiments unless you have a specific reason not to. 121 121 + 122 122 + ### 19. **yolo11s is too slow for this hardware tier.** 123 123 + 124 124 + yolo11s (the "small" variant of yolo11) has ~3-4× the parameters of yolo11n and on the A36 5G runs at ~2.5 Hz at imgsz=416. Quality wins are marginal vs yolo26n. **Don't use yolo11s here.** 125 125 + 126 126 + ### 20. **`imgsz=320` is too small for basketball detection on this dataset.** 127 127 + 128 128 + At 320² square + letterbox of a 16:9 source, the basketball ends up at ~6 pixels, near YOLO's small-object detection floor. mAP50 dropped from 0.76 (at 416) to 0.63 (at 320). Detection rate stayed at 100% but per-frame confidence dropped from ~0.82 to ~0.63. Use rectangular models or larger imgsz instead. 129 129 + 130 130 + ### 21. **Rectangular models always beat square models with letterbox at the same compute budget.** 131 131 + 132 132 + For a non-square camera source, a rect model at `(H, W)` has H*W pixels of effective content. A square model at imgsz=N has N*min(H,W)/max(H,W)*N pixels of effective content (the rest is gray padding waste). Same total inference cost, ~25% more useful pixels in the rect model for a 4:3 source. The R&D session's 60s on-device tests confirmed this is real. 133 133 + 134 134 + ## TL;DR for the next session 135 135 + 136 136 + 1. Use `yolo26n_v11_rect_512x384.tflite` as the default. 137 137 + 2. Pin the camera to 4:3, lock the activity to landscape. 138 138 + 3. The Android detector MUST do letterbox preprocessing + un-letterbox of output bboxes. 139 139 + 4. fp32. Don't bother with fp16. 140 140 + 5. Always run the noise-floor sanity check before any A/B test. 141 141 + 6. 60-second back-to-back captures, never 10s. 142 142 + 7. Within-session relative ordering is the only reliable signal.

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··· 1 1 + # Models — Detailed Comparison 2 2 + 3 3 + All measurements were taken on a **Samsung Galaxy A36 5G**, **TFLite GPU delegate**, running the test app filming a 4K (3840×2160) basketball video played on a Mac monitor at landscape 16:9, captured for 60 seconds at video offset 10s. Phone in landscape orientation, camera pinned to `RATIO_4_3`. Letterbox preprocessing in the Android detector. 4 4 + 5 5 + **Run-to-run noise floor on this test setup**: ~±0.05 mean confidence on stable models like `yolo11n_dataset_dataset`. The yolo26n family is more sensitive (~±0.10) — small changes in phone position can shift its measured confidence noticeably without affecting framerate or detection rate. Run a noise check (capture `yolo11n_noise_floor_baseline.tflite` first; expect ~0.72 mean conf) before any A/B test. 6 6 + 7 7 + ## All 6 candidates at a glance 8 8 + 9 9 + | File | Arch | Input shape | Pixels | **Hz** | **Mean conf range** | val mAP50 | Size | Use case | 10 10 + |---|---|---|---:|---:|---:|---:|---:|---| 11 11 + | **`yolo26n_v11_rect_512x384.tflite`** ⭐ | yolo26n | `[1, 384, 512, 3]` | 197k | **6.7** | **0.78–0.83** | 0.7632 | 9.3 MB | **Recommended production default** | 12 12 + | `yolo26n_v11_rect_384x288.tflite` | yolo26n | `[1, 288, 384, 3]` | 110k | **10.5** | 0.59–0.73 | 0.6456 | 9.3 MB | Real-time tracking, >10 Hz needed | 13 13 + | `yolo26n_v11_square_512.tflite` | yolo26n | `[1, 512, 512, 3]` | 262k | 5.0 | 0.78–0.83 | 0.8206 | 9.3 MB | Quality-first, square inputs OK | 14 14 + | `yolo26n_v11_square_416.tflite` | yolo26n | `[1, 416, 416, 3]` | 173k | 7.5 | 0.64–0.84 | 0.7649 | 9.3 MB | Square baseline reference | 15 15 + | `yolo11n_dataset640.tflite` | yolo11n | `[1, 640, 640, 3]` | 410k | 3.2 | 0.81–0.85 | 0.881 | 10.7 MB | Historical conf champion ⚠️ caveats | 16 16 + | `yolo11n_noise_floor_baseline.tflite` | yolo11n | `[1, 416, 416, 3]` | 173k | 6.9 | 0.72 (stable) | — | 10.6 MB | **Sanity check / noise floor reference** | 17 17 + 18 18 + ⭐ = recommended default. ⚠️ = see caveats below. 19 19 + 20 20 + ## Tier 1 — Recommended Production Default 21 21 + 22 22 + ### `yolo26n_v11_rect_512x384.tflite` 23 23 + 24 24 + The clear winner of the R&D session. Trained at `imgsz=512` with `rect=True`, which produces 384×512 rectangular tensors per batch (the dataset's natural aspect ratio is 4:3). The training and inference tensor shapes match exactly, eliminating the train/inference distribution shift that hurts square-trained models on rectangular cameras. 25 25 + 26 26 + **Strengths:** 27 27 + - Best on-device confidence among the 6.7-Hz speed class. 28 28 + - 14% more pixels than the square 416² baseline at only 13% slower speed — close to a strict Pareto improvement. 29 29 + - Natural 4:3 alignment with the camera. No wasted padding pixels at inference. 30 30 + - mAP50 0.7632 essentially matches the square 416² baseline (0.7649) on the val set, but **significantly outperforms it on the on-device test** (+0.07 to +0.15 mean conf in the same session). The val/on-device gap is the rect=True benefit — see `LESSONS_LEARNED.md`. 31 31 + 32 32 + **Trade-offs:** 33 33 + - `rect=True` auto-disables mosaic augmentation during training. The val mAP took a small hit, but on-device beats every prior square-trained variant in within-session comparisons. 34 34 + - Locked to landscape 4:3. The kima app must pin CameraX accordingly. 35 35 + 36 36 + ## Tier 2 — Speed-Critical (10+ Hz) 37 37 + 38 38 + ### `yolo26n_v11_rect_384x288.tflite` 39 39 + 40 40 + Same training method as the Tier 1 model but at `imgsz=384` rect=True → 288×384 tensors. Smaller pixel budget = faster, less accurate. 41 41 + 42 42 + **Strengths:** 43 43 + - Cleared the **10 Hz target** in measurement — the only model in the lineup besides the deprecated 320² square that does so. 44 44 + - Same `rect=True` training benefit — the rectangular geometry alignment translates into much higher confidence than the comparable 320² square model (which dropped to ~0.59 even though it has a similar pixel count). 45 45 + 46 46 + **Trade-offs:** 47 47 + - Quality drops noticeably vs the 512×384 rect: mean conf ~0.59–0.73 (depending on session noise), which is OK for a real-time tracker that smooths per-frame jitter but not great for one-shot frame analysis. 48 48 + - Same 4:3 landscape requirement. 49 49 + 50 50 + **When to use it:** if the downstream consumer is a Kalman tracker / smoothing filter on basketball position that benefits from 10+ Hz position updates and can tolerate per-frame noise. 51 51 + 52 52 + ## Tier 3 — Quality-First (slower) 53 53 + 54 54 + ### `yolo26n_v11_square_512.tflite` 55 55 + 56 56 + Square 512², trained without `rect=True`. Slower than the rect 512×384 (5 Hz vs 6.7 Hz) but slightly higher val mAP (0.8206 vs 0.7632) and similar on-device confidence. The extra pixels go to padding the 4:3 source instead of effective content, so the on-device quality benefit is minor — but it's a known-good square model if landscape lock isn't acceptable. 57 57 + 58 58 + **When to use it:** if the kima app cannot pin orientation to landscape (e.g., app design needs portrait + landscape support) and you need the highest quality among square models. 59 59 + 60 60 + ### `yolo11n_dataset640.tflite` ⚠️ 61 61 + 62 62 + Trained way back on the **stretched** Roboflow dataset v8 (before we discovered Roboflow's "Resize: Stretch to" preprocessing was destroying aspect ratios — see `LESSONS_LEARNED.md`). Has the **highest measured on-device mean confidence** of the entire R&D session (~0.84) but with **known box-shape artifacts** because the model learned distorted geometry. 63 63 + 64 64 + **Use only as a quality reference** to establish what "best possible accuracy on this clip" looks like. Do not ship. 65 65 + 66 66 + ## Reference baselines 67 67 + 68 68 + ### `yolo26n_v11_square_416.tflite` 69 69 + 70 70 + Square 416² yolo26n trained on the v11 letterboxed dataset without `rect=True`. The historical "best balance" champion before the rect=True experiments. Similar pixel count (173k) to the new rect 512×384 (197k, +14%). **This is the right A/B comparison target** when validating that the rect=True model wins in the kima app's specific test environment. 71 71 + 72 72 + ### `yolo11n_noise_floor_baseline.tflite` 73 73 + 74 74 + This is `yolo11n_dataset_dataset.tflite` from the original repo — a stable, oft-tested model whose measured mean confidence stays consistently in the 0.71–0.76 range across many sessions on this test setup. **Use it as a sanity check before any A/B test**: capture this model on the test video, compare against the historical ~0.72 baseline, and if it's >0.05 off, the test setup has drifted (phone position, lighting, framing) — fix that before judging other models. 75 75 + 76 76 + ## Cross-model summary table 77 77 + 78 78 + For quick decision-making: 79 79 + 80 80 + | Use case | Model | Hz | Mean conf | Notes | 81 81 + |---|---|---:|---:|---| 82 82 + | **Default** | `yolo26n_v11_rect_512x384.tflite` | 6.7 | ~0.80 | Use this unless you have a specific reason not to | 83 83 + | Real-time tracking @ 10+ Hz | `yolo26n_v11_rect_384x288.tflite` | 10.5 | ~0.65 | Tracker absorbs the per-frame noise | 84 84 + | Highest one-shot accuracy (rect-aspect lock OK) | `yolo26n_v11_square_512.tflite` | 5.0 | ~0.81 | Slower, marginal quality win | 85 85 + | Highest one-shot accuracy (no aspect lock) | `yolo26n_v11_square_416.tflite` | 7.5 | ~0.78 | Square, no orientation constraints | 86 86 + | A/B test sanity check | `yolo11n_noise_floor_baseline.tflite` | 6.9 | 0.72 | Run first, verify ~0.72 before testing other models | 87 87 + 88 88 + ## Output schema (all models) 89 89 + 90 90 + All six models share the same output tensor schema, since they all came from the ultralytics export pipeline with `nms=True` (which is auto-handled for end2end yolo26 models): 91 91 + 92 92 + - Shape: `[1, 300, 6]` 93 93 + - Per-row format: `[x1, y1, x2, y2, conf, cls]` where: 94 94 + - `x1, y1` = top-left corner, normalized `[0, 1]` over the model's input tensor 95 95 + - `x2, y2` = bottom-right corner, same normalization 96 96 + - `conf` = confidence score in `[0, 1]` 97 97 + - `cls` = float class index (0.0 = basketball, 1.0 = basketball_hoop) 98 98 + - Up to 300 detections per frame, sorted by confidence descending. Trailing rows have `conf = 0`. 99 99 + - **No additional NMS needed in the Android code** — it's already baked into the graph. 100 100 + 101 101 + ## Class labels 102 102 + 103 103 + ``` 104 104 + 0 basketball 105 105 + 1 basketball_hoop 106 106 + ```

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handover/README.md

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··· 1 1 + # Basketball Object Detection — Production Handover 2 2 + 3 3 + This folder is the result of a multi-day R&D session optimizing YOLO-family models for basketball + basketball_hoop detection on Android (Samsung Galaxy A36 5G as reference device). It contains the production-ready tflite models, the Android-side requirements for consuming them, and a protocol for verifying quality in the kima app before shipping. 4 4 + 5 5 + ## ⚠️ Library version: bump to `posedetection-compose:4.11.0` 6 6 + 7 7 + The R&D work shipped a new **`posedetection-compose:4.11.0`** library release containing the letterbox-aware detector + camera config + delegate logging that the rect models depend on. The kima app currently uses an older version (probably 4.10.0). **Bump the dependency before integrating any of these models.** 8 8 + 9 9 + The new version is available in two places: 10 10 + 11 11 + ### Option A — Maven Local (immediate, no remote publish needed) 12 12 + 13 13 + The library is already published to maven local on the dev machine: 14 14 + `~/.m2/repository/com/performancecoachlab/posedetection/posedetection-compose/4.11.0/` 15 15 + 16 16 + To pull it from kima, the kima app's root `build.gradle.kts` (or `settings.gradle.kts`'s `dependencyResolutionManagement`) must include `mavenLocal()` in its repositories list: 17 17 + 18 18 + ```kotlin 19 19 + repositories { 20 20 + mavenLocal() // ← add this line if not already present 21 21 + google() 22 22 + mavenCentral() 23 23 + } 24 24 + ``` 25 25 + 26 26 + Then bump the dependency in whichever module consumes posedetection (typically the kima Android app's `build.gradle.kts`): 27 27 + 28 28 + ```kotlin 29 29 + implementation("com.performancecoachlab.posedetection:posedetection-compose:4.11.0") 30 30 + ``` 31 31 + 32 32 + This will resolve from maven local without any network calls. 33 33 + 34 34 + ### Option B — Remote (after publishing) 35 35 + 36 36 + The 4.11.0 library code lives on the **`release-4.11.0`** branch in the PoseDetection repo. Once it's merged + published to maven central via the existing `vanniktech.maven.publish` flow, kima can drop the `mavenLocal()` line and just consume it from central. Until then, use Option A. 37 37 + 38 38 + ### What's actually in 4.11.0 39 39 + 40 40 + The full commit message on `release-4.11.0` lists everything. The headline: 41 41 + - **`ImageDetector.android.kt`** (NEW class) — letterbox-aware detector with full un-letterbox of output bboxes. The detector all the rect models in `models/` were tested against. 42 42 + - **`CameraView.android.kt`** — pins `ImageAnalysis` to `AspectRatio.RATIO_4_3` so the camera frame distribution matches the rect models' expected geometry. 43 43 + - **`CustomObjectModel.android.kt`** — explicit GPU/NNAPI/CPU delegate logging at INFO level. Surfaces silent CPU fallbacks via `adb logcat -s TFLite:I`. 44 44 + - **No breaking API changes** — only additive. Existing kima usage of the library should keep working unchanged after the version bump. 45 45 + 46 46 + ## TL;DR — Use this model 47 47 + 48 48 + **`models/yolo26n_v11_rect_512x384.tflite`** is the recommended production default. 49 49 + 50 50 + | Property | Value | 51 51 + |---|---| 52 52 + | Architecture | YOLO26n (2026 generation, ~5.2 GFLOPs, ~2.4M params after fusion) | 53 53 + | Training | 50 epochs, AdamW, `imgsz=512`, `rect=True`, Roboflow `dataset_v11` (basketball court footage, letterboxed preprocessing) | 54 54 + | Input tensor | `[1, 384, 512, 3]` float32 (NHWC), pixel values normalized to `[0, 1]` | 55 55 + | Output tensor | `[1, 300, 6]` float32 = `[x1, y1, x2, y2, conf, cls]` per row, **NMS already applied**, coords normalized to `[0, 1]` over the input tensor | 56 56 + | Classes | `0 = basketball`, `1 = basketball_hoop` | 57 57 + | File size | 9.3 MB (fp32) | 58 58 + | Measured speed | ~6.7 Hz on the A36 5G via TFLite GPU delegate | 59 59 + | Measured quality | mean confidence ~0.78–0.83 across the 60s basketball test clip | 60 60 + | val mAP50 | 0.7632 | 61 61 + 62 62 + This model is **rectangular** (4:3 landscape) and was trained with `rect=True`, which means it learned features tuned to a 384×512 tensor. It expects the camera to deliver landscape 4:3 frames, which the kima app must enforce — see `ANDROID_INTEGRATION.md`. 63 63 + 64 64 + ## Why fp32 not fp16 65 65 + 66 66 + Tested both. fp16 saves ~50% file size but on the A36 5G's TFLite GPU delegate they ran at the same speed and fp16 cost ~0.02 mean confidence. Since 4 MB of model file is negligible vs the production app's overall size, **stick with fp32** — same speed, better quality. 67 67 + 68 68 + ## What's in this folder 69 69 + 70 70 + ``` 71 71 + handover/ 72 72 + ├── README.md ← you are here 73 73 + ├── MODELS.md ← detailed comparison of all 6 candidates 74 74 + ├── ANDROID_INTEGRATION.md ← what the kima app's Android side needs 75 75 + ├── TESTING_PROTOCOL.md ← how to A/B test models in the kima app 76 76 + ├── LESSONS_LEARNED.md ← key insights from the R&D session 77 77 + ├── models/ 78 78 + │ ├── yolo26n_v11_rect_512x384.tflite ← RECOMMENDED PRODUCTION DEFAULT (rect=True) 79 79 + │ ├── yolo26n_v11_rect_384x288.tflite ← speed-critical alternative (>10 Hz) 80 80 + │ ├── yolo26n_v11_square_512.tflite ← square 512², slightly higher quality at lower speed 81 81 + │ ├── yolo26n_v11_square_416.tflite ← square 416² baseline reference 82 82 + │ ├── yolo11n_dataset640.tflite ← historical quality ceiling, KNOWN LOOSE BBOXES 83 83 + │ └── yolo11n_noise_floor_baseline.tflite ← stable model for noise-floor sanity checks 84 84 + └── reference_code/ 85 85 + ├── ImageDetector.android.kt ← working letterbox-aware detector 86 86 + ├── CustomObjectModel.android.kt ← TFLite interpreter setup with delegate logging 87 87 + ├── CameraView_snippet.kt ← CameraX 4:3 + landscape pin 88 88 + ├── AndroidManifest_snippet.xml ← orientation lock 89 89 + └── compare_logs.py ← portable Python report generator for A/B tests 90 90 + ``` 91 91 + 92 92 + ## Quick start 93 93 + 94 94 + 1. **Read `MODELS.md`** to understand the candidates and pick a starting model. The recommended default is `yolo26n_v11_rect_512x384.tflite`. 95 95 + 2. **Read `ANDROID_INTEGRATION.md`** to see what camera config and detector code the kima app needs. **Critical**: the model expects a 4:3 landscape camera frame; the kima app must pin CameraX accordingly. 96 96 + 3. **Drop the chosen tflite** into the kima app's assets folder. 97 97 + 4. **Implement the camera + detector** per `ANDROID_INTEGRATION.md`. The reference code in `reference_code/` is the working version from this project. 98 98 + 5. **Verify with `TESTING_PROTOCOL.md`** before shipping. Even a single 60-second comparison run against a known video can catch integration mistakes early. 99 99 + 100 100 + ## Source notes 101 101 + 102 102 + - All models are trained on the `kima-rbjnn/dataset-3k5b6` Roboflow project, version 11 (preprocessing = "Fit (black padding)"; the v8 stretched dataset was the source of weeks of accuracy issues — **never train on stretched data again**, see `LESSONS_LEARNED.md`). 103 103 + - The pipeline is closed-loop: edit `collab/Model Training.ipynb` cell 5, run training in Colab, drive-sync the tflite to the Mac, sync to assets via `tools/sync_models.py`, build, install, run a 60s back-to-back comparison via `tools/run_auto_experiment.sh`, generate the report via `tools/compare_logs.py`. The full source pipeline lives in the parent project repo. 104 104 + - **iOS is not covered here.** This handover is Android-only. iOS uses CoreML exports of the same `.pt` weights, and the user has confirmed the iOS pipeline already works at 10+ Hz with high accuracy because CoreML bakes preprocessing into the model graph. If kima ships iOS too, the iOS side likely doesn't need any of this — just the same Roboflow dataset versions and a separate CoreML export.

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··· 1 1 + # Testing Protocol — Comparing Models in the Kima App 2 2 + 3 3 + This document describes how to validate that a model behaves correctly in the kima app and how to A/B test different candidates against each other. The protocol mirrors what worked in the R&D project (60s back-to-back captures of the same video segment) but is portable to any Android app that can load multiple TFLite models. 4 4 + 5 5 + ## Setup requirements 6 6 + 7 7 + ### Hardware 8 8 + - **A test phone** that runs the kima app (the R&D used a Samsung Galaxy A36 5G — different phones will give different absolute Hz numbers but the relative ordering of models should be similar). 9 9 + - **A mounted phone holder / tripod** that does not move between captures. This is the single most important variable for getting reproducible results. 10 10 + - **A monitor or large screen** for playing the test video. The phone films the screen. 11 11 + 12 12 + ### Test video 13 13 + - **Use the same video for all comparisons.** The R&D project used `~/Downloads/20250901_100828.mp4` (a 4K, 165s, 16:9 basketball court clip with shooting). Any consistent video with visible basketballs and hoops works — pick one once and keep using it. 14 14 + - **Use a fixed segment** (e.g. seconds 10–70 = 60s of video). Don't sample randomly; the same frames need to flow into every model under test. 15 15 + - Play the video **fullscreen** in QuickTime or whatever player. This avoids window-chrome interference. 16 16 + 17 17 + ### Android side 18 18 + - Camera pinned to `RATIO_4_3`, activity orientation locked to landscape — see `ANDROID_INTEGRATION.md`. 19 19 + - The kima app needs a way to **switch between models at runtime** (a debug picker, an intent extra, or just rebuilding with a different default model in assets). 20 20 + - The kima app needs a way to **log detector output to a file or logcat** so you can analyze it after the run. Minimum schema below. 21 21 + 22 22 + ## Minimum logging schema 23 23 + 24 24 + For each model run, capture per-frame events with the following structure (this is what `compare_logs.py` consumes): 25 25 + 26 26 + ```json 27 27 + { 28 28 + "device_model": "samsung SM-A366B", 29 29 + "model_name": "yolo26n_v11_rect_512x384", 30 30 + "run_id": "1775898858800_yolo26n_v11_rect_512x384", 31 31 + "started_wall_ms": 1775898858801, 32 32 + "stopped_wall_ms": 1775898918803, 33 33 + "events": [ 34 34 + { 35 35 + "wall_ms": 1775898858900, 36 36 + "skeleton": null, 37 37 + "objects": [ 38 38 + { 39 39 + "label": "basketball", 40 40 + "confidence": 0.872, 41 41 + "bbox": { "left": 412.5, "top": 230.1, "right": 488.2, "bottom": 308.9 }, 42 42 + "frame_size": { "width": 1280, "height": 960 } 43 43 + } 44 44 + ] 45 45 + } 46 46 + ] 47 47 + } 48 48 + ``` 49 49 + 50 50 + Key fields: 51 51 + - `wall_ms` — absolute wall-clock timestamp of each detector emission. Used to compute Hz and to align events across runs. 52 52 + - `objects[]` — list of detections per frame. Empty list = the detector ran but found nothing. 53 53 + - `confidence` — the conf score from the detector output (0–1). 54 54 + - `bbox.left/top/right/bottom` — pixel coords in the original frame's coordinate space (post un-letterbox). 55 55 + 56 56 + The R&D project's reference implementation lives in: 57 57 + - `posedetection/src/commonMain/kotlin/com/nate/posedetection/ExperimentLogger.kt` — the logger 58 58 + - `posedetection/src/commonMain/kotlin/com/nate/posedetection/ExperimentAuto.kt` — the auto-mode driver 59 59 + 60 60 + The kima app can implement its own equivalent in any language/framework. The schema is the contract. 61 61 + 62 62 + ## The protocol 63 63 + 64 64 + ### Step 0 — Position the phone 65 65 + 66 66 + Mount the phone in landscape, aimed at the monitor showing the test video. **Don't move it between captures.** Even small bumps shift the framing and can swing yolo26n confidence by 0.10+. 67 67 + 68 68 + ### Step 1 — Noise-floor sanity check 69 69 + 70 70 + Before testing the model you actually care about, capture a known-stable model and verify it lands at the expected confidence. The handover bundle includes `yolo11n_noise_floor_baseline.tflite` for exactly this purpose. 71 71 + 72 72 + ``` 73 73 + Run: yolo11n_noise_floor_baseline.tflite for 60s 74 74 + Expected mean confidence: ~0.72 (range 0.71–0.76 across many sessions) 75 75 + ``` 76 76 + 77 77 + If this lands more than 0.05 off the expected value, **reposition the phone** before continuing. The lighting, phone framing, or video timing has drifted and any subsequent A/B test will be measuring noise instead of model quality. 78 78 + 79 79 + ### Step 2 — Capture the actual A/B set 80 80 + 81 81 + Run **60-second back-to-back captures** of each model under test, against the same video segment, **without moving anything** between captures. The R&D project used this sequence as the standard 5-way: 82 82 + 83 83 + 1. `yolo11n_noise_floor_baseline.tflite` (sanity) 84 84 + 2. The model you're considering shipping (e.g. `yolo26n_v11_rect_512x384.tflite`) 85 85 + 3. A direct competitor (e.g. `yolo26n_v11_square_416.tflite` for the rect-vs-square comparison) 86 86 + 4. A speed alternative (e.g. `yolo26n_v11_rect_384x288.tflite`) 87 87 + 5. A quality reference (e.g. `yolo26n_v11_square_512.tflite` or `yolo11n_dataset640.tflite`) 88 88 + 89 89 + 60 seconds is the minimum for stable measurements. 10s clips have a noise floor of ~±0.10 mean conf which is too high to detect real differences. 60s clips drop the noise floor to ~±0.05 (still significant for yolo26n, see below). 90 90 + 91 91 + ### Step 3 — Run the report generator 92 92 + 93 93 + Drop the captured JSON files into a folder (one subfolder per run, with the JSON inside) and run `reference_code/compare_logs.py`: 94 94 + 95 95 + ```bash 96 96 + python3 compare_logs.py /path/to/captures/ -o /tmp/reports --bucket 250 97 97 + open /tmp/reports/report.html 98 98 + ``` 99 99 + 100 100 + The HTML report shows side-by-side scorecards (events, det rate, mean conf, mean bbox area, per-class), per-bucket histograms, and per-class timelines. Look at: 101 101 + - **Mean confidence** — the headline quality number 102 102 + - **Detection rate** — should be 100% on a video with always-visible objects 103 103 + - **Mean bbox area** — proxy for box tightness; lower = tighter (usually better) 104 104 + - **Per-class timelines** — visualize where each model loses detections 105 105 + 106 106 + ### Step 4 — Interpret with the noise floor in mind 107 107 + 108 108 + **The yolo26n family is noticeably more sensitive to small framing changes than yolo11n.** Across multiple R&D sessions, the same `yolo26n_v11_square_416.tflite` measured between **0.64 and 0.84 mean confidence** depending on how the phone was positioned. The yolo11n baseline barely budged across the same conditions. 109 109 + 110 110 + What this means: 111 111 + - **Within-session comparisons are reliable.** All models in the same back-to-back capture sequence saw the same framing, so their relative ordering is meaningful. If model A scores 0.80 and model B scores 0.70 in the same session, A is genuinely better. 112 112 + - **Cross-session comparisons are not reliable** unless you re-validate via the noise-floor model. Don't quote V5.x results from this project's history as direct benchmarks for the kima app's measurements. 113 113 + 114 114 + If your A/B test gives results that surprise you, run Step 1 again — the phone might have shifted between captures. 115 115 + 116 116 + ## Recommended A/B sequence for kima 117 117 + 118 118 + When you start integrating models, run this sequence in order. Each step should yield a clear yes/no decision before proceeding. 119 119 + 120 120 + ### A. "Does the model load and run?" 121 121 + - Build kima with `yolo26n_v11_rect_512x384.tflite` in assets. 122 122 + - Cold-start the app, verify logcat shows `delegate=GPU inputShape=[1, 384, 512, 3]`. 123 123 + - Aim the camera at any basketball-containing scene and verify detections appear. 124 124 + - **Decision**: if yes, proceed. If no, debug per `ANDROID_INTEGRATION.md` "Common gotchas". 125 125 + 126 126 + ### B. "Is the geometry right?" 127 127 + - Run a 30s capture against the test video. 128 128 + - In the report, check **mean bbox area**. For `yolo26n_v11_rect_512x384` it should be in the 480–560 range (loose box) on the standard test video. 129 129 + - Visually check 5–10 frames in the per-class image gallery (if the kima app supports this) — the boxes should hug the basketball, not pad around it. 130 130 + - **Decision**: if boxes look correctly placed, the letterbox preprocessing is right. If they're consistently offset or distorted, re-check the un-letterbox math. 131 131 + 132 132 + ### C. "Is the speed acceptable?" 133 133 + - 60s capture, look at events/sec (Hz). 134 134 + - Expect ~5–7 Hz for `yolo26n_v11_rect_512x384` on a modern phone. Anything below 3 Hz means the GPU delegate didn't engage. 135 135 + - **Decision**: if Hz is in the expected range, proceed. If much slower, check delegate selection in logcat. 136 136 + 137 137 + ### D. "Is it production quality?" 138 138 + - Run the noise-floor sanity check (`yolo11n_noise_floor_baseline.tflite` should hit ~0.72). 139 139 + - Run the A/B comparison: `yolo26n_v11_rect_512x384` vs `yolo26n_v11_square_416`. 140 140 + - Expect the rect_512x384 to win on confidence by ~0.05–0.15 in within-session comparison. 141 141 + - **Decision**: if the rect model wins, ship it. If not, the kima camera/orientation config probably isn't matching the model's expected aspect ratio — re-check `ANDROID_INTEGRATION.md` step 1 + 2. 142 142 + 143 143 + ### E. "Does it work in real-world conditions?" 144 144 + - Beyond the test video, capture 5–10 minutes of real basketball footage with the kima app and the chosen model. 145 145 + - Look for consistent detections across distance, angle, lighting changes. 146 146 + - This is the only step that the test video can't catch — the dataset was trained on indoor court footage similar to but not identical to whatever real users will film. 147 147 + 148 148 + ## What NOT to test against 149 149 + 150 150 + - **Don't test fp16 vs fp32 separately.** They give the same speed and ~0.02 conf difference on this hardware. Settled — use fp32, file size doesn't matter at this scale. 151 151 + - **Don't run val mAP comparisons** as the deciding metric. The val set is held-out from Roboflow and doesn't reflect on-device performance well — see `LESSONS_LEARNED.md`. 152 152 + - **Don't compare against the V5.x historical numbers in this handover.** They were measured on a different phone setup and the within-session noise is real. The history is useful for understanding the *direction* of each tuning lever, not the absolute numbers in the kima app's measurement environment. 153 153 + 154 154 + ## If you only have time for one test 155 155 + 156 156 + Run the A/B at step **D**: capture `yolo11n_noise_floor_baseline`, `yolo26n_v11_rect_512x384`, and `yolo26n_v11_square_416` back-to-back for 60 seconds each on the same test video. If the rect model wins on confidence and the noise floor is on target, ship the rect model. If the noise floor is off, fix the phone setup first and re-run.

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··· 1 1 + <!-- AndroidManifest snippet — the activity-level orientation lock that the kima app 2 2 + must include if it ships the rect 4:3 model and wants the simplest integration. 3 3 + 4 4 + Source: sample/composeApp/src/androidMain/AndroidManifest.xml in the R&D project. 5 5 + 6 6 + The single critical line is `android:screenOrientation="landscape"` on whatever 7 7 + activity hosts the camera + detector. Without this, the camera frame arrives 8 8 + portrait-rotated and the detector sees a 3:4 input instead of 4:3, causing 9 9 + letterbox padding to fight the model. 10 10 + 11 11 + If the kima app needs to support both portrait AND landscape, see the 12 12 + "Option B" rotation fallback in ANDROID_INTEGRATION.md. 13 13 + --> 14 14 + 15 15 + <manifest xmlns:android="http://schemas.android.com/apk/res/android"> 16 16 + 17 17 + <uses-feature 18 18 + android:name="android.hardware.camera" 19 19 + android:required="false" /> 20 20 + <uses-permission android:name="android.permission.CAMERA" /> 21 21 + 22 22 + <application 23 23 + android:icon="@mipmap/ic_launcher" 24 24 + android:label="kima" 25 25 + android:theme="@android:style/Theme.Material.NoActionBar"> 26 26 + 27 27 + <activity 28 28 + android:name=".MainActivity" 29 29 + android:configChanges="orientation|screenSize|screenLayout|keyboardHidden" 30 30 + android:exported="true" 31 31 + android:launchMode="singleInstance" 32 32 + android:screenOrientation="landscape" 33 33 + android:windowSoftInputMode="adjustPan"> 34 34 + <intent-filter> 35 35 + <action android:name="android.intent.action.MAIN" /> 36 36 + <category android:name="android.intent.category.LAUNCHER" /> 37 37 + </intent-filter> 38 38 + </activity> 39 39 + 40 40 + <!-- ... other providers, services, etc ... --> 41 41 + 42 42 + </application> 43 43 + 44 44 + </manifest>

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handover/reference_code/CameraView_snippet.kt

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··· 1 1 + // CameraView snippet — the parts of the CameraX setup that the kima app must include 2 2 + // to consume the rect 4:3 model. Adapt the surrounding code to whatever the kima app's 3 3 + // camera setup looks like. 4 4 + // 5 5 + // Source: posedetection/src/androidMain/kotlin/com/performancecoachlab/posedetection/camera/CameraView.android.kt 6 6 + // (Lines 286-289 in the R&D project, plus the AspectRatio import) 7 7 + 8 8 + import androidx.camera.core.AspectRatio // ← REQUIRED for the new line 9 9 + import androidx.camera.core.ImageAnalysis 10 10 + 11 11 + // ... inside whatever Composable / Activity sets up the CameraX use cases ... 12 12 + 13 13 + val imageAnalysis = ImageAnalysis.Builder() 14 14 + .setBackpressureStrategy(ImageAnalysis.STRATEGY_KEEP_ONLY_LATEST) 15 15 + .setOutputImageFormat(ImageAnalysis.OUTPUT_IMAGE_FORMAT_RGBA_8888) 16 16 + .setTargetAspectRatio(AspectRatio.RATIO_4_3) // ← THE CRITICAL ADDITION 17 17 + .build() 18 18 + .also { analysis -> 19 19 + analysis.targetRotation = currentRotation.intValue 20 20 + analysis.setAnalyzer(executor) { imageProxy -> 21 21 + // ... process imageProxy → bitmap → detector ... 22 22 + } 23 23 + } 24 24 + 25 25 + // Notes: 26 26 + // 27 27 + // - `setTargetAspectRatio` is a hint, not a hard guarantee. CameraX picks the closest 28 28 + // available 4:3 mode for the device — typically 1280×960, 1440×1080, or 2048×1536. 29 29 + // Different devices = different exact resolutions but always 4:3 aspect. 30 30 + // 31 31 + // - If a device truly cannot deliver 4:3, CameraX falls back to its default. The 32 32 + // detector's letterbox math handles aspect mismatches gracefully, so this is at 33 33 + // worst a "wastes pixels on padding" case, not a crash. 34 34 + // 35 35 + // - The newer CameraX way to do the same thing is `ResolutionSelector.Builder() 36 36 + // .setAspectRatioStrategy(AspectRatioStrategy.RATIO_4_3_FALLBACK_AUTO_STRATEGY) 37 37 + // .build()`. Either works. Use whichever matches your CameraX version. 38 38 + // 39 39 + // - DO NOT use `setTargetResolution(Size(W, H))` — it's deprecated and the device 40 40 + // may not honor exact pixel dimensions. The aspect ratio is what matters.

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handover/reference_code/CustomObjectModel.android.kt

Reviewed

··· 1 1 + package com.performancecoachlab.posedetection.custom 2 2 + 3 3 + import androidx.compose.runtime.Composable 4 4 + import androidx.compose.ui.platform.LocalContext 5 5 + import co.touchlab.kermit.Logger 6 6 + import org.json.JSONObject 7 7 + import android.os.Build 8 8 + import org.tensorflow.lite.Interpreter 9 9 + import org.tensorflow.lite.gpu.GpuDelegate 10 10 + import org.tensorflow.lite.support.common.FileUtil 11 11 + import org.tensorflow.lite.support.metadata.MetadataExtractor 12 12 + import java.io.ByteArrayInputStream 13 13 + import java.nio.MappedByteBuffer 14 14 + import java.nio.charset.StandardCharsets 15 15 + import java.util.zip.GZIPInputStream 16 16 + import java.util.zip.Inflater 17 17 + import java.util.zip.InflaterInputStream 18 18 + import java.util.zip.ZipInputStream 19 19 + 20 20 + @Composable 21 21 + actual fun initialiseObjectModel(modelPath: ModelPath): ObjectModel { 22 22 + if (modelPath.androidModelPath == null) { 23 23 + throw IllegalArgumentException("Android model path cannot be null") 24 24 + } 25 25 + // Prefer GPU, then NNAPI (API 27+), then CPU. `selectedDelegate` tracks 26 26 + // which one actually ends up in the final interpreter so we can log it. 27 27 + var selectedDelegate = "CPU" 28 28 + val (options, gpuDelegate) = runCatching { 29 29 + val delegate = GpuDelegate() 30 30 + val opts = Interpreter.Options().apply { 31 31 + addDelegate(delegate) 32 32 + setNumThreads(2) 33 33 + } 34 34 + selectedDelegate = "GPU" 35 35 + Logger.i { "TFLite: GPU delegate constructed" } 36 36 + opts to delegate 37 37 + }.onFailure { t -> 38 38 + Logger.w(t) { "TFLite: GPU delegate not available; trying NNAPI" } 39 39 + }.getOrElse { 40 40 + if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.O_MR1) { 41 41 + runCatching { 42 42 + val nnapiDelegate = org.tensorflow.lite.nnapi.NnApiDelegate() 43 43 + val opts = Interpreter.Options().apply { 44 44 + addDelegate(nnapiDelegate) 45 45 + setNumThreads(2) 46 46 + } 47 47 + selectedDelegate = "NNAPI" 48 48 + Logger.i { "TFLite: NNAPI delegate constructed" } 49 49 + opts to null 50 50 + }.onFailure { t -> 51 51 + Logger.w(t) { "TFLite: NNAPI delegate not available; falling back to CPU" } 52 52 + }.getOrElse { 53 53 + selectedDelegate = "CPU" 54 54 + Interpreter.Options().apply { setNumThreads(4) } to null 55 55 + } 56 56 + } else { 57 57 + selectedDelegate = "CPU" 58 58 + Interpreter.Options().apply { setNumThreads(4) } to null 59 59 + } 60 60 + } 61 61 + 62 62 + val model = FileUtil.loadMappedFile(LocalContext.current, modelPath.androidModelPath) 63 63 + val labels = labels(model) 64 64 + 65 65 + val interpreter = runCatching { 66 66 + Interpreter(model, options) 67 67 + }.onFailure { t -> 68 68 + // If the chosen delegate can't actually build an interpreter (common 69 69 + // for GPU on models with unsupported ops), fall back to pure CPU. 70 70 + Logger.w(t) { "TFLite: failed to create interpreter with $selectedDelegate delegate; retrying on CPU" } 71 71 + gpuDelegate?.close() 72 72 + selectedDelegate = "CPU" 73 73 + }.getOrElse { 74 74 + val cpuOptions = Interpreter.Options().apply { setNumThreads(4) } 75 75 + Interpreter(model, cpuOptions) 76 76 + } 77 77 + 78 78 + val inputShape = interpreter.getInputTensor(0)?.shape() 79 79 + val outputShape = interpreter.getOutputTensor(0)?.shape() 80 80 + Logger.i { 81 81 + "TFLite: model='${modelPath.androidModelPath}' delegate=$selectedDelegate " + 82 82 + "inputShape=${inputShape?.toList()} outputShape=${outputShape?.toList()}" 83 83 + } 84 84 + val modelInfo = ModelInfo.fromShapes( 85 85 + inputShape = inputShape 86 86 + ?: throw IllegalArgumentException("Invalid model: input shape is null"), 87 87 + outputShape = outputShape 88 88 + ?: throw IllegalArgumentException("Invalid model: output shape is null"), 89 89 + labels, 90 90 + ) 91 91 + val androidDetector = AndroidDetector( 92 92 + interpreter = interpreter, modelInfo = modelInfo 93 93 + ) 94 94 + return ObjectModel(androidDetector) 95 95 + } 96 96 + 97 97 + fun labels(model: MappedByteBuffer): List<String> { 98 98 + return runCatching { 99 99 + val extractor = MetadataExtractor(model) 100 100 + val files = extractor.associatedFileNames.orEmpty() 101 101 + if (files.isEmpty()) return@runCatching emptyList() 102 102 + 103 103 + // Try every associated file. Pick the first that decodes to JSON with a `names` object. 104 104 + for (name in files) { 105 105 + val rawBytes = runCatching { extractor.getAssociatedFile(name).readBytes() }.getOrNull() 106 106 + ?: continue 107 107 + 108 108 + val decoded = rawBytes.decodeUtf8PossiblyCompressed() 109 109 + val trimmed = decoded.trimStart() 110 110 + if (!trimmed.startsWith("{")) continue 111 111 + 112 112 + val labels = parseUltralyticsNamesJson(decoded) 113 113 + if (labels.isNotEmpty()) { 114 114 + Logger.d { "Loaded ${labels.size} labels from associated file '$name'" } 115 115 + return@runCatching labels 116 116 + } 117 117 + } 118 118 + emptyList() 119 119 + }.onFailure { t -> 120 120 + Logger.w(t) { "Failed to load labels from TFLite metadata" } 121 121 + }.getOrDefault(emptyList()) 122 122 + } 123 123 + 124 124 + private fun ByteArray.decodeUtf8PossiblyCompressed(): String { 125 125 + if (isEmpty()) return "" 126 126 + 127 127 + fun firstBytesHex(n: Int = 8): String = 128 128 + take(minOf(size, n)).joinToString(" ") { b -> "%02x".format(b) } 129 129 + 130 130 + // 0) Scan for embedded magic headers / common compressed stream signatures. 131 131 + val zipOffset = 132 132 + indexOfSubsequence(byteArrayOf('P'.code.toByte(), 'K'.code.toByte(), 0x03, 0x04)) 133 133 + val gzipOffset = indexOfSubsequence(byteArrayOf(0x1F.toByte(), 0x8B.toByte())) 134 134 + 135 135 + // Common zlib headers (CMF/FLG). Most common are 0x78 0x9C (default), 0x78 0xDA (best), 0x78 0x01 (no compression). 136 136 + val zlibOffsets = listOf( 137 137 + indexOfSubsequence(byteArrayOf(0x78.toByte(), 0x9C.toByte())), 138 138 + indexOfSubsequence(byteArrayOf(0x78.toByte(), 0xDA.toByte())), 139 139 + indexOfSubsequence(byteArrayOf(0x78.toByte(), 0x01.toByte())), 140 140 + ).filter { it >= 0 }.distinct().sorted() 141 141 + 142 142 + if (zipOffset > 0) { 143 143 + runCatching { 144 144 + val sliced = copyOfRange(zipOffset, size) 145 145 + val text = sliced.decodeFromZipIfPossible(preferEntryName = "metadata.json") 146 146 + if (text.isNotBlank() && text.trimStart().startsWith("{")) return text 147 147 + } 148 148 + } 149 149 + 150 150 + if (gzipOffset > 0) { 151 151 + runCatching { 152 152 + val sliced = copyOfRange(gzipOffset, size) 153 153 + val text = sliced.decodeFromGzipOrTarGz() 154 154 + if (text.isNotBlank() && text.trimStart().startsWith("{")) return text 155 155 + } 156 156 + } 157 157 + 158 158 + for (off in zlibOffsets) { 159 159 + if (off <= 0) continue 160 160 + runCatching { 161 161 + val sliced = copyOfRange(off, size) 162 162 + val text = sliced.decodeFromZlib() 163 163 + if (text.isNotBlank() && text.trimStart().startsWith("{")) return text 164 164 + } 165 165 + } 166 166 + 167 167 + runCatching { 168 168 + val text = decodeFromGzipOrTarGz() 169 169 + if (text.isNotBlank() && text.trimStart().startsWith("{")) return text 170 170 + } 171 171 + 172 172 + runCatching { 173 173 + val text = decodeFromZipIfPossible(preferEntryName = "metadata.json") 174 174 + if (text.isNotBlank() && text.trimStart().startsWith("{")) return text 175 175 + } 176 176 + 177 177 + runCatching { 178 178 + val text = decodeFromZlib() 179 179 + if (text.isNotBlank() && text.trimStart().startsWith("{")) return text 180 180 + } 181 181 + 182 182 + runCatching { 183 183 + val text = decodeFromDeflateRaw() 184 184 + if (text.isNotBlank() && text.trimStart().startsWith("{")) return text 185 185 + } 186 186 + 187 187 + return toString(StandardCharsets.UTF_8) 188 188 + } 189 189 + 190 190 + private fun ByteArray.decodeFromDeflateRaw(): String { 191 191 + return runCatching { 192 192 + val inflater = Inflater(true) // nowrap=true => raw DEFLATE 193 193 + InflaterInputStream(ByteArrayInputStream(this), inflater) 194 194 + .bufferedReader(StandardCharsets.UTF_8) 195 195 + .use { it.readText() } 196 196 + }.getOrDefault("") 197 197 + } 198 198 + 199 199 + private fun ByteArray.decodeFromZlib(): String { 200 200 + return runCatching { 201 201 + InflaterInputStream(ByteArrayInputStream(this)) 202 202 + .bufferedReader(StandardCharsets.UTF_8) 203 203 + .use { it.readText() } 204 204 + }.getOrDefault("") 205 205 + } 206 206 + 207 207 + private fun ByteArray.decodeFromGzipOrTarGz(): String { 208 208 + // First try: plain gzipped UTF-8 directly 209 209 + runCatching { 210 210 + val text = GZIPInputStream(ByteArrayInputStream(this)) 211 211 + .bufferedReader(StandardCharsets.UTF_8) 212 212 + .use { it.readText() } 213 213 + if (text.isNotBlank()) return text 214 214 + } 215 215 + 216 216 + // Second try: gzipped TAR that contains metadata.json (or first file) 217 217 + return runCatching { 218 218 + GZIPInputStream(ByteArrayInputStream(this)).use { gz -> 219 219 + extractFirstTarFileUtf8(gz) 220 220 + } 221 221 + }.getOrElse { 222 222 + "" 223 223 + } 224 224 + } 225 225 + 226 226 + private fun extractFirstTarFileUtf8(input: java.io.InputStream): String { 227 227 + // Minimal TAR reader: TAR headers are 512 bytes. 228 228 + // We only need to extract the first regular file (or metadata.json if present) 229 229 + // and decode it as UTF-8. 230 230 + 231 231 + fun readExactly(buf: ByteArray): Boolean { 232 232 + var off = 0 233 233 + while (off < buf.size) { 234 234 + val r = input.read(buf, off, buf.size - off) 235 235 + if (r <= 0) return false 236 236 + off += r 237 237 + } 238 238 + return true 239 239 + } 240 240 + 241 241 + val header = ByteArray(512) 242 242 + var firstText: String? = null 243 243 + 244 244 + while (true) { 245 245 + if (!readExactly(header)) break 246 246 + // End of archive: two consecutive 512-byte blocks of zero 247 247 + if (header.all { it == 0.toByte() }) break 248 248 + 249 249 + val name = header.copyOfRange(0, 100).toString(StandardCharsets.US_ASCII) 250 250 + .trimEnd { it == '\u0000' } 251 251 + val sizeOctal = header.copyOfRange(124, 136).toString(StandardCharsets.US_ASCII).trim() 252 252 + .trimEnd { it == '\u0000' } 253 253 + val typeFlag = header[156] 254 254 + 255 255 + val fileSize = sizeOctal.toLongOrNull(8) ?: 0L 256 256 + val isRegularFile = typeFlag == 0.toByte() || typeFlag == '0'.code.toByte() 257 257 + 258 258 + val fileData = ByteArray(fileSize.toInt()) 259 259 + if (fileSize > 0 && !readExactly(fileData)) break 260 260 + 261 261 + // TAR pads file data to 512 byte blocks. 262 262 + val pad = ((512 - (fileSize % 512)) % 512).toInt() 263 263 + if (pad > 0) { 264 264 + val skip = ByteArray(pad) 265 265 + if (!readExactly(skip)) break 266 266 + } 267 267 + 268 268 + if (isRegularFile) { 269 269 + val text = fileData.toString(StandardCharsets.UTF_8) 270 270 + if (name.equals("metadata.json", ignoreCase = true)) return text 271 271 + if (firstText == null && text.isNotBlank()) firstText = text 272 272 + } 273 273 + } 274 274 + 275 275 + return firstText.orEmpty() 276 276 + } 277 277 + 278 278 + private fun ByteArray.indexOfSubsequence(needle: ByteArray): Int { 279 279 + if (needle.isEmpty() || size < needle.size) return -1 280 280 + outer@ for (i in 0..(size - needle.size)) { 281 281 + for (j in needle.indices) { 282 282 + if (this[i + j] != needle[j]) continue@outer 283 283 + } 284 284 + return i 285 285 + } 286 286 + return -1 287 287 + } 288 288 + 289 289 + private fun parseUltralyticsNamesJson(text: String): List<String> { 290 290 + return runCatching { 291 291 + val root = JSONObject(text) 292 292 + val namesObj = root.optJSONObject("names") ?: return@runCatching emptyList() 293 293 + 294 294 + val keys = namesObj.keys().asSequence() 295 295 + .mapNotNull { k -> k.toIntOrNull()?.let { idx -> idx to k } } 296 296 + .sortedBy { it.first } 297 297 + .toList() 298 298 + 299 299 + if (keys.isEmpty()) return@runCatching emptyList() 300 300 + 301 301 + val maxIdx = keys.maxOf { it.first } 302 302 + val out = MutableList(maxIdx + 1) { "" } 303 303 + 304 304 + for ((idx, key) in keys) { 305 305 + val label = namesObj.optString(key, "").trim() 306 306 + if (label.isNotBlank() && idx in out.indices) { 307 307 + out[idx] = label 308 308 + } 309 309 + } 310 310 + 311 311 + out.filter { it.isNotBlank() } 312 312 + }.getOrDefault(emptyList()) 313 313 + } 314 314 + 315 315 + private fun parseUltralyticsNames(text: String): List<String> { 316 316 + // Grab the `names: { ... }` section (non-greedy) to avoid matching other maps. 317 317 + val namesBlock = Regex("""['"]names['"]\s*:\s*\{([\s\S]*?)\}""") 318 318 + .find(text) 319 319 + ?.groupValues 320 320 + ?.getOrNull(1) 321 321 + ?: return emptyList() 322 322 + 323 323 + // Match entries like: 0: 'person' OR 0: "person" 324 324 + val entry = Regex("""(\d+)\s*:\s*['"]([^'"]+)['"]""") 325 325 + val pairs = entry.findAll(namesBlock).mapNotNull { m -> 326 326 + val idx = m.groupValues[1].toIntOrNull() ?: return@mapNotNull null 327 327 + val name = m.groupValues[2].trim() 328 328 + if (name.isBlank()) null else idx to name 329 329 + }.toList() 330 330 + 331 331 + if (pairs.isEmpty()) return emptyList() 332 332 + 333 333 + val maxIdx = pairs.maxOf { it.first } 334 334 + val out = MutableList(maxIdx + 1) { "" } 335 335 + for ((i, label) in pairs) { 336 336 + if (i in out.indices) out[i] = label 337 337 + } 338 338 + return out.filter { it.isNotBlank() } 339 339 + } 340 340 + 341 341 + data class ModelInfo( 342 342 + val inputShape: IntArray, 343 343 + val outputShape: IntArray, 344 344 + val inputWidth: Int, 345 345 + val inputHeight: Int, 346 346 + val inputChannels: Int? = null, 347 347 + val isNhwc: Boolean? = null, 348 348 + val labels: List<String>, 349 349 + ) { 350 350 + companion object { 351 351 + fun fromShapes( 352 352 + inputShape: IntArray, 353 353 + outputShape: IntArray, 354 354 + labels: List<String> 355 355 + ): ModelInfo { 356 356 + // Common TFLite image shapes: 357 357 + // NHWC: [1, H, W, C] 358 358 + // NCHW: [1, C, H, W] (less common on Android) 359 359 + // Some models might be [H, W, C] (no batch). 360 360 + val (h, w, c, nhwc) = when (inputShape.size) { 361 361 + 4 -> { 362 362 + val isNhwcGuess = inputShape[3] in 1..4 363 363 + if (isNhwcGuess) { 364 364 + Quad(inputShape[1], inputShape[2], inputShape[3], true) 365 365 + } else { 366 366 + Quad(inputShape[2], inputShape[3], inputShape[1], false) 367 367 + } 368 368 + } 369 369 + 370 370 + 3 -> Quad(inputShape[0], inputShape[1], inputShape[2], true) 371 371 + else -> Quad(0, 0, null, null) 372 372 + } 373 373 + 374 374 + return ModelInfo( 375 375 + inputShape = inputShape, 376 376 + outputShape = outputShape, 377 377 + inputWidth = w, 378 378 + inputHeight = h, 379 379 + inputChannels = c, 380 380 + isNhwc = nhwc, 381 381 + labels = labels, 382 382 + ) 383 383 + } 384 384 + } 385 385 + } 386 386 + 387 387 + private data class Quad( 388 388 + val h: Int, val w: Int, val c: Int?, val nhwc: Boolean? 389 389 + ) 390 390 + 391 391 + data class AndroidDetector( 392 392 + val interpreter: Interpreter, val modelInfo: ModelInfo 393 393 + ) 394 394 + 395 395 + actual class ObjectModel { 396 396 + 397 397 + private var detector: AndroidDetector? = null 398 398 + 399 399 + constructor(detector: AndroidDetector) { 400 400 + this.detector = detector 401 401 + } 402 402 + 403 403 + fun getDetector(): AndroidDetector? { 404 404 + return detector 405 405 + } 406 406 + } 407 407 + 408 408 + private fun ByteArray.decodeFromZipIfPossible(preferEntryName: String): String { 409 409 + return runCatching { 410 410 + ZipInputStream(ByteArrayInputStream(this)).use { zis -> 411 411 + var firstNonEmpty: String? = null 412 412 + val entries = mutableListOf<String>() 413 413 + 414 414 + while (true) { 415 415 + val entry = zis.nextEntry ?: break 416 416 + entries += entry.name 417 417 + if (entry.isDirectory) continue 418 418 + 419 419 + val entryBytes = zis.readBytes() 420 420 + val text = entryBytes.toString(StandardCharsets.UTF_8) 421 421 + 422 422 + if (entry.name.equals(preferEntryName, ignoreCase = true)) { 423 423 + Logger.d { "TFLite metadata zip entry=${entry.name} size=${entryBytes.size} entries=$entries" } 424 424 + return@use text 425 425 + } 426 426 + 427 427 + if (firstNonEmpty == null && text.isNotBlank()) { 428 428 + firstNonEmpty = text 429 429 + } 430 430 + } 431 431 + 432 432 + firstNonEmpty.orEmpty() 433 433 + } 434 434 + }.getOrDefault("") 435 435 + } 436 436 + 437 437 + @Composable 438 438 + internal actual fun platformRememberObjectModel(modelPath: ModelPath): ObjectModel { 439 439 + return ObjectModelProvider.get(modelPath) 440 440 + }

+150

handover/reference_code/ImageDetector.android.kt

Reviewed

··· 1 1 + package com.performancecoachlab.posedetection.custom 2 2 + 3 3 + import android.graphics.Bitmap 4 4 + import android.graphics.Canvas 5 5 + import android.graphics.Color 6 6 + import android.graphics.Paint 7 7 + import android.graphics.RectF 8 8 + import androidx.compose.ui.geometry.Rect 9 9 + import androidx.compose.ui.graphics.ImageBitmap 10 10 + import androidx.compose.ui.graphics.asAndroidBitmap 11 11 + import co.touchlab.kermit.Logger 12 12 + import com.performancecoachlab.posedetection.camera.label 13 13 + import com.performancecoachlab.posedetection.recording.AnalysisObject 14 14 + import com.performancecoachlab.posedetection.recording.FrameSize 15 15 + import com.performancecoachlab.posedetection.recording.Label 16 16 + import org.tensorflow.lite.DataType 17 17 + import org.tensorflow.lite.support.common.ops.CastOp 18 18 + import org.tensorflow.lite.support.common.ops.NormalizeOp 19 19 + import org.tensorflow.lite.support.image.ImageProcessor 20 20 + import org.tensorflow.lite.support.image.TensorImage 21 21 + import org.tensorflow.lite.support.tensorbuffer.TensorBuffer 22 22 + import kotlin.math.absoluteValue 23 23 + import kotlin.math.max 24 24 + import kotlin.math.min 25 25 + import kotlin.math.roundToInt 26 26 + 27 27 + actual class ImageDetector actual constructor(model: ObjectModel) { 28 28 + 29 29 + private val detector: AndroidDetector? = model.getDetector() 30 30 + 31 31 + private val imageProcessor = ImageProcessor.Builder() 32 32 + .add(NormalizeOp(0f, 255f)) 33 33 + .add(CastOp(DataType.FLOAT32)) 34 34 + .build() 35 35 + 36 36 + actual fun detect(image: ImageBitmap): List<AnalysisObject> { 37 37 + val det = detector ?: return emptyList() 38 38 + val info = det.modelInfo 39 39 + val inputW = info.inputWidth 40 40 + val inputH = info.inputHeight 41 41 + if (inputW <= 0 || inputH <= 0) return emptyList() 42 42 + 43 43 + val srcBitmap = image.asAndroidBitmap().let { bmp -> 44 44 + if (bmp.config == Bitmap.Config.ARGB_8888) bmp 45 45 + else bmp.copy(Bitmap.Config.ARGB_8888, false) 46 46 + } 47 47 + val imgW = srcBitmap.width 48 48 + val imgH = srcBitmap.height 49 49 + 50 50 + // Letterbox: scale the source to fit inside (inputW, inputH) while 51 51 + // preserving aspect ratio, center it, and pad the remainder with 52 52 + // gray 114 — matching ultralytics training-time preprocessing. 53 53 + // (A naive stretch-resize destroys aspect ratio and hurts detection 54 54 + // quality on non-square camera frames.) 55 55 + val scale = min( 56 56 + inputW.toFloat() / imgW.toFloat(), 57 57 + inputH.toFloat() / imgH.toFloat() 58 58 + ) 59 59 + val scaledW = (imgW * scale).roundToInt().coerceAtLeast(1) 60 60 + val scaledH = (imgH * scale).roundToInt().coerceAtLeast(1) 61 61 + val padX = (inputW - scaledW) / 2f 62 62 + val padY = (inputH - scaledH) / 2f 63 63 + 64 64 + val resized = Bitmap.createBitmap(inputW, inputH, Bitmap.Config.ARGB_8888) 65 65 + val canvas = Canvas(resized) 66 66 + canvas.drawColor(Color.rgb(114, 114, 114)) 67 67 + canvas.drawBitmap( 68 68 + srcBitmap, null, 69 69 + RectF(padX, padY, padX + scaledW, padY + scaledH), 70 70 + Paint(Paint.FILTER_BITMAP_FLAG) 71 71 + ) 72 72 + 73 73 + // Normalize and convert to tensor 74 74 + val tensorImage = TensorImage(DataType.FLOAT32).also { it.load(resized) } 75 75 + .let(imageProcessor::process) 76 76 + 77 77 + // Run inference 78 78 + val outputShape = info.outputShape 79 79 + val output = TensorBuffer.createFixedSize(outputShape, DataType.FLOAT32) 80 80 + try { 81 81 + det.interpreter.run(tensorImage.buffer, output.buffer) 82 82 + } catch (e: Exception) { 83 83 + Logger.e(e) { "ImageDetector: interpreter.run failed" } 84 84 + return emptyList() 85 85 + } 86 86 + 87 87 + // Parse output 88 88 + val array = output.floatArray 89 89 + if (outputShape.size != 3) return emptyList() 90 90 + 91 91 + val dim1 = outputShape[1] 92 92 + val dim2 = outputShape[2] 93 93 + 94 94 + val elements: Int 95 95 + val channels: Int 96 96 + val isElementsFirst: Boolean 97 97 + 98 98 + when { 99 99 + dim2 == 6 -> { elements = dim1; channels = dim2; isElementsFirst = true } 100 100 + dim1 == 6 -> { channels = dim1; elements = dim2; isElementsFirst = false } 101 101 + else -> return emptyList() 102 102 + } 103 103 + 104 104 + fun valueAt(elementIndex: Int, channelIndex: Int): Float { 105 105 + return if (isElementsFirst) array[elementIndex * channels + channelIndex] 106 106 + else array[channelIndex * elements + elementIndex] 107 107 + } 108 108 + 109 109 + val imgWF = imgW.toFloat() 110 110 + val imgHF = imgH.toFloat() 111 111 + 112 112 + return (0 until elements).mapNotNull { i -> 113 113 + val cnf = valueAt(i, 4) 114 114 + if (cnf > 0.25f) { 115 115 + val x1 = valueAt(i, 0) 116 116 + val y1 = valueAt(i, 1) 117 117 + val x2 = valueAt(i, 2) 118 118 + val y2 = valueAt(i, 3) 119 119 + val cls = valueAt(i, 5).toInt() 120 120 + 121 121 + // Model outputs are normalized [0,1] over the letterboxed 122 122 + // input (inputW × inputH). Un-letterbox: convert to 123 123 + // letterboxed pixel coords, subtract padding, divide by 124 124 + // the fit ratio to get original-image pixel coords. 125 125 + val x1pLb = min(x1, x2) * inputW 126 126 + val y1pLb = min(y1, y2) * inputH 127 127 + val x2pLb = max(x1, x2) * inputW 128 128 + val y2pLb = max(y1, y2) * inputH 129 129 + 130 130 + val left = ((x1pLb - padX) / scale).coerceIn(0f, imgWF) 131 131 + val top = ((y1pLb - padY) / scale).coerceIn(0f, imgHF) 132 132 + val right = ((x2pLb - padX) / scale).coerceIn(0f, imgWF) 133 133 + val bottom = ((y2pLb - padY) / scale).coerceIn(0f, imgHF) 134 134 + 135 135 + AnalysisObject( 136 136 + boundingBox = Rect( 137 137 + left = left, 138 138 + top = top, 139 139 + right = right, 140 140 + bottom = bottom 141 141 + ), 142 142 + trackingId = 0, 143 143 + labels = listOf(Label(info.label(cls), cnf)), 144 144 + frameSize = FrameSize(width = imgW.absoluteValue, height = imgH.absoluteValue), 145 145 + timestamp = 0L 146 146 + ) 147 147 + } else null 148 148 + } 149 149 + } 150 150 + }

+427

handover/reference_code/compare_logs.py

Reviewed

··· 1 1 + #!/usr/bin/env python3 2 2 + """ 3 3 + Generate a model-comparison report from experiments/<run-id>/ directories. 4 4 + 5 5 + Each ``experiments/<run-id>/`` directory is expected to contain: 6 6 + 7 7 + * ``manifest.json`` with at least ``run_id``, ``model_tag``, ``t0_wall_ms``, 8 8 + ``duration_seconds``, ``device_label`` (written by ``tools/run_experiment.sh``). 9 9 + * One or more ``*.json`` files matching the experiment-log schema produced by 10 10 + the sample app's Experiment Mode (``device_model``, ``model_name``, ``run_id``, 11 11 + ``started_wall_ms``, ``stopped_wall_ms``, ``events``). 12 12 + 13 13 + For every run we compute a scorecard (detection rate, mean confidence, class 14 14 + diversity, per-class jitter), a per-bucket comparison table aligned to each 15 15 + run's ``t0_wall_ms`` so model A and model B share the same x-axis when run 16 16 + against the same video, and one matplotlib chart per class plotting confidence 17 17 + over time. matplotlib is optional — if it's not installed the report still 18 18 + renders, just without the timeline images. 19 19 + 20 20 + Usage: 21 21 + tools/compare_logs.py experiments/ # report.html + summary.csv in cwd 22 22 + tools/compare_logs.py experiments/ -o ./reports/ # write into ./reports/ 23 23 + tools/compare_logs.py experiments/ --bucket 200 # use 200ms buckets 24 24 + """ 25 25 + 26 26 + from __future__ import annotations 27 27 + 28 28 + import argparse 29 29 + import base64 30 30 + import csv 31 31 + import io 32 32 + import json 33 33 + import math 34 34 + import statistics 35 35 + import sys 36 36 + from collections import defaultdict 37 37 + from html import escape 38 38 + from pathlib import Path 39 39 + 40 40 + # matplotlib is optional. If unavailable, the report omits timeline charts. 41 41 + try: 42 42 + import matplotlib 43 43 + 44 44 + matplotlib.use("Agg") 45 45 + import matplotlib.pyplot as plt 46 46 + 47 47 + HAS_MPL = True 48 48 + except ImportError: 49 49 + HAS_MPL = False 50 50 + 51 51 + 52 52 + # ----------------------------------------------------------------------------- run loading 53 53 + 54 54 + def load_runs(experiments_dir: Path): 55 55 + """Yield (manifest, events_sorted_by_wall_ms) for each run subdir.""" 56 56 + for run_dir in sorted(experiments_dir.iterdir()): 57 57 + if not run_dir.is_dir(): 58 58 + continue 59 59 + manifest_path = run_dir / "manifest.json" 60 60 + if not manifest_path.exists(): 61 61 + print(f" skipping {run_dir.name}: no manifest.json", file=sys.stderr) 62 62 + continue 63 63 + try: 64 64 + with open(manifest_path) as f: 65 65 + manifest = json.load(f) 66 66 + except json.JSONDecodeError as e: 67 67 + print(f" skipping {run_dir.name}: bad manifest.json ({e})", file=sys.stderr) 68 68 + continue 69 69 + log_files = [f for f in run_dir.glob("*.json") if f.name != "manifest.json"] 70 70 + if not log_files: 71 71 + print(f" skipping {run_dir.name}: no log files", file=sys.stderr) 72 72 + continue 73 73 + events: list[dict] = [] 74 74 + for log in log_files: 75 75 + try: 76 76 + with open(log) as f: 77 77 + data = json.load(f) 78 78 + except json.JSONDecodeError as e: 79 79 + print(f" warning: bad log {log.name} ({e})", file=sys.stderr) 80 80 + continue 81 81 + events.extend(data.get("events", [])) 82 82 + events.sort(key=lambda e: e.get("wall_ms", 0)) 83 83 + yield manifest, events 84 84 + 85 85 + 86 86 + # ----------------------------------------------------------------------------- per-run metrics 87 87 + 88 88 + def compute_scorecard(manifest: dict, events: list[dict]) -> dict: 89 89 + """Return summary metrics for one run.""" 90 90 + t0 = manifest["t0_wall_ms"] 91 91 + duration_s = float(manifest.get("duration_seconds") or 0) 92 92 + 93 93 + total_events = len(events) 94 94 + events_with_objects = sum(1 for e in events if e.get("objects")) 95 95 + 96 96 + all_confs: list[float] = [] 97 97 + classes: set[str] = set() 98 98 + bbox_areas: list[float] = [] 99 99 + for e in events: 100 100 + for obj in e.get("objects", []): 101 101 + all_confs.append(float(obj.get("confidence", 0))) 102 102 + classes.add(obj.get("label", "")) 103 103 + bbox = obj.get("bbox", [0, 0, 0, 0]) 104 104 + if len(bbox) >= 4: 105 105 + w = max(0.0, float(bbox[2]) - float(bbox[0])) 106 106 + h = max(0.0, float(bbox[3]) - float(bbox[1])) 107 107 + bbox_areas.append(w * h) 108 108 + 109 109 + # Jitter per class: stddev of consecutive bbox-center deltas. High jitter 110 110 + # means the box is bouncing around frame-to-frame; low jitter means a 111 111 + # stable lock. We sum stdev_dx + stdev_dy as a coarse single-number score. 112 112 + jitter_per_class: dict[str, float] = {} 113 113 + for cls in classes: 114 114 + centers: list[tuple[float, float]] = [] 115 115 + for e in events: 116 116 + for obj in e.get("objects", []): 117 117 + if obj.get("label") == cls: 118 118 + bbox = obj.get("bbox", [0, 0, 0, 0]) 119 119 + if len(bbox) >= 4: 120 120 + cx = (float(bbox[0]) + float(bbox[2])) / 2 121 121 + cy = (float(bbox[1]) + float(bbox[3])) / 2 122 122 + centers.append((cx, cy)) 123 123 + break # at most one center per frame for each class 124 124 + if len(centers) >= 3: 125 125 + dxs = [centers[i + 1][0] - centers[i][0] for i in range(len(centers) - 1)] 126 126 + dys = [centers[i + 1][1] - centers[i][1] for i in range(len(centers) - 1)] 127 127 + jitter_per_class[cls] = statistics.stdev(dxs) + statistics.stdev(dys) 128 128 + 129 129 + return { 130 130 + "run_id": manifest["run_id"], 131 131 + "model_tag": manifest.get("model_tag", "unknown"), 132 132 + "device_label": manifest.get("device_label", "unknown"), 133 133 + "video_path": manifest.get("video_path", ""), 134 134 + "duration_s": duration_s, 135 135 + "t0_wall_ms": t0, 136 136 + "total_events": total_events, 137 137 + "events_with_objects": events_with_objects, 138 138 + "detection_rate": events_with_objects / total_events if total_events else 0.0, 139 139 + "events_per_second": total_events / duration_s if duration_s else 0.0, 140 140 + "mean_confidence": statistics.mean(all_confs) if all_confs else 0.0, 141 141 + "class_diversity": len(classes), 142 142 + "classes": sorted(c for c in classes if c), 143 143 + "mean_bbox_area": statistics.mean(bbox_areas) if bbox_areas else 0.0, 144 144 + "jitter_per_class": jitter_per_class, 145 145 + } 146 146 + 147 147 + 148 148 + # ----------------------------------------------------------------------------- bucketing 149 149 + 150 150 + def bucket_events(events: list[dict], t0: int, bucket_ms: int) -> dict[int, list[dict]]: 151 151 + """Group events into buckets keyed by floor(relative_ms / bucket_ms).""" 152 152 + buckets: dict[int, list[dict]] = defaultdict(list) 153 153 + for e in events: 154 154 + rel = int(e.get("wall_ms", 0)) - t0 155 155 + if rel < 0: 156 156 + continue 157 157 + buckets[rel // bucket_ms].append(e) 158 158 + return buckets 159 159 + 160 160 + 161 161 + def bucket_summary(events_in_bucket: list[dict]) -> dict: 162 162 + """Per-bucket / per-model aggregation: count, top-class, top-confidence.""" 163 163 + confs: list[float] = [] 164 164 + label_counts: dict[str, int] = defaultdict(int) 165 165 + for e in events_in_bucket: 166 166 + for obj in e.get("objects", []): 167 167 + confs.append(float(obj.get("confidence", 0))) 168 168 + label_counts[obj.get("label", "")] += 1 169 169 + top_label = max(label_counts.items(), key=lambda kv: kv[1])[0] if label_counts else "" 170 170 + return { 171 171 + "frames": len(events_in_bucket), 172 172 + "detections": sum(label_counts.values()), 173 173 + "top_label": top_label, 174 174 + "max_conf": max(confs) if confs else 0.0, 175 175 + "mean_conf": statistics.mean(confs) if confs else 0.0, 176 176 + } 177 177 + 178 178 + 179 179 + # ----------------------------------------------------------------------------- charts 180 180 + 181 181 + def render_timeline_png(per_run_series: dict[str, list[tuple[float, float]]], title: str) -> str | None: 182 182 + """Build a single matplotlib chart and return it as a base64 data URI.""" 183 183 + if not HAS_MPL: 184 184 + return None 185 185 + fig, ax = plt.subplots(figsize=(9, 3)) 186 186 + has_data = False 187 187 + for model_tag, series in per_run_series.items(): 188 188 + if not series: 189 189 + continue 190 190 + xs = [s[0] / 1000.0 for s in series] 191 191 + ys = [s[1] for s in series] 192 192 + ax.plot(xs, ys, marker=".", linestyle="-", linewidth=1, label=model_tag) 193 193 + has_data = True 194 194 + if not has_data: 195 195 + plt.close(fig) 196 196 + return None 197 197 + ax.set_xlabel("seconds since t0") 198 198 + ax.set_ylabel("confidence") 199 199 + ax.set_ylim(0, 1) 200 200 + ax.set_title(title) 201 201 + ax.legend(loc="upper right", fontsize=8) 202 202 + ax.grid(True, alpha=0.3) 203 203 + buf = io.BytesIO() 204 204 + fig.tight_layout() 205 205 + fig.savefig(buf, format="png", dpi=110) 206 206 + plt.close(fig) 207 207 + return "data:image/png;base64," + base64.b64encode(buf.getvalue()).decode("ascii") 208 208 + 209 209 + 210 210 + def class_timeline_data(manifest: dict, events: list[dict], cls: str) -> list[tuple[float, float]]: 211 211 + """Return [(relative_ms, top_confidence_for_class)] for one run + class.""" 212 212 + t0 = manifest["t0_wall_ms"] 213 213 + out: list[tuple[float, float]] = [] 214 214 + for e in events: 215 215 + rel = int(e.get("wall_ms", 0)) - t0 216 216 + if rel < 0: 217 217 + continue 218 218 + best = 0.0 219 219 + found = False 220 220 + for obj in e.get("objects", []): 221 221 + if obj.get("label") == cls: 222 222 + c = float(obj.get("confidence", 0)) 223 223 + if c > best: 224 224 + best = c 225 225 + found = True 226 226 + if found: 227 227 + out.append((rel, best)) 228 228 + return out 229 229 + 230 230 + 231 231 + # ----------------------------------------------------------------------------- HTML rendering 232 232 + 233 233 + def fmt_pct(x: float) -> str: 234 234 + return f"{x * 100:.1f}%" 235 235 + 236 236 + def fmt_num(x: float, places: int = 2) -> str: 237 237 + return f"{x:.{places}f}" 238 238 + 239 239 + 240 240 + def render_html(scorecards: list[dict], runs: list[tuple[dict, list[dict]]], bucket_ms: int) -> str: 241 241 + style = """ 242 242 + body { font-family: -apple-system, sans-serif; margin: 24px; color: #222; } 243 243 + h1, h2 { font-weight: 600; } 244 244 + table { border-collapse: collapse; margin: 8px 0 24px 0; font-size: 13px; } 245 245 + th, td { border: 1px solid #ddd; padding: 4px 8px; text-align: right; } 246 246 + th { background: #f5f5f5; text-align: left; } 247 247 + td.left, th.left { text-align: left; } 248 248 + .scorecard td:first-child { font-weight: 600; } 249 249 + .small { color: #888; font-size: 11px; } 250 250 + img { max-width: 100%; border: 1px solid #ddd; margin: 8px 0; } 251 251 + .warn { background: #fff7e0; border: 1px solid #f0c000; padding: 8px 12px; border-radius: 4px; } 252 252 + """ 253 253 + 254 254 + parts: list[str] = [] 255 255 + parts.append("<!doctype html><html><head><meta charset='utf-8'>") 256 256 + parts.append("<title>Model comparison report</title>") 257 257 + parts.append(f"<style>{style}</style></head><body>") 258 258 + parts.append("<h1>Model comparison report</h1>") 259 259 + parts.append(f"<p class='small'>{len(scorecards)} run(s), bucket size {bucket_ms} ms</p>") 260 260 + 261 261 + if not HAS_MPL: 262 262 + parts.append( 263 263 + "<p class='warn'>matplotlib is not installed — timeline charts are omitted. " 264 264 + "Install with <code>uv pip install matplotlib</code> (or pip) and re-run.</p>" 265 265 + ) 266 266 + 267 267 + # ----- per-run scorecards 268 268 + parts.append("<h2>Per-run scorecard</h2>") 269 269 + parts.append("<table class='scorecard'>") 270 270 + headers = ["metric"] + [s["model_tag"] for s in scorecards] 271 271 + parts.append("<tr>" + "".join(f"<th class='left'>{escape(h)}</th>" for h in headers) + "</tr>") 272 272 + 273 273 + def row(label: str, values: list[str]) -> str: 274 274 + return "<tr><td class='left'>" + escape(label) + "</td>" + "".join( 275 275 + f"<td>{escape(v)}</td>" for v in values 276 276 + ) + "</tr>" 277 277 + 278 278 + parts.append(row("run_id", [s["run_id"] for s in scorecards])) 279 279 + parts.append(row("device", [s["device_label"] for s in scorecards])) 280 280 + parts.append(row("duration (s)", [fmt_num(s["duration_s"], 1) for s in scorecards])) 281 281 + parts.append(row("total events", [str(s["total_events"]) for s in scorecards])) 282 282 + parts.append(row("events / second", [fmt_num(s["events_per_second"], 2) for s in scorecards])) 283 283 + parts.append(row("frames with detection", [fmt_pct(s["detection_rate"]) for s in scorecards])) 284 284 + parts.append(row("mean confidence", [fmt_num(s["mean_confidence"], 3) for s in scorecards])) 285 285 + parts.append(row("classes seen", [str(s["class_diversity"]) for s in scorecards])) 286 286 + parts.append(row("mean bbox area (px²)", [fmt_num(s["mean_bbox_area"], 0) for s in scorecards])) 287 287 + parts.append(row("class list", [", ".join(s["classes"]) for s in scorecards])) 288 288 + parts.append("</table>") 289 289 + 290 290 + # ----- per-class jitter table 291 291 + all_classes = sorted({c for s in scorecards for c in s["classes"]}) 292 292 + if all_classes: 293 293 + parts.append("<h2>Jitter per class (lower = more stable)</h2>") 294 294 + parts.append("<table>") 295 295 + parts.append("<tr><th class='left'>class</th>" + "".join(f"<th>{escape(s['model_tag'])}</th>" for s in scorecards) + "</tr>") 296 296 + for cls in all_classes: 297 297 + row_vals = [] 298 298 + for s in scorecards: 299 299 + v = s["jitter_per_class"].get(cls) 300 300 + row_vals.append(fmt_num(v, 1) if v is not None else "—") 301 301 + parts.append("<tr><td class='left'>" + escape(cls) + "</td>" + "".join(f"<td>{v}</td>" for v in row_vals) + "</tr>") 302 302 + parts.append("</table>") 303 303 + 304 304 + # ----- per-bucket comparison table 305 305 + parts.append(f"<h2>Per-bucket comparison ({bucket_ms} ms bins)</h2>") 306 306 + bucketed_per_run = [] 307 307 + for s, (manifest, events) in zip(scorecards, runs): 308 308 + bucketed_per_run.append(bucket_events(events, manifest["t0_wall_ms"], bucket_ms)) 309 309 + all_buckets = sorted({b for d in bucketed_per_run for b in d.keys()}) 310 310 + parts.append("<table>") 311 311 + header_cells = ["<th class='left'>bucket (ms)</th>"] 312 312 + for s in scorecards: 313 313 + tag = escape(s["model_tag"]) 314 314 + header_cells.append(f"<th>{tag}<br><span class='small'>frames</span></th>") 315 315 + header_cells.append(f"<th>{tag}<br><span class='small'>top class</span></th>") 316 316 + header_cells.append(f"<th>{tag}<br><span class='small'>max conf</span></th>") 317 317 + parts.append("<tr>" + "".join(header_cells) + "</tr>") 318 318 + for b in all_buckets: 319 319 + cells = [f"<td class='left'>{b * bucket_ms}–{(b + 1) * bucket_ms}</td>"] 320 320 + for run_buckets in bucketed_per_run: 321 321 + evs = run_buckets.get(b, []) 322 322 + if evs: 323 323 + summ = bucket_summary(evs) 324 324 + cells.append(f"<td>{summ['frames']}</td>") 325 325 + cells.append(f"<td>{escape(summ['top_label'])}</td>") 326 326 + cells.append(f"<td>{fmt_num(summ['max_conf'], 2)}</td>") 327 327 + else: 328 328 + cells.append("<td>—</td><td>—</td><td>—</td>") 329 329 + parts.append("<tr>" + "".join(cells) + "</tr>") 330 330 + parts.append("</table>") 331 331 + 332 332 + # ----- per-class timeline charts 333 333 + if HAS_MPL: 334 334 + parts.append("<h2>Per-class confidence timelines</h2>") 335 335 + for cls in all_classes: 336 336 + per_run_series: dict[str, list[tuple[float, float]]] = {} 337 337 + for s, (manifest, events) in zip(scorecards, runs): 338 338 + per_run_series[s["model_tag"]] = class_timeline_data(manifest, events, cls) 339 339 + data_uri = render_timeline_png(per_run_series, f"class: {cls}") 340 340 + if data_uri: 341 341 + parts.append(f"<img alt='{escape(cls)} timeline' src='{data_uri}'/>") 342 342 + 343 343 + parts.append("</body></html>") 344 344 + return "".join(parts) 345 345 + 346 346 + 347 347 + # ----------------------------------------------------------------------------- summary csv 348 348 + 349 349 + def write_summary_csv(scorecards: list[dict], path: Path) -> None: 350 350 + fieldnames = [ 351 351 + "run_id", 352 352 + "model_tag", 353 353 + "device_label", 354 354 + "duration_s", 355 355 + "total_events", 356 356 + "events_per_second", 357 357 + "detection_rate", 358 358 + "mean_confidence", 359 359 + "class_diversity", 360 360 + "classes", 361 361 + "mean_bbox_area", 362 362 + ] 363 363 + with open(path, "w", newline="") as f: 364 364 + w = csv.DictWriter(f, fieldnames=fieldnames) 365 365 + w.writeheader() 366 366 + for s in scorecards: 367 367 + w.writerow( 368 368 + { 369 369 + "run_id": s["run_id"], 370 370 + "model_tag": s["model_tag"], 371 371 + "device_label": s["device_label"], 372 372 + "duration_s": f"{s['duration_s']:.2f}", 373 373 + "total_events": s["total_events"], 374 374 + "events_per_second": f"{s['events_per_second']:.3f}", 375 375 + "detection_rate": f"{s['detection_rate']:.4f}", 376 376 + "mean_confidence": f"{s['mean_confidence']:.4f}", 377 377 + "class_diversity": s["class_diversity"], 378 378 + "classes": "|".join(s["classes"]), 379 379 + "mean_bbox_area": f"{s['mean_bbox_area']:.1f}", 380 380 + } 381 381 + ) 382 382 + 383 383 + 384 384 + # ----------------------------------------------------------------------------- main 385 385 + 386 386 + def main(argv: list[str]) -> int: 387 387 + parser = argparse.ArgumentParser( 388 388 + description="Build a model comparison report from experiments/<run-id>/ directories.", 389 389 + ) 390 390 + parser.add_argument("experiments_dir", type=Path, help="path to the experiments/ directory") 391 391 + parser.add_argument("-o", "--output-dir", type=Path, default=Path.cwd(), 392 392 + help="where report.html and summary.csv should be written (default: cwd)") 393 393 + parser.add_argument("--bucket", type=int, default=100, help="bucket size in milliseconds (default: 100)") 394 394 + args = parser.parse_args(argv) 395 395 + 396 396 + if not args.experiments_dir.is_dir(): 397 397 + print(f"error: not a directory: {args.experiments_dir}", file=sys.stderr) 398 398 + return 2 399 399 + 400 400 + runs = list(load_runs(args.experiments_dir)) 401 401 + if not runs: 402 402 + print("error: no usable runs found.", file=sys.stderr) 403 403 + return 2 404 404 + 405 405 + scorecards = [compute_scorecard(m, e) for m, e in runs] 406 406 + 407 407 + args.output_dir.mkdir(parents=True, exist_ok=True) 408 408 + html_path = args.output_dir / "report.html" 409 409 + csv_path = args.output_dir / "summary.csv" 410 410 + 411 411 + html_path.write_text(render_html(scorecards, runs, args.bucket)) 412 412 + write_summary_csv(scorecards, csv_path) 413 413 + 414 414 + print(f"wrote {html_path}") 415 415 + print(f"wrote {csv_path}") 416 416 + print(f"runs: {len(runs)}") 417 417 + for s in scorecards: 418 418 + print( 419 419 + f" {s['model_tag']}: {s['total_events']} events, " 420 420 + f"{s['detection_rate'] * 100:.1f}% with detection, " 421 421 + f"mean conf {s['mean_confidence']:.3f}" 422 422 + ) 423 423 + return 0 424 424 + 425 425 + 426 426 + if __name__ == "__main__": 427 427 + sys.exit(main(sys.argv[1:]))

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reports/report.html

Reviewed

··· 1 1 + <!doctype html><html><head><meta charset='utf-8'><title>Model comparison report</title><style> 2 2 + body { font-family: -apple-system, sans-serif; margin: 24px; color: #222; } 3 3 + h1, h2 { font-weight: 600; } 4 4 + table { border-collapse: collapse; margin: 8px 0 24px 0; font-size: 13px; } 5 5 + th, td { border: 1px solid #ddd; padding: 4px 8px; text-align: right; } 6 6 + th { background: #f5f5f5; text-align: left; } 7 7 + td.left, th.left { text-align: left; } 8 8 + .scorecard td:first-child { font-weight: 600; } 9 9 + .small { color: #888; font-size: 11px; } 10 10 + img { max-width: 100%; border: 1px solid #ddd; margin: 8px 0; } 11 11 + .warn { background: #fff7e0; border: 1px solid #f0c000; padding: 8px 12px; border-radius: 4px; } 12 12 + </style></head><body><h1>Model comparison report</h1><p class='small'>5 run(s), bucket size 250 ms</p><h2>Per-run scorecard</h2><table class='scorecard'><tr><th class='left'>metric</th><th class='left'>yolo11n_dataset_dataset</th><th class='left'>yolo26n_dataset_v11_dataset_v11_20260410_201918__best_float32</th><th class='left'>yolo26n_v11_rect_384x288_fp16_20260411_083350__best_float16</th><th class='left'>yolo26n_v11_rect_512x384_fp32_20260411_090924__best_float32</th><th class='left'>yolo26n_v11_rect_512x384_fp16_20260411_090924__best_float16</th></tr><tr><td class='left'>run_id</td><td>1775898625937_yolo11n_dataset_dataset</td><td>1775898703073_yolo26n_dataset_v11_dataset_v11_20260410_201918__best_float32</td><td>1775898781050_yolo26n_v11_rect_384x288_fp16_20260411_083350__best_float16</td><td>1775898858800_yolo26n_v11_rect_512x384_fp32_20260411_090924__best_float32</td><td>1775898936159_yolo26n_v11_rect_512x384_fp16_20260411_090924__best_float16</td></tr><tr><td class='left'>device</td><td>samsung SM-A366B</td><td>samsung SM-A366B</td><td>samsung SM-A366B</td><td>samsung SM-A366B</td><td>samsung SM-A366B</td></tr><tr><td class='left'>duration (s)</td><td>60.0</td><td>60.0</td><td>60.0</td><td>60.0</td><td>60.0</td></tr><tr><td class='left'>total events</td><td>419</td><td>461</td><td>678</td><td>402</td><td>404</td></tr><tr><td class='left'>events / second</td><td>6.98</td><td>7.68</td><td>11.30</td><td>6.70</td><td>6.73</td></tr><tr><td class='left'>frames with detection</td><td>100.0%</td><td>100.0%</td><td>100.0%</td><td>100.0%</td><td>100.0%</td></tr><tr><td class='left'>mean confidence</td><td>0.719</td><td>0.639</td><td>0.590</td><td>0.796</td><td>0.777</td></tr><tr><td class='left'>classes seen</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td></tr><tr><td class='left'>mean bbox area (px²)</td><td>531</td><td>523</td><td>570</td><td>536</td><td>533</td></tr><tr><td class='left'>class list</td><td>basketball, basketball_hoop</td><td>basketball, basketball_hoop</td><td>basketball, basketball_hoop</td><td>basketball, basketball_hoop</td><td>basketball, basketball_hoop</td></tr></table><h2>Jitter per class (lower = more stable)</h2><table><tr><th class='left'>class</th><th>yolo11n_dataset_dataset</th><th>yolo26n_dataset_v11_dataset_v11_20260410_201918__best_float32</th><th>yolo26n_v11_rect_384x288_fp16_20260411_083350__best_float16</th><th>yolo26n_v11_rect_512x384_fp32_20260411_090924__best_float32</th><th>yolo26n_v11_rect_512x384_fp16_20260411_090924__best_float16</th></tr><tr><td class='left'>basketball</td><td>110.9</td><td>70.2</td><td>69.3</td><td>68.3</td><td>77.0</td></tr><tr><td class='left'>basketball_hoop</td><td>0.6</td><td>0.6</td><td>1.5</td><td>0.9</td><td>0.8</td></tr></table><h2>Per-bucket comparison (250 ms bins)</h2><table><tr><th class='left'>bucket (ms)</th><th>yolo11n_dataset_dataset<br><span class='small'>frames</span></th><th>yolo11n_dataset_dataset<br><span class='small'>top class</span></th><th>yolo11n_dataset_dataset<br><span class='small'>max conf</span></th><th>yolo26n_dataset_v11_dataset_v11_20260410_201918__best_float32<br><span class='small'>frames</span></th><th>yolo26n_dataset_v11_dataset_v11_20260410_201918__best_float32<br><span class='small'>top class</span></th><th>yolo26n_dataset_v11_dataset_v11_20260410_201918__best_float32<br><span class='small'>max conf</span></th><th>yolo26n_v11_rect_384x288_fp16_20260411_083350__best_float16<br><span class='small'>frames</span></th><th>yolo26n_v11_rect_384x288_fp16_20260411_083350__best_float16<br><span class='small'>top class</span></th><th>yolo26n_v11_rect_384x288_fp16_20260411_083350__best_float16<br><span class='small'>max conf</span></th><th>yolo26n_v11_rect_512x384_fp32_20260411_090924__best_float32<br><span class='small'>frames</span></th><th>yolo26n_v11_rect_512x384_fp32_20260411_090924__best_float32<br><span class='small'>top class</span></th><th>yolo26n_v11_rect_512x384_fp32_20260411_090924__best_float32<br><span class='small'>max conf</span></th><th>yolo26n_v11_rect_512x384_fp16_20260411_090924__best_float16<br><span class='small'>frames</span></th><th>yolo26n_v11_rect_512x384_fp16_20260411_090924__best_float16<br><span class='small'>top class</span></th><th>yolo26n_v11_rect_512x384_fp16_20260411_090924__best_float16<br><span class='small'>max conf</span></th></tr><tr><td class='left'>0–250</td><td>1</td><td>basketball</td><td>0.82</td><td>2</td><td>basketball_hoop</td><td>0.88</td><td>2</td><td>basketball_hoop</td><td>0.94</td><td>2</td><td>basketball</td><td>0.90</td><td>2</td><td>basketball</td><td>0.89</td></tr><tr><td class='left'>250–500</td><td>2</td><td>basketball</td><td>0.83</td><td>3</td><td>basketball_hoop</td><td>0.85</td><td>3</td><td>basketball_hoop</td><td>0.84</td><td>2</td><td>basketball</td><td>0.90</td><td>2</td><td>basketball_hoop</td><td>0.86</td></tr><tr><td class='left'>500–750</td><td>2</td><td>basketball</td><td>0.80</td><td>2</td><td>basketball_hoop</td><td>0.79</td><td>2</td><td>basketball_hoop</td><td>0.81</td><td>2</td><td>basketball_hoop</td><td>0.85</td><td>2</td><td>basketball_hoop</td><td>0.85</td></tr><tr><td class='left'>750–1000</td><td>2</td><td>basketball</td><td>0.85</td><td>1</td><td>basketball_hoop</td><td>0.82</td><td>3</td><td>basketball_hoop</td><td>0.89</td><td>2</td><td>basketball</td><td>0.94</td><td>2</td><td>basketball</td><td>0.85</td></tr><tr><td class='left'>1000–1250</td><td>1</td><td>basketball</td><td>0.81</td><td>2</td><td>basketball</td><td>0.85</td><td>3</td><td>basketball_hoop</td><td>0.88</td><td>2</td><td>basketball</td><td>0.93</td><td>2</td><td>basketball</td><td>0.90</td></tr><tr><td class='left'>1250–1500</td><td>2</td><td>basketball</td><td>0.80</td><td>2</td><td>basketball_hoop</td><td>0.81</td><td>2</td><td>basketball_hoop</td><td>0.87</td><td>1</td><td>basketball</td><td>0.89</td><td>1</td><td>basketball</td><td>0.87</td></tr><tr><td class='left'>1500–1750</td><td>2</td><td>basketball</td><td>0.80</td><td>2</td><td>basketball</td><td>0.88</td><td>3</td><td>basketball_hoop</td><td>0.73</td><td>2</td><td>basketball_hoop</td><td>0.84</td><td>2</td><td>basketball_hoop</td><td>0.89</td></tr><tr><td class='left'>1750–2000</td><td>2</td><td>basketball</td><td>0.76</td><td>2</td><td>basketball_hoop</td><td>0.88</td><td>3</td><td>basketball_hoop</td><td>0.88</td><td>2</td><td>basketball_hoop</td><td>0.85</td><td>2</td><td>basketball</td><td>0.87</td></tr><tr><td class='left'>2000–2250</td><td>1</td><td>basketball</td><td>0.75</td><td>2</td><td>basketball_hoop</td><td>0.90</td><td>3</td><td>basketball</td><td>0.87</td><td>2</td><td>basketball</td><td>0.88</td><td>2</td><td>basketball</td><td>0.84</td></tr><tr><td class='left'>2250–2500</td><td>2</td><td>basketball</td><td>0.76</td><td>2</td><td>basketball_hoop</td><td>0.90</td><td>3</td><td>basketball</td><td>0.86</td><td>2</td><td>basketball_hoop</td><td>0.89</td><td>2</td><td>basketball</td><td>0.86</td></tr><tr><td class='left'>2500–2750</td><td>2</td><td>basketball_hoop</td><td>0.76</td><td>3</td><td>basketball_hoop</td><td>0.89</td><td>2</td><td>basketball_hoop</td><td>0.75</td><td>2</td><td>basketball_hoop</td><td>0.85</td><td>2</td><td>basketball_hoop</td><td>0.86</td></tr><tr><td class='left'>2750–3000</td><td>2</td><td>basketball_hoop</td><td>0.75</td><td>2</td><td>basketball_hoop</td><td>0.77</td><td>3</td><td>basketball_hoop</td><td>0.85</td><td>1</td><td>basketball_hoop</td><td>0.84</td><td>2</td><td>basketball_hoop</td><td>0.85</td></tr><tr><td class='left'>3000–3250</td><td>1</td><td>basketball</td><td>0.81</td><td>2</td><td>basketball_hoop</td><td>0.80</td><td>3</td><td>basketball_hoop</td><td>0.90</td><td>2</td><td>basketball_hoop</td><td>0.86</td><td>2</td><td>basketball</td><td>0.88</td></tr><tr><td class='left'>3250–3500</td><td>2</td><td>basketball</td><td>0.81</td><td>2</td><td>basketball_hoop</td><td>0.88</td><td>3</td><td>basketball_hoop</td><td>0.89</td><td>2</td><td>basketball</td><td>0.93</td><td>1</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>3500–3750</td><td>2</td><td>basketball</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.79</td><td>3</td><td>basketball_hoop</td><td>0.63</td><td>1</td><td>basketball_hoop</td><td>0.85</td><td>2</td><td>basketball_hoop</td><td>0.86</td></tr><tr><td class='left'>3750–4000</td><td>2</td><td>basketball</td><td>0.79</td><td>2</td><td>basketball_hoop</td><td>0.82</td><td>3</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball_hoop</td><td>0.89</td><td>2</td><td>basketball</td><td>0.90</td></tr><tr><td class='left'>4000–4250</td><td>1</td><td>basketball</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.91</td><td>3</td><td>basketball_hoop</td><td>0.89</td><td>2</td><td>basketball</td><td>0.91</td><td>2</td><td>basketball</td><td>0.91</td></tr><tr><td class='left'>4250–4500</td><td>2</td><td>basketball</td><td>0.79</td><td>3</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball_hoop</td><td>0.85</td><td>2</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>4500–4750</td><td>2</td><td>basketball</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.73</td><td>3</td><td>basketball_hoop</td><td>0.51</td><td>1</td><td>basketball_hoop</td><td>0.86</td><td>2</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>4750–5000</td><td>2</td><td>basketball</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.86</td><td>3</td><td>basketball_hoop</td><td>0.80</td><td>2</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>5000–5250</td><td>2</td><td>basketball</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.75</td><td>3</td><td>basketball_hoop</td><td>0.67</td><td>2</td><td>basketball_hoop</td><td>0.87</td><td>1</td><td>basketball_hoop</td><td>0.86</td></tr><tr><td class='left'>5250–5500</td><td>1</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.76</td><td>3</td><td>basketball_hoop</td><td>0.55</td><td>1</td><td>basketball_hoop</td><td>0.86</td><td>2</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>5500–5750</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.79</td><td>3</td><td>basketball_hoop</td><td>0.52</td><td>2</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>5750–6000</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>3</td><td>basketball_hoop</td><td>0.81</td><td>2</td><td>basketball_hoop</td><td>0.59</td><td>2</td><td>basketball_hoop</td><td>0.87</td><td>1</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>6000–6250</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.76</td><td>3</td><td>basketball_hoop</td><td>0.54</td><td>1</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball_hoop</td><td>0.86</td></tr><tr><td class='left'>6250–6500</td><td>1</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.75</td><td>3</td><td>basketball_hoop</td><td>0.75</td><td>2</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball</td><td>0.87</td></tr><tr><td class='left'>6500–6750</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.81</td><td>3</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.87</td><td>1</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>6750–7000</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.72</td><td>3</td><td>basketball_hoop</td><td>0.76</td><td>1</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>7000–7250</td><td>2</td><td>basketball</td><td>0.79</td><td>2</td><td>basketball_hoop</td><td>0.88</td><td>3</td><td>basketball_hoop</td><td>0.67</td><td>2</td><td>basketball</td><td>0.89</td><td>1</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>7250–7500</td><td>1</td><td>basketball</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.89</td><td>3</td><td>basketball_hoop</td><td>0.89</td><td>2</td><td>basketball</td><td>0.89</td><td>2</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>7500–7750</td><td>2</td><td>basketball</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.88</td><td>2</td><td>basketball_hoop</td><td>0.84</td><td>1</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball</td><td>0.90</td></tr><tr><td class='left'>7750–8000</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.81</td><td>3</td><td>basketball_hoop</td><td>0.76</td><td>2</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball</td><td>0.89</td></tr><tr><td class='left'>8000–8250</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.72</td><td>3</td><td>basketball_hoop</td><td>0.56</td><td>2</td><td>basketball_hoop</td><td>0.88</td><td>1</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>8250–8500</td><td>1</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.65</td><td>3</td><td>basketball_hoop</td><td>0.56</td><td>1</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>8500–8750</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.83</td><td>3</td><td>basketball_hoop</td><td>0.56</td><td>2</td><td>basketball_hoop</td><td>0.88</td><td>1</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>8750–9000</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.76</td><td>3</td><td>basketball_hoop</td><td>0.76</td><td>2</td><td>basketball_hoop</td><td>0.88</td><td>2</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>9000–9250</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.80</td><td>3</td><td>basketball_hoop</td><td>0.59</td><td>1</td><td>basketball_hoop</td><td>0.88</td><td>2</td><td>basketball_hoop</td><td>0.88</td></tr><tr><td class='left'>9250–9500</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.72</td><td>3</td><td>basketball_hoop</td><td>0.66</td><td>2</td><td>basketball_hoop</td><td>0.88</td><td>1</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>9500–9750</td><td>1</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.75</td><td>3</td><td>basketball_hoop</td><td>0.74</td><td>2</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball_hoop</td><td>0.88</td></tr><tr><td class='left'>9750–10000</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.79</td><td>2</td><td>basketball_hoop</td><td>0.79</td><td>1</td><td>basketball</td><td>0.87</td><td>2</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>10000–10250</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>1</td><td>basketball_hoop</td><td>0.80</td><td>3</td><td>basketball_hoop</td><td>0.67</td><td>2</td><td>basketball_hoop</td><td>0.88</td><td>2</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>10250–10500</td><td>1</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.81</td><td>2</td><td>basketball_hoop</td><td>0.59</td><td>2</td><td>basketball_hoop</td><td>0.87</td><td>1</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>10500–10750</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.75</td><td>3</td><td>basketball_hoop</td><td>0.65</td><td>1</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>10750–11000</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.76</td><td>2</td><td>basketball_hoop</td><td>0.57</td><td>2</td><td>basketball_hoop</td><td>0.88</td><td>1</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>11000–11250</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.74</td><td>3</td><td>basketball_hoop</td><td>0.60</td><td>2</td><td>basketball_hoop</td><td>0.88</td><td>2</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>11250–11500</td><td>2</td><td>basketball</td><td>0.79</td><td>2</td><td>basketball_hoop</td><td>0.77</td><td>3</td><td>basketball_hoop</td><td>0.79</td><td>1</td><td>basketball_hoop</td><td>0.88</td><td>2</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>11500–11750</td><td>1</td><td>basketball</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.79</td><td>2</td><td>basketball_hoop</td><td>0.88</td><td>2</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>11750–12000</td><td>2</td><td>basketball</td><td>0.86</td><td>2</td><td>basketball_hoop</td><td>0.83</td><td>3</td><td>basketball_hoop</td><td>0.89</td><td>2</td><td>basketball_hoop</td><td>0.88</td><td>1</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>12000–12250</td><td>2</td><td>basketball_hoop</td><td>0.82</td><td>1</td><td>basketball_hoop</td><td>0.73</td><td>3</td><td>basketball_hoop</td><td>0.58</td><td>1</td><td>basketball_hoop</td><td>0.88</td><td>2</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>12250–12500</td><td>2</td><td>basketball</td><td>0.81</td><td>2</td><td>basketball_hoop</td><td>0.75</td><td>3</td><td>basketball_hoop</td><td>0.91</td><td>2</td><td>basketball</td><td>0.88</td><td>1</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>12500–12750</td><td>1</td><td>basketball</td><td>0.82</td><td>2</td><td>basketball_hoop</td><td>0.85</td><td>3</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball</td><td>0.89</td><td>2</td><td>basketball_hoop</td><td>0.91</td></tr><tr><td class='left'>12750–13000</td><td>2</td><td>basketball</td><td>0.81</td><td>2</td><td>basketball_hoop</td><td>0.89</td><td>2</td><td>basketball_hoop</td><td>0.91</td><td>1</td><td>basketball</td><td>0.91</td><td>2</td><td>basketball</td><td>0.88</td></tr><tr><td class='left'>13000–13250</td><td>2</td><td>basketball</td><td>0.84</td><td>2</td><td>basketball_hoop</td><td>0.86</td><td>3</td><td>basketball_hoop</td><td>0.82</td><td>2</td><td>basketball</td><td>0.91</td><td>2</td><td>basketball</td><td>0.89</td></tr><tr><td class='left'>13250–13500</td><td>2</td><td>basketball_hoop</td><td>0.75</td><td>2</td><td>basketball_hoop</td><td>0.53</td><td>3</td><td>basketball_hoop</td><td>0.84</td><td>2</td><td>basketball_hoop</td><td>0.64</td><td>1</td><td>basketball_hoop</td><td>0.80</td></tr><tr><td class='left'>13500–13750</td><td>1</td><td>basketball_hoop</td><td>0.76</td><td>2</td><td>basketball_hoop</td><td>0.80</td><td>3</td><td>basketball_hoop</td><td>0.88</td><td>1</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.90</td></tr><tr><td class='left'>13750–14000</td><td>2</td><td>basketball</td><td>0.83</td><td>2</td><td>basketball_hoop</td><td>0.82</td><td>3</td><td>basketball_hoop</td><td>0.92</td><td>2</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball</td><td>0.90</td></tr><tr><td class='left'>14000–14250</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.81</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball</td><td>0.85</td><td>1</td><td>basketball_hoop</td><td>0.84</td></tr><tr><td class='left'>14250–14500</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.82</td><td>3</td><td>basketball_hoop</td><td>0.95</td><td>1</td><td>basketball</td><td>0.85</td><td>2</td><td>basketball</td><td>0.90</td></tr><tr><td class='left'>14500–14750</td><td>1</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.86</td><td>3</td><td>basketball_hoop</td><td>0.71</td><td>2</td><td>basketball_hoop</td><td>0.85</td><td>2</td><td>basketball_hoop</td><td>0.85</td></tr><tr><td class='left'>14750–15000</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>1</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.65</td><td>2</td><td>basketball_hoop</td><td>0.85</td><td>1</td><td>basketball_hoop</td><td>0.85</td></tr><tr><td class='left'>15000–15250</td><td>2</td><td>basketball</td><td>0.80</td><td>2</td><td>basketball_hoop</td><td>0.81</td><td>3</td><td>basketball_hoop</td><td>0.63</td><td>1</td><td>basketball_hoop</td><td>0.85</td><td>2</td><td>basketball_hoop</td><td>0.85</td></tr><tr><td class='left'>15250–15500</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.79</td><td>3</td><td>basketball_hoop</td><td>0.72</td><td>2</td><td>basketball_hoop</td><td>0.85</td><td>2</td><td>basketball_hoop</td><td>0.86</td></tr><tr><td class='left'>15500–15750</td><td>1</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.76</td><td>3</td><td>basketball_hoop</td><td>0.66</td><td>2</td><td>basketball_hoop</td><td>0.86</td><td>1</td><td>basketball_hoop</td><td>0.85</td></tr><tr><td class='left'>15750–16000</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.79</td><td>2</td><td>basketball_hoop</td><td>0.64</td><td>1</td><td>basketball_hoop</td><td>0.84</td><td>2</td><td>basketball_hoop</td><td>0.85</td></tr><tr><td class='left'>16000–16250</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.80</td><td>3</td><td>basketball_hoop</td><td>0.64</td><td>2</td><td>basketball_hoop</td><td>0.88</td><td>2</td><td>basketball_hoop</td><td>0.84</td></tr><tr><td class='left'>16250–16500</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.77</td><td>3</td><td>basketball_hoop</td><td>0.86</td><td>2</td><td>basketball</td><td>0.86</td><td>1</td><td>basketball_hoop</td><td>0.84</td></tr><tr><td class='left'>16500–16750</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.79</td><td>2</td><td>basketball_hoop</td><td>0.61</td><td>1</td><td>basketball_hoop</td><td>0.85</td><td>2</td><td>basketball_hoop</td><td>0.88</td></tr><tr><td class='left'>16750–17000</td><td>1</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>3</td><td>basketball_hoop</td><td>0.65</td><td>2</td><td>basketball_hoop</td><td>0.85</td><td>2</td><td>basketball_hoop</td><td>0.85</td></tr><tr><td class='left'>17000–17250</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>3</td><td>basketball_hoop</td><td>0.63</td><td>2</td><td>basketball_hoop</td><td>0.85</td><td>1</td><td>basketball_hoop</td><td>0.84</td></tr><tr><td class='left'>17250–17500</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.80</td><td>3</td><td>basketball_hoop</td><td>0.64</td><td>1</td><td>basketball_hoop</td><td>0.84</td><td>2</td><td>basketball_hoop</td><td>0.84</td></tr><tr><td class='left'>17500–17750</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.81</td><td>3</td><td>basketball_hoop</td><td>0.63</td><td>2</td><td>basketball_hoop</td><td>0.85</td><td>2</td><td>basketball_hoop</td><td>0.85</td></tr><tr><td class='left'>17750–18000</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.82</td><td>3</td><td>basketball_hoop</td><td>0.64</td><td>2</td><td>basketball_hoop</td><td>0.84</td><td>1</td><td>basketball_hoop</td><td>0.83</td></tr><tr><td class='left'>18000–18250</td><td>1</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.81</td><td>3</td><td>basketball_hoop</td><td>0.65</td><td>2</td><td>basketball_hoop</td><td>0.84</td><td>2</td><td>basketball_hoop</td><td>0.84</td></tr><tr><td class='left'>18250–18500</td><td>2</td><td>basketball</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.64</td><td>1</td><td>basketball_hoop</td><td>0.84</td><td>2</td><td>basketball_hoop</td><td>0.84</td></tr><tr><td class='left'>18500–18750</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.79</td><td>3</td><td>basketball_hoop</td><td>0.66</td><td>2</td><td>basketball_hoop</td><td>0.85</td><td>1</td><td>basketball_hoop</td><td>0.85</td></tr><tr><td class='left'>18750–19000</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.79</td><td>3</td><td>basketball_hoop</td><td>0.65</td><td>2</td><td>basketball_hoop</td><td>0.84</td><td>2</td><td>basketball_hoop</td><td>0.84</td></tr><tr><td class='left'>19000–19250</td><td>1</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.77</td><td>3</td><td>basketball</td><td>0.88</td><td>1</td><td>basketball</td><td>0.88</td><td>2</td><td>basketball_hoop</td><td>0.85</td></tr><tr><td class='left'>19250–19500</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.82</td><td>3</td><td>basketball_hoop</td><td>0.67</td><td>2</td><td>basketball_hoop</td><td>0.85</td><td>1</td><td>basketball_hoop</td><td>0.84</td></tr><tr><td class='left'>19500–19750</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.80</td><td>2</td><td>basketball</td><td>0.71</td><td>2</td><td>basketball_hoop</td><td>0.84</td><td>2</td><td>basketball_hoop</td><td>0.84</td></tr><tr><td class='left'>19750–20000</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.80</td><td>3</td><td>basketball_hoop</td><td>0.86</td><td>1</td><td>basketball_hoop</td><td>0.85</td><td>2</td><td>basketball</td><td>0.86</td></tr><tr><td class='left'>20000–20250</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.79</td><td>3</td><td>basketball_hoop</td><td>0.82</td><td>2</td><td>basketball</td><td>0.89</td><td>1</td><td>basketball</td><td>0.86</td></tr><tr><td class='left'>20250–20500</td><td>1</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.81</td><td>3</td><td>basketball_hoop</td><td>0.83</td><td>2</td><td>basketball_hoop</td><td>0.85</td><td>2</td><td>basketball</td><td>0.84</td></tr><tr><td class='left'>20500–20750</td><td>2</td><td>basketball</td><td>0.81</td><td>2</td><td>basketball_hoop</td><td>0.85</td><td>3</td><td>basketball_hoop</td><td>0.74</td><td>1</td><td>basketball</td><td>0.89</td><td>2</td><td>basketball_hoop</td><td>0.85</td></tr><tr><td class='left'>20750–21000</td><td>2</td><td>basketball</td><td>0.79</td><td>2</td><td>basketball_hoop</td><td>0.84</td><td>3</td><td>basketball_hoop</td><td>0.83</td><td>2</td><td>basketball_hoop</td><td>0.85</td><td>1</td><td>basketball_hoop</td><td>0.84</td></tr><tr><td class='left'>21000–21250</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.80</td><td>2</td><td>basketball</td><td>0.81</td><td>2</td><td>basketball_hoop</td><td>0.85</td><td>2</td><td>basketball_hoop</td><td>0.84</td></tr><tr><td class='left'>21250–21500</td><td>2</td><td>basketball</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.92</td><td>3</td><td>basketball_hoop</td><td>0.81</td><td>1</td><td>basketball</td><td>0.92</td><td>2</td><td>basketball</td><td>0.85</td></tr><tr><td class='left'>21500–21750</td><td>1</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.80</td><td>3</td><td>basketball_hoop</td><td>0.64</td><td>2</td><td>basketball_hoop</td><td>0.85</td><td>2</td><td>basketball_hoop</td><td>0.84</td></tr><tr><td class='left'>21750–22000</td><td>2</td><td>basketball_hoop</td><td>0.79</td><td>2</td><td>basketball_hoop</td><td>0.83</td><td>3</td><td>basketball_hoop</td><td>0.68</td><td>2</td><td>basketball_hoop</td><td>0.85</td><td>1</td><td>basketball_hoop</td><td>0.85</td></tr><tr><td class='left'>22000–22250</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.81</td><td>2</td><td>basketball_hoop</td><td>0.62</td><td>1</td><td>basketball_hoop</td><td>0.85</td><td>2</td><td>basketball_hoop</td><td>0.84</td></tr><tr><td class='left'>22250–22500</td><td>2</td><td>basketball_hoop</td><td>0.79</td><td>2</td><td>basketball_hoop</td><td>0.81</td><td>3</td><td>basketball_hoop</td><td>0.90</td><td>2</td><td>basketball_hoop</td><td>0.85</td><td>2</td><td>basketball_hoop</td><td>0.85</td></tr><tr><td class='left'>22500–22750</td><td>1</td><td>basketball_hoop</td><td>0.78</td><td>1</td><td>basketball_hoop</td><td>0.81</td><td>3</td><td>basketball_hoop</td><td>0.93</td><td>2</td><td>basketball</td><td>0.88</td><td>1</td><td>basketball</td><td>0.86</td></tr><tr><td class='left'>22750–23000</td><td>2</td><td>basketball_hoop</td><td>0.80</td><td>2</td><td>basketball_hoop</td><td>0.81</td><td>3</td><td>basketball_hoop</td><td>0.77</td><td>1</td><td>basketball_hoop</td><td>0.84</td><td>2</td><td>basketball</td><td>0.86</td></tr><tr><td class='left'>23000–23250</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.83</td><td>3</td><td>basketball_hoop</td><td>0.75</td><td>2</td><td>basketball</td><td>0.86</td><td>2</td><td>basketball_hoop</td><td>0.89</td></tr><tr><td class='left'>23250–23500</td><td>2</td><td>basketball_hoop</td><td>0.83</td><td>2</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball_hoop</td><td>0.82</td><td>1</td><td>basketball_hoop</td><td>0.84</td><td>1</td><td>basketball_hoop</td><td>0.84</td></tr><tr><td class='left'>23500–23750</td><td>1</td><td>basketball_hoop</td><td>0.78</td><td>1</td><td>basketball_hoop</td><td>0.82</td><td>3</td><td>basketball_hoop</td><td>0.83</td><td>2</td><td>basketball</td><td>0.88</td><td>2</td><td>basketball_hoop</td><td>0.85</td></tr><tr><td class='left'>23750–24000</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.82</td><td>3</td><td>basketball_hoop</td><td>0.71</td><td>2</td><td>basketball_hoop</td><td>0.84</td><td>2</td><td>basketball_hoop</td><td>0.84</td></tr><tr><td class='left'>24000–24250</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.82</td><td>3</td><td>basketball_hoop</td><td>0.66</td><td>1</td><td>basketball_hoop</td><td>0.84</td><td>1</td><td>basketball_hoop</td><td>0.84</td></tr><tr><td class='left'>24250–24500</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.82</td><td>3</td><td>basketball_hoop</td><td>0.65</td><td>2</td><td>basketball_hoop</td><td>0.84</td><td>2</td><td>basketball_hoop</td><td>0.84</td></tr><tr><td class='left'>24500–24750</td><td>1</td><td>basketball</td><td>0.81</td><td>2</td><td>basketball_hoop</td><td>0.90</td><td>3</td><td>basketball_hoop</td><td>0.89</td><td>2</td><td>basketball_hoop</td><td>0.84</td><td>2</td><td>basketball_hoop</td><td>0.84</td></tr><tr><td class='left'>24750–25000</td><td>2</td><td>basketball</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>3</td><td>basketball_hoop</td><td>0.90</td><td>2</td><td>basketball_hoop</td><td>0.86</td><td>1</td><td>basketball</td><td>0.88</td></tr><tr><td class='left'>25000–25250</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.80</td><td>2</td><td>basketball_hoop</td><td>0.67</td><td>1</td><td>basketball_hoop</td><td>0.84</td><td>2</td><td>basketball_hoop</td><td>0.84</td></tr><tr><td class='left'>25250–25500</td><td>2</td><td>basketball</td><td>0.83</td><td>2</td><td>basketball_hoop</td><td>0.90</td><td>3</td><td>basketball_hoop</td><td>0.79</td><td>2</td><td>basketball</td><td>0.86</td><td>2</td><td>basketball</td><td>0.92</td></tr><tr><td class='left'>25500–25750</td><td>2</td><td>basketball</td><td>0.85</td><td>2</td><td>basketball_hoop</td><td>0.81</td><td>3</td><td>basketball_hoop</td><td>0.89</td><td>2</td><td>basketball_hoop</td><td>0.94</td><td>1</td><td>basketball</td><td>0.86</td></tr><tr><td class='left'>25750–26000</td><td>1</td><td>basketball</td><td>0.80</td><td>2</td><td>basketball_hoop</td><td>0.90</td><td>3</td><td>basketball_hoop</td><td>0.83</td><td>1</td><td>basketball_hoop</td><td>0.84</td><td>2</td><td>basketball</td><td>0.84</td></tr><tr><td class='left'>26000–26250</td><td>2</td><td>basketball</td><td>0.81</td><td>2</td><td>basketball_hoop</td><td>0.90</td><td>3</td><td>basketball_hoop</td><td>0.76</td><td>2</td><td>basketball_hoop</td><td>0.84</td><td>2</td><td>basketball_hoop</td><td>0.85</td></tr><tr><td class='left'>26250–26500</td><td>2</td><td>basketball</td><td>0.75</td><td>2</td><td>basketball</td><td>0.83</td><td>3</td><td>basketball_hoop</td><td>0.89</td><td>2</td><td>basketball_hoop</td><td>0.77</td><td>1</td><td>basketball</td><td>0.87</td></tr><tr><td class='left'>26500–26750</td><td>2</td><td>basketball</td><td>0.86</td><td>2</td><td>basketball</td><td>0.87</td><td>2</td><td>basketball_hoop</td><td>0.92</td><td>1</td><td>basketball</td><td>0.88</td><td>2</td><td>basketball</td><td>0.89</td></tr><tr><td class='left'>26750–27000</td><td>1</td><td>basketball</td><td>0.81</td><td>2</td><td>basketball_hoop</td><td>0.90</td><td>3</td><td>basketball_hoop</td><td>0.81</td><td>2</td><td>basketball</td><td>0.89</td><td>1</td><td>basketball</td><td>0.88</td></tr><tr><td class='left'>27000–27250</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.85</td><td>3</td><td>basketball_hoop</td><td>0.76</td><td>2</td><td>basketball_hoop</td><td>0.86</td><td>2</td><td>basketball_hoop</td><td>0.86</td></tr><tr><td class='left'>27250–27500</td><td>2</td><td>basketball_hoop</td><td>0.79</td><td>2</td><td>basketball_hoop</td><td>0.86</td><td>3</td><td>basketball_hoop</td><td>0.93</td><td>1</td><td>basketball</td><td>0.91</td><td>1</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>27500–27750</td><td>2</td><td>basketball</td><td>0.82</td><td>2</td><td>basketball_hoop</td><td>0.89</td><td>3</td><td>basketball_hoop</td><td>0.93</td><td>2</td><td>basketball_hoop</td><td>0.83</td><td>2</td><td>basketball</td><td>0.84</td></tr><tr><td class='left'>27750–28000</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>1</td><td>basketball_hoop</td><td>0.92</td><td>3</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball</td><td>0.92</td><td>2</td><td>basketball</td><td>0.93</td></tr><tr><td class='left'>28000–28250</td><td>1</td><td>basketball_hoop</td><td>0.77</td><td>2</td><td>basketball_hoop</td><td>0.76</td><td>3</td><td>basketball_hoop</td><td>0.75</td><td>1</td><td>basketball_hoop</td><td>0.82</td><td>1</td><td>basketball_hoop</td><td>0.83</td></tr><tr><td class='left'>28250–28500</td><td>2</td><td>basketball</td><td>0.82</td><td>2</td><td>basketball_hoop</td><td>0.90</td><td>2</td><td>basketball_hoop</td><td>0.91</td><td>2</td><td>basketball_hoop</td><td>0.85</td><td>2</td><td>basketball_hoop</td><td>0.92</td></tr><tr><td class='left'>28500–28750</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.83</td><td>3</td><td>basketball_hoop</td><td>0.89</td><td>2</td><td>basketball_hoop</td><td>0.84</td><td>2</td><td>basketball_hoop</td><td>0.84</td></tr><tr><td class='left'>28750–29000</td><td>2</td><td>basketball</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.74</td><td>3</td><td>basketball</td><td>0.84</td><td>1</td><td>basketball_hoop</td><td>0.82</td><td>1</td><td>basketball</td><td>0.86</td></tr><tr><td class='left'>29000–29250</td><td>1</td><td>basketball</td><td>0.80</td><td>2</td><td>basketball_hoop</td><td>0.84</td><td>3</td><td>basketball_hoop</td><td>0.85</td><td>2</td><td>basketball_hoop</td><td>0.84</td><td>2</td><td>basketball</td><td>0.82</td></tr><tr><td class='left'>29250–29500</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.74</td><td>3</td><td>basketball_hoop</td><td>0.82</td><td>2</td><td>basketball_hoop</td><td>0.83</td><td>2</td><td>basketball_hoop</td><td>0.83</td></tr><tr><td class='left'>29500–29750</td><td>2</td><td>basketball_hoop</td><td>0.77</td><td>2</td><td>basketball_hoop</td><td>0.76</td><td>3</td><td>basketball_hoop</td><td>0.86</td><td>1</td><td>basketball_hoop</td><td>0.82</td><td>1</td><td>basketball_hoop</td><td>0.83</td></tr><tr><td class='left'>29750–30000</td><td>1</td><td>basketball_hoop</td><td>0.78</td><td>1</td><td>basketball_hoop</td><td>0.73</td><td>3</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.83</td><td>2</td><td>basketball_hoop</td><td>0.83</td></tr><tr><td class='left'>30000–30250</td><td>2</td><td>basketball_hoop</td><td>0.77</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>3</td><td>basketball_hoop</td><td>0.77</td><td>2</td><td>basketball_hoop</td><td>0.82</td><td>2</td><td>basketball_hoop</td><td>0.82</td></tr><tr><td class='left'>30250–30500</td><td>2</td><td>basketball</td><td>0.77</td><td>2</td><td>basketball_hoop</td><td>0.79</td><td>3</td><td>basketball_hoop</td><td>0.75</td><td>1</td><td>basketball_hoop</td><td>0.82</td><td>1</td><td>basketball_hoop</td><td>0.82</td></tr><tr><td class='left'>30500–30750</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.72</td><td>2</td><td>basketball_hoop</td><td>0.89</td><td>2</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>30750–31000</td><td>1</td><td>basketball</td><td>0.79</td><td>2</td><td>basketball_hoop</td><td>0.79</td><td>3</td><td>basketball_hoop</td><td>0.80</td><td>2</td><td>basketball</td><td>0.87</td><td>2</td><td>basketball</td><td>0.85</td></tr><tr><td class='left'>31000–31250</td><td>2</td><td>basketball_hoop</td><td>0.77</td><td>2</td><td>basketball_hoop</td><td>0.85</td><td>3</td><td>basketball_hoop</td><td>0.86</td><td>1</td><td>basketball_hoop</td><td>0.82</td><td>1</td><td>basketball_hoop</td><td>0.83</td></tr><tr><td class='left'>31250–31500</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.85</td><td>3</td><td>basketball_hoop</td><td>0.80</td><td>2</td><td>basketball</td><td>0.86</td><td>2</td><td>basketball</td><td>0.84</td></tr><tr><td class='left'>31500–31750</td><td>2</td><td>basketball_hoop</td><td>0.79</td><td>2</td><td>basketball_hoop</td><td>0.83</td><td>3</td><td>basketball_hoop</td><td>0.64</td><td>2</td><td>basketball_hoop</td><td>0.88</td><td>2</td><td>basketball_hoop</td><td>0.83</td></tr><tr><td class='left'>31750–32000</td><td>2</td><td>basketball_hoop</td><td>0.80</td><td>1</td><td>basketball_hoop</td><td>0.78</td><td>3</td><td>basketball_hoop</td><td>0.87</td><td>1</td><td>basketball_hoop</td><td>0.83</td><td>1</td><td>basketball</td><td>0.85</td></tr><tr><td class='left'>32000–32250</td><td>1</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.77</td><td>3</td><td>basketball_hoop</td><td>0.73</td><td>2</td><td>basketball_hoop</td><td>0.83</td><td>2</td><td>basketball_hoop</td><td>0.83</td></tr><tr><td class='left'>32250–32500</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.76</td><td>3</td><td>basketball_hoop</td><td>0.84</td><td>2</td><td>basketball_hoop</td><td>0.82</td><td>2</td><td>basketball_hoop</td><td>0.83</td></tr><tr><td class='left'>32500–32750</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.76</td><td>2</td><td>basketball_hoop</td><td>0.62</td><td>2</td><td>basketball_hoop</td><td>0.92</td><td>1</td><td>basketball_hoop</td><td>0.83</td></tr><tr><td class='left'>32750–33000</td><td>2</td><td>basketball</td><td>0.80</td><td>2</td><td>basketball_hoop</td><td>0.88</td><td>3</td><td>basketball_hoop</td><td>0.90</td><td>1</td><td>basketball</td><td>0.87</td><td>2</td><td>basketball</td><td>0.90</td></tr><tr><td class='left'>33000–33250</td><td>2</td><td>basketball</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.79</td><td>3</td><td>basketball_hoop</td><td>0.84</td><td>2</td><td>basketball_hoop</td><td>0.86</td><td>2</td><td>basketball_hoop</td><td>0.86</td></tr><tr><td class='left'>33250–33500</td><td>1</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.79</td><td>3</td><td>basketball_hoop</td><td>0.76</td><td>2</td><td>basketball_hoop</td><td>0.84</td><td>1</td><td>basketball</td><td>0.86</td></tr><tr><td class='left'>33500–33750</td><td>2</td><td>basketball</td><td>0.81</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>3</td><td>basketball_hoop</td><td>0.83</td><td>1</td><td>basketball_hoop</td><td>0.83</td><td>2</td><td>basketball</td><td>0.83</td></tr><tr><td class='left'>33750–34000</td><td>2</td><td>basketball_hoop</td><td>0.84</td><td>1</td><td>basketball_hoop</td><td>0.89</td><td>2</td><td>basketball_hoop</td><td>0.64</td><td>2</td><td>basketball_hoop</td><td>0.85</td><td>2</td><td>basketball_hoop</td><td>0.84</td></tr><tr><td class='left'>34000–34250</td><td>2</td><td>basketball_hoop</td><td>0.77</td><td>2</td><td>basketball_hoop</td><td>0.76</td><td>3</td><td>basketball_hoop</td><td>0.61</td><td>2</td><td>basketball_hoop</td><td>0.84</td><td>1</td><td>basketball_hoop</td><td>0.83</td></tr><tr><td class='left'>34250–34500</td><td>1</td><td>basketball_hoop</td><td>0.77</td><td>2</td><td>basketball_hoop</td><td>0.76</td><td>3</td><td>basketball_hoop</td><td>0.61</td><td>1</td><td>basketball_hoop</td><td>0.82</td><td>2</td><td>basketball_hoop</td><td>0.83</td></tr><tr><td class='left'>34500–34750</td><td>2</td><td>basketball_hoop</td><td>0.77</td><td>2</td><td>basketball_hoop</td><td>0.75</td><td>3</td><td>basketball_hoop</td><td>0.62</td><td>2</td><td>basketball_hoop</td><td>0.83</td><td>2</td><td>basketball_hoop</td><td>0.83</td></tr><tr><td class='left'>34750–35000</td><td>2</td><td>basketball_hoop</td><td>0.77</td><td>2</td><td>basketball_hoop</td><td>0.77</td><td>3</td><td>basketball_hoop</td><td>0.80</td><td>2</td><td>basketball_hoop</td><td>0.83</td><td>1</td><td>basketball_hoop</td><td>0.83</td></tr><tr><td class='left'>35000–35250</td><td>2</td><td>basketball</td><td>0.82</td><td>2</td><td>basketball_hoop</td><td>0.91</td><td>3</td><td>basketball_hoop</td><td>0.87</td><td>1</td><td>basketball_hoop</td><td>0.83</td><td>2</td><td>basketball_hoop</td><td>0.89</td></tr><tr><td class='left'>35250–35500</td><td>1</td><td>basketball</td><td>0.79</td><td>2</td><td>basketball_hoop</td><td>0.73</td><td>3</td><td>basketball_hoop</td><td>0.64</td><td>2</td><td>basketball_hoop</td><td>0.89</td><td>2</td><td>basketball_hoop</td><td>0.83</td></tr><tr><td class='left'>35500–35750</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>3</td><td>basketball_hoop</td><td>0.80</td><td>2</td><td>basketball_hoop</td><td>0.93</td><td>2</td><td>basketball_hoop</td><td>0.83</td></tr><tr><td class='left'>35750–36000</td><td>2</td><td>basketball_hoop</td><td>0.80</td><td>1</td><td>basketball_hoop</td><td>0.75</td><td>2</td><td>basketball_hoop</td><td>0.86</td><td>1</td><td>basketball_hoop</td><td>0.82</td><td>1</td><td>basketball</td><td>0.90</td></tr><tr><td class='left'>36000–36250</td><td>2</td><td>basketball</td><td>0.85</td><td>2</td><td>basketball_hoop</td><td>0.93</td><td>3</td><td>basketball_hoop</td><td>0.83</td><td>2</td><td>basketball_hoop</td><td>0.89</td><td>2</td><td>basketball</td><td>0.89</td></tr><tr><td class='left'>36250–36500</td><td>2</td><td>basketball</td><td>0.85</td><td>2</td><td>basketball_hoop</td><td>0.79</td><td>3</td><td>basketball_hoop</td><td>0.89</td><td>2</td><td>basketball_hoop</td><td>0.85</td><td>2</td><td>basketball_hoop</td><td>0.83</td></tr><tr><td class='left'>36500–36750</td><td>1</td><td>basketball_hoop</td><td>0.77</td><td>2</td><td>basketball_hoop</td><td>0.89</td><td>3</td><td>basketball_hoop</td><td>0.91</td><td>2</td><td>basketball_hoop</td><td>0.86</td><td>1</td><td>basketball_hoop</td><td>0.83</td></tr><tr><td class='left'>36750–37000</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball</td><td>0.80</td><td>3</td><td>basketball_hoop</td><td>0.85</td><td>1</td><td>basketball</td><td>0.85</td><td>2</td><td>basketball_hoop</td><td>0.86</td></tr><tr><td class='left'>37000–37250</td><td>2</td><td>basketball_hoop</td><td>0.77</td><td>2</td><td>basketball_hoop</td><td>0.79</td><td>3</td><td>basketball_hoop</td><td>0.84</td><td>2</td><td>basketball</td><td>0.91</td><td>2</td><td>basketball</td><td>0.88</td></tr><tr><td class='left'>37250–37500</td><td>2</td><td>basketball</td><td>0.80</td><td>2</td><td>basketball_hoop</td><td>0.89</td><td>3</td><td>basketball_hoop</td><td>0.85</td><td>1</td><td>basketball_hoop</td><td>0.87</td><td>1</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>37500–37750</td><td>1</td><td>basketball</td><td>0.81</td><td>2</td><td>basketball_hoop</td><td>0.92</td><td>3</td><td>basketball_hoop</td><td>0.89</td><td>2</td><td>basketball</td><td>0.93</td><td>2</td><td>basketball</td><td>0.93</td></tr><tr><td class='left'>37750–38000</td><td>2</td><td>basketball</td><td>0.81</td><td>1</td><td>basketball_hoop</td><td>0.90</td><td>2</td><td>basketball_hoop</td><td>0.84</td><td>2</td><td>basketball</td><td>0.92</td><td>2</td><td>basketball</td><td>0.92</td></tr><tr><td class='left'>38000–38250</td><td>2</td><td>basketball_hoop</td><td>0.83</td><td>2</td><td>basketball_hoop</td><td>0.79</td><td>3</td><td>basketball_hoop</td><td>0.80</td><td>1</td><td>basketball</td><td>0.86</td><td>1</td><td>basketball_hoop</td><td>0.86</td></tr><tr><td class='left'>38250–38500</td><td>2</td><td>basketball</td><td>0.82</td><td>2</td><td>basketball_hoop</td><td>0.91</td><td>3</td><td>basketball_hoop</td><td>0.71</td><td>2</td><td>basketball_hoop</td><td>0.90</td><td>2</td><td>basketball_hoop</td><td>0.89</td></tr><tr><td class='left'>38500–38750</td><td>1</td><td>basketball_hoop</td><td>0.77</td><td>2</td><td>basketball_hoop</td><td>0.88</td><td>3</td><td>basketball_hoop</td><td>0.89</td><td>2</td><td>basketball</td><td>0.87</td><td>2</td><td>basketball</td><td>0.88</td></tr><tr><td class='left'>38750–39000</td><td>2</td><td>basketball_hoop</td><td>0.80</td><td>2</td><td>basketball_hoop</td><td>0.83</td><td>3</td><td>basketball_hoop</td><td>0.87</td><td>1</td><td>basketball</td><td>0.94</td><td>1</td><td>basketball</td><td>0.93</td></tr><tr><td class='left'>39000–39250</td><td>2</td><td>basketball</td><td>0.80</td><td>2</td><td>basketball_hoop</td><td>0.86</td><td>3</td><td>basketball_hoop</td><td>0.72</td><td>2</td><td>basketball</td><td>0.86</td><td>2</td><td>basketball</td><td>0.87</td></tr><tr><td class='left'>39250–39500</td><td>2</td><td>basketball_hoop</td><td>0.77</td><td>2</td><td>basketball_hoop</td><td>0.79</td><td>3</td><td>basketball_hoop</td><td>0.77</td><td>2</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>39500–39750</td><td>1</td><td>basketball_hoop</td><td>0.77</td><td>2</td><td>basketball_hoop</td><td>0.79</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>1</td><td>basketball</td><td>0.87</td><td>1</td><td>basketball</td><td>0.87</td></tr><tr><td class='left'>39750–40000</td><td>2</td><td>basketball_hoop</td><td>0.77</td><td>1</td><td>basketball_hoop</td><td>0.90</td><td>3</td><td>basketball_hoop</td><td>0.81</td><td>2</td><td>basketball</td><td>0.89</td><td>2</td><td>basketball</td><td>0.89</td></tr><tr><td class='left'>40000–40250</td><td>1</td><td>basketball_hoop</td><td>0.77</td><td>2</td><td>basketball_hoop</td><td>0.77</td><td>3</td><td>basketball_hoop</td><td>0.89</td><td>2</td><td>basketball</td><td>0.86</td><td>2</td><td>basketball</td><td>0.87</td></tr><tr><td class='left'>40250–40500</td><td>2</td><td>basketball_hoop</td><td>0.77</td><td>2</td><td>basketball_hoop</td><td>0.79</td><td>3</td><td>basketball_hoop</td><td>0.67</td><td>1</td><td>basketball_hoop</td><td>0.85</td><td>1</td><td>basketball_hoop</td><td>0.86</td></tr><tr><td class='left'>40500–40750</td><td>2</td><td>basketball_hoop</td><td>0.77</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>3</td><td>basketball_hoop</td><td>0.65</td><td>2</td><td>basketball_hoop</td><td>0.86</td><td>2</td><td>basketball_hoop</td><td>0.86</td></tr><tr><td class='left'>40750–41000</td><td>1</td><td>basketball_hoop</td><td>0.77</td><td>2</td><td>basketball_hoop</td><td>0.77</td><td>3</td><td>basketball_hoop</td><td>0.65</td><td>1</td><td>basketball_hoop</td><td>0.85</td><td>2</td><td>basketball_hoop</td><td>0.86</td></tr><tr><td class='left'>41000–41250</td><td>2</td><td>basketball_hoop</td><td>0.77</td><td>2</td><td>basketball_hoop</td><td>0.76</td><td>3</td><td>basketball_hoop</td><td>0.65</td><td>2</td><td>basketball_hoop</td><td>0.86</td><td>1</td><td>basketball_hoop</td><td>0.86</td></tr><tr><td class='left'>41250–41500</td><td>2</td><td>basketball_hoop</td><td>0.77</td><td>2</td><td>basketball_hoop</td><td>0.79</td><td>3</td><td>basketball_hoop</td><td>0.66</td><td>2</td><td>basketball_hoop</td><td>0.86</td><td>2</td><td>basketball_hoop</td><td>0.86</td></tr><tr><td class='left'>41500–41750</td><td>2</td><td>basketball_hoop</td><td>0.77</td><td>2</td><td>basketball_hoop</td><td>0.86</td><td>2</td><td>basketball_hoop</td><td>0.76</td><td>1</td><td>basketball_hoop</td><td>0.85</td><td>1</td><td>basketball_hoop</td><td>0.86</td></tr><tr><td class='left'>41750–42000</td><td>1</td><td>basketball_hoop</td><td>0.77</td><td>1</td><td>basketball_hoop</td><td>0.74</td><td>3</td><td>basketball_hoop</td><td>0.66</td><td>2</td><td>basketball_hoop</td><td>0.86</td><td>2</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>42000–42250</td><td>2</td><td>basketball_hoop</td><td>0.79</td><td>2</td><td>basketball_hoop</td><td>0.88</td><td>3</td><td>basketball_hoop</td><td>0.82</td><td>1</td><td>basketball_hoop</td><td>0.86</td><td>2</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>42250–42500</td><td>2</td><td>basketball</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.79</td><td>3</td><td>basketball_hoop</td><td>0.65</td><td>2</td><td>basketball_hoop</td><td>0.86</td><td>1</td><td>basketball_hoop</td><td>0.86</td></tr><tr><td class='left'>42500–42750</td><td>2</td><td>basketball</td><td>0.82</td><td>2</td><td>basketball_hoop</td><td>0.77</td><td>3</td><td>basketball_hoop</td><td>0.91</td><td>2</td><td>basketball</td><td>0.87</td><td>2</td><td>basketball_hoop</td><td>0.89</td></tr><tr><td class='left'>42750–43000</td><td>2</td><td>basketball_hoop</td><td>0.77</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>3</td><td>basketball_hoop</td><td>0.66</td><td>1</td><td>basketball_hoop</td><td>0.86</td><td>2</td><td>basketball_hoop</td><td>0.86</td></tr><tr><td class='left'>43000–43250</td><td>1</td><td>basketball_hoop</td><td>0.76</td><td>2</td><td>basketball_hoop</td><td>0.88</td><td>3</td><td>basketball_hoop</td><td>0.73</td><td>2</td><td>basketball_hoop</td><td>0.86</td><td>1</td><td>basketball_hoop</td><td>0.86</td></tr><tr><td class='left'>43250–43500</td><td>2</td><td>basketball</td><td>0.85</td><td>2</td><td>basketball_hoop</td><td>0.87</td><td>3</td><td>basketball_hoop</td><td>0.89</td><td>2</td><td>basketball</td><td>0.94</td><td>2</td><td>basketball</td><td>0.86</td></tr><tr><td class='left'>43500–43750</td><td>2</td><td>basketball</td><td>0.88</td><td>2</td><td>basketball_hoop</td><td>0.92</td><td>3</td><td>basketball_hoop</td><td>0.86</td><td>1</td><td>basketball_hoop</td><td>0.85</td><td>2</td><td>basketball</td><td>0.92</td></tr><tr><td class='left'>43750–44000</td><td>1</td><td>basketball</td><td>0.82</td><td>1</td><td>basketball_hoop</td><td>0.90</td><td>2</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball</td><td>0.92</td><td>1</td><td>basketball_hoop</td><td>0.86</td></tr><tr><td class='left'>44000–44250</td><td>2</td><td>basketball_hoop</td><td>0.79</td><td>2</td><td>basketball_hoop</td><td>0.89</td><td>3</td><td>basketball_hoop</td><td>0.91</td><td>2</td><td>basketball_hoop</td><td>0.88</td><td>2</td><td>basketball_hoop</td><td>0.86</td></tr><tr><td class='left'>44250–44500</td><td>2</td><td>basketball_hoop</td><td>0.75</td><td>2</td><td>basketball_hoop</td><td>0.81</td><td>3</td><td>basketball_hoop</td><td>0.75</td><td>1</td><td>basketball_hoop</td><td>0.85</td><td>2</td><td>basketball_hoop</td><td>0.86</td></tr><tr><td class='left'>44500–44750</td><td>2</td><td>basketball</td><td>0.81</td><td>2</td><td>basketball_hoop</td><td>0.91</td><td>3</td><td>basketball_hoop</td><td>0.91</td><td>2</td><td>basketball</td><td>0.87</td><td>1</td><td>basketball_hoop</td><td>0.82</td></tr><tr><td class='left'>44750–45000</td><td>1</td><td>basketball_hoop</td><td>0.77</td><td>2</td><td>basketball_hoop</td><td>0.91</td><td>3</td><td>basketball_hoop</td><td>0.74</td><td>1</td><td>basketball_hoop</td><td>0.88</td><td>2</td><td>basketball</td><td>0.91</td></tr><tr><td class='left'>45000–45250</td><td>2</td><td>basketball_hoop</td><td>0.84</td><td>2</td><td>basketball_hoop</td><td>0.89</td><td>3</td><td>basketball_hoop</td><td>0.89</td><td>2</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball_hoop</td><td>0.90</td></tr><tr><td class='left'>45250–45500</td><td>2</td><td>basketball</td><td>0.83</td><td>2</td><td>basketball_hoop</td><td>0.92</td><td>2</td><td>basketball_hoop</td><td>0.89</td><td>2</td><td>basketball</td><td>0.94</td><td>2</td><td>basketball</td><td>0.94</td></tr><tr><td class='left'>45500–45750</td><td>1</td><td>basketball</td><td>0.85</td><td>1</td><td>basketball_hoop</td><td>0.87</td><td>3</td><td>basketball_hoop</td><td>0.88</td><td>1</td><td>basketball</td><td>0.90</td><td>1</td><td>basketball</td><td>0.93</td></tr><tr><td class='left'>45750–46000</td><td>2</td><td>basketball</td><td>0.80</td><td>2</td><td>basketball_hoop</td><td>0.84</td><td>3</td><td>basketball_hoop</td><td>0.88</td><td>2</td><td>basketball</td><td>0.91</td><td>2</td><td>basketball</td><td>0.92</td></tr><tr><td class='left'>46000–46250</td><td>2</td><td>basketball</td><td>0.85</td><td>2</td><td>basketball_hoop</td><td>0.88</td><td>3</td><td>basketball_hoop</td><td>0.88</td><td>2</td><td>basketball</td><td>0.93</td><td>2</td><td>basketball_hoop</td><td>0.94</td></tr><tr><td class='left'>46250–46500</td><td>2</td><td>basketball</td><td>0.86</td><td>2</td><td>basketball_hoop</td><td>0.86</td><td>3</td><td>basketball_hoop</td><td>0.88</td><td>1</td><td>basketball_hoop</td><td>0.87</td><td>1</td><td>basketball_hoop</td><td>0.88</td></tr><tr><td class='left'>46500–46750</td><td>1</td><td>basketball</td><td>0.82</td><td>1</td><td>basketball_hoop</td><td>0.76</td><td>3</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball</td><td>0.89</td><td>2</td><td>basketball</td><td>0.93</td></tr><tr><td class='left'>46750–47000</td><td>2</td><td>basketball</td><td>0.79</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>3</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>47000–47250</td><td>2</td><td>basketball</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.76</td><td>3</td><td>basketball</td><td>0.69</td><td>1</td><td>basketball_hoop</td><td>0.87</td><td>1</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>47250–47500</td><td>1</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.73</td><td>3</td><td>basketball_hoop</td><td>0.79</td><td>2</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball_hoop</td><td>0.88</td></tr><tr><td class='left'>47500–47750</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.83</td><td>3</td><td>basketball_hoop</td><td>0.86</td><td>2</td><td>basketball_hoop</td><td>0.88</td><td>2</td><td>basketball_hoop</td><td>0.92</td></tr><tr><td class='left'>47750–48000</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.89</td><td>2</td><td>basketball_hoop</td><td>0.87</td><td>1</td><td>basketball</td><td>0.90</td><td>1</td><td>basketball</td><td>0.92</td></tr><tr><td class='left'>48000–48250</td><td>2</td><td>basketball</td><td>0.79</td><td>2</td><td>basketball_hoop</td><td>0.80</td><td>3</td><td>basketball_hoop</td><td>0.67</td><td>2</td><td>basketball_hoop</td><td>0.89</td><td>2</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>48250–48500</td><td>1</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.89</td><td>3</td><td>basketball_hoop</td><td>0.70</td><td>2</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>48500–48750</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.76</td><td>3</td><td>basketball_hoop</td><td>0.74</td><td>1</td><td>basketball_hoop</td><td>0.88</td><td>1</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>48750–49000</td><td>2</td><td>basketball</td><td>0.83</td><td>2</td><td>basketball_hoop</td><td>0.91</td><td>3</td><td>basketball_hoop</td><td>0.90</td><td>2</td><td>basketball</td><td>0.88</td><td>2</td><td>basketball</td><td>0.87</td></tr><tr><td class='left'>49000–49250</td><td>1</td><td>basketball</td><td>0.82</td><td>2</td><td>basketball_hoop</td><td>0.80</td><td>3</td><td>basketball_hoop</td><td>0.91</td><td>2</td><td>basketball</td><td>0.89</td><td>2</td><td>basketball_hoop</td><td>0.89</td></tr><tr><td class='left'>49250–49500</td><td>2</td><td>basketball</td><td>0.83</td><td>1</td><td>basketball_hoop</td><td>0.78</td><td>3</td><td>basketball_hoop</td><td>0.85</td><td>1</td><td>basketball_hoop</td><td>0.87</td><td>1</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>49500–49750</td><td>2</td><td>basketball</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.83</td><td>3</td><td>basketball_hoop</td><td>0.86</td><td>2</td><td>basketball</td><td>0.88</td><td>2</td><td>basketball</td><td>0.89</td></tr><tr><td class='left'>49750–50000</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.72</td><td>3</td><td>basketball_hoop</td><td>0.66</td><td>2</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>50000–50250</td><td>1</td><td>basketball</td><td>0.80</td><td>2</td><td>basketball_hoop</td><td>0.93</td><td>3</td><td>basketball_hoop</td><td>0.92</td><td>1</td><td>basketball</td><td>0.87</td><td>2</td><td>basketball_hoop</td><td>0.88</td></tr><tr><td class='left'>50250–50500</td><td>2</td><td>basketball</td><td>0.86</td><td>2</td><td>basketball_hoop</td><td>0.95</td><td>3</td><td>basketball_hoop</td><td>0.91</td><td>2</td><td>basketball</td><td>0.89</td><td>1</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>50500–50750</td><td>2</td><td>basketball</td><td>0.83</td><td>2</td><td>basketball_hoop</td><td>0.80</td><td>3</td><td>basketball_hoop</td><td>0.88</td><td>2</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball</td><td>0.91</td></tr><tr><td class='left'>50750–51000</td><td>1</td><td>basketball</td><td>0.86</td><td>2</td><td>basketball_hoop</td><td>0.89</td><td>2</td><td>basketball_hoop</td><td>0.81</td><td>1</td><td>basketball</td><td>0.88</td><td>2</td><td>basketball</td><td>0.88</td></tr><tr><td class='left'>51000–51250</td><td>2</td><td>basketball_hoop</td><td>0.75</td><td>2</td><td>basketball_hoop</td><td>0.76</td><td>3</td><td>basketball_hoop</td><td>0.79</td><td>2</td><td>basketball_hoop</td><td>0.85</td><td>1</td><td>basketball_hoop</td><td>0.85</td></tr><tr><td class='left'>51250–51500</td><td>2</td><td>basketball_hoop</td><td>0.81</td><td>1</td><td>basketball_hoop</td><td>0.67</td><td>3</td><td>basketball_hoop</td><td>0.93</td><td>1</td><td>basketball</td><td>0.84</td><td>2</td><td>basketball_hoop</td><td>0.88</td></tr><tr><td class='left'>51500–51750</td><td>1</td><td>basketball</td><td>0.80</td><td>2</td><td>basketball_hoop</td><td>0.90</td><td>3</td><td>basketball_hoop</td><td>0.92</td><td>2</td><td>basketball</td><td>0.91</td><td>2</td><td>basketball</td><td>0.90</td></tr><tr><td class='left'>51750–52000</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.81</td><td>3</td><td>basketball_hoop</td><td>0.80</td><td>2</td><td>basketball</td><td>0.91</td><td>1</td><td>basketball</td><td>0.85</td></tr><tr><td class='left'>52000–52250</td><td>2</td><td>basketball</td><td>0.81</td><td>2</td><td>basketball_hoop</td><td>0.83</td><td>2</td><td>basketball_hoop</td><td>0.84</td><td>1</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball</td><td>0.93</td></tr><tr><td class='left'>52250–52500</td><td>2</td><td>basketball</td><td>0.85</td><td>2</td><td>basketball_hoop</td><td>0.91</td><td>3</td><td>basketball_hoop</td><td>0.83</td><td>2</td><td>basketball</td><td>0.90</td><td>2</td><td>basketball_hoop</td><td>0.88</td></tr><tr><td class='left'>52500–52750</td><td>1</td><td>basketball</td><td>0.82</td><td>2</td><td>basketball_hoop</td><td>0.93</td><td>3</td><td>basketball_hoop</td><td>0.88</td><td>2</td><td>basketball_hoop</td><td>0.87</td><td>1</td><td>basketball</td><td>0.88</td></tr><tr><td class='left'>52750–53000</td><td>2</td><td>basketball</td><td>0.81</td><td>2</td><td>basketball_hoop</td><td>0.87</td><td>3</td><td>basketball_hoop</td><td>0.85</td><td>1</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball</td><td>0.91</td></tr><tr><td class='left'>53000–53250</td><td>2</td><td>basketball</td><td>0.79</td><td>2</td><td>basketball_hoop</td><td>0.89</td><td>3</td><td>basketball_hoop</td><td>0.89</td><td>2</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball</td><td>0.91</td></tr><tr><td class='left'>53250–53500</td><td>2</td><td>basketball</td><td>0.83</td><td>1</td><td>basketball_hoop</td><td>0.84</td><td>3</td><td>basketball_hoop</td><td>0.86</td><td>2</td><td>basketball</td><td>0.90</td><td>1</td><td>basketball_hoop</td><td>0.88</td></tr><tr><td class='left'>53500–53750</td><td>1</td><td>basketball</td><td>0.79</td><td>2</td><td>basketball_hoop</td><td>0.80</td><td>3</td><td>basketball_hoop</td><td>0.89</td><td>1</td><td>basketball_hoop</td><td>0.86</td><td>2</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>53750–54000</td><td>2</td><td>basketball_hoop</td><td>0.81</td><td>2</td><td>basketball_hoop</td><td>0.88</td><td>3</td><td>basketball_hoop</td><td>0.90</td><td>2</td><td>basketball</td><td>0.92</td><td>2</td><td>basketball_hoop</td><td>0.89</td></tr><tr><td class='left'>54000–54250</td><td>2</td><td>basketball_hoop</td><td>0.81</td><td>2</td><td>basketball_hoop</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.86</td><td>2</td><td>basketball</td><td>0.87</td><td>1</td><td>basketball</td><td>0.92</td></tr><tr><td class='left'>54250–54500</td><td>1</td><td>basketball_hoop</td><td>0.77</td><td>2</td><td>basketball_hoop</td><td>0.79</td><td>3</td><td>basketball_hoop</td><td>0.73</td><td>1</td><td>basketball_hoop</td><td>0.86</td><td>2</td><td>basketball</td><td>0.89</td></tr><tr><td class='left'>54500–54750</td><td>2</td><td>basketball_hoop</td><td>0.77</td><td>2</td><td>basketball_hoop</td><td>0.83</td><td>3</td><td>basketball_hoop</td><td>0.85</td><td>2</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball_hoop</td><td>0.91</td></tr><tr><td class='left'>54750–55000</td><td>2</td><td>basketball</td><td>0.77</td><td>2</td><td>basketball_hoop</td><td>0.81</td><td>3</td><td>basketball_hoop</td><td>0.75</td><td>2</td><td>basketball_hoop</td><td>0.87</td><td>1</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>55000–55250</td><td>1</td><td>basketball_hoop</td><td>0.77</td><td>2</td><td>basketball_hoop</td><td>0.76</td><td>3</td><td>basketball_hoop</td><td>0.72</td><td>1</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>55250–55500</td><td>2</td><td>basketball_hoop</td><td>0.77</td><td>1</td><td>basketball_hoop</td><td>0.77</td><td>3</td><td>basketball_hoop</td><td>0.91</td><td>2</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball_hoop</td><td>0.86</td></tr><tr><td class='left'>55500–55750</td><td>2</td><td>basketball_hoop</td><td>0.77</td><td>2</td><td>basketball_hoop</td><td>0.80</td><td>3</td><td>basketball_hoop</td><td>0.66</td><td>2</td><td>basketball_hoop</td><td>0.87</td><td>1</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>55750–56000</td><td>1</td><td>basketball_hoop</td><td>0.77</td><td>2</td><td>basketball_hoop</td><td>0.79</td><td>3</td><td>basketball_hoop</td><td>0.70</td><td>1</td><td>basketball_hoop</td><td>0.86</td><td>2</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>56000–56250</td><td>2</td><td>basketball</td><td>0.79</td><td>2</td><td>basketball_hoop</td><td>0.81</td><td>2</td><td>basketball_hoop</td><td>0.75</td><td>2</td><td>basketball_hoop</td><td>0.86</td><td>2</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>56250–56500</td><td>2</td><td>basketball</td><td>0.79</td><td>2</td><td>basketball_hoop</td><td>0.79</td><td>3</td><td>basketball_hoop</td><td>0.70</td><td>2</td><td>basketball_hoop</td><td>0.86</td><td>1</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>56500–56750</td><td>2</td><td>basketball</td><td>0.82</td><td>2</td><td>basketball_hoop</td><td>0.89</td><td>3</td><td>basketball_hoop</td><td>0.84</td><td>1</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>56750–57000</td><td>1</td><td>basketball_hoop</td><td>0.77</td><td>2</td><td>basketball_hoop</td><td>0.77</td><td>3</td><td>basketball_hoop</td><td>0.77</td><td>2</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>57000–57250</td><td>2</td><td>basketball_hoop</td><td>0.79</td><td>2</td><td>basketball_hoop</td><td>0.79</td><td>3</td><td>basketball_hoop</td><td>0.91</td><td>2</td><td>basketball_hoop</td><td>0.86</td><td>1</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>57250–57500</td><td>2</td><td>basketball</td><td>0.87</td><td>2</td><td>basketball_hoop</td><td>0.92</td><td>2</td><td>basketball_hoop</td><td>0.83</td><td>1</td><td>basketball</td><td>0.88</td><td>2</td><td>basketball_hoop</td><td>0.92</td></tr><tr><td class='left'>57500–57750</td><td>1</td><td>basketball</td><td>0.82</td><td>1</td><td>basketball_hoop</td><td>0.77</td><td>3</td><td>basketball_hoop</td><td>0.88</td><td>2</td><td>basketball</td><td>0.87</td><td>2</td><td>basketball</td><td>0.92</td></tr><tr><td class='left'>57750–58000</td><td>2</td><td>basketball</td><td>0.83</td><td>2</td><td>basketball_hoop</td><td>0.91</td><td>3</td><td>basketball_hoop</td><td>0.92</td><td>2</td><td>basketball_hoop</td><td>0.87</td><td>1</td><td>basketball_hoop</td><td>0.86</td></tr><tr><td class='left'>58000–58250</td><td>2</td><td>basketball_hoop</td><td>0.71</td><td>2</td><td>basketball_hoop</td><td>0.86</td><td>3</td><td>basketball_hoop</td><td>0.73</td><td>1</td><td>basketball_hoop</td><td>0.73</td><td>2</td><td>basketball_hoop</td><td>0.71</td></tr><tr><td class='left'>58250–58500</td><td>1</td><td>basketball_hoop</td><td>0.73</td><td>2</td><td>basketball_hoop</td><td>0.87</td><td>3</td><td>basketball_hoop</td><td>0.83</td><td>2</td><td>basketball_hoop</td><td>0.88</td><td>2</td><td>basketball_hoop</td><td>0.88</td></tr><tr><td class='left'>58500–58750</td><td>2</td><td>basketball_hoop</td><td>0.79</td><td>2</td><td>basketball_hoop</td><td>0.80</td><td>2</td><td>basketball_hoop</td><td>0.88</td><td>2</td><td>basketball_hoop</td><td>0.88</td><td>2</td><td>basketball_hoop</td><td>0.88</td></tr><tr><td class='left'>58750–59000</td><td>2</td><td>basketball_hoop</td><td>0.82</td><td>2</td><td>basketball_hoop</td><td>0.82</td><td>3</td><td>basketball_hoop</td><td>0.79</td><td>2</td><td>basketball_hoop</td><td>0.89</td><td>1</td><td>basketball_hoop</td><td>0.89</td></tr><tr><td class='left'>59000–59250</td><td>1</td><td>basketball</td><td>0.82</td><td>2</td><td>basketball_hoop</td><td>0.80</td><td>2</td><td>basketball_hoop</td><td>0.88</td><td>1</td><td>basketball_hoop</td><td>0.88</td><td>2</td><td>basketball_hoop</td><td>0.87</td></tr><tr><td class='left'>59250–59500</td><td>2</td><td>basketball</td><td>0.78</td><td>2</td><td>basketball_hoop</td><td>0.82</td><td>3</td><td>basketball_hoop</td><td>0.85</td><td>2</td><td>basketball_hoop</td><td>0.87</td><td>2</td><td>basketball_hoop</td><td>0.88</td></tr><tr><td class='left'>59500–59750</td><td>2</td><td>basketball</td><td>0.79</td><td>2</td><td>basketball_hoop</td><td>0.89</td><td>3</td><td>basketball_hoop</td><td>0.91</td><td>1</td><td>basketball</td><td>0.94</td><td>1</td><td>basketball_hoop</td><td>0.95</td></tr><tr><td class='left'>59750–60000</td><td>1</td><td>basketball</td><td>0.82</td><td>1</td><td>basketball_hoop</td><td>0.76</td><td>1</td><td>basketball_hoop</td><td>0.89</td><td>2</td><td>basketball</td><td>0.87</td><td>1</td><td>basketball</td><td>0.87</td></tr></table><h2>Per-class confidence timelines</h2><img alt='basketball timeline' src='data:image/png;base64,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alt='basketball_hoop timeline' 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'/></body></html>

+6

reports/summary.csv

Reviewed

··· 1 1 + run_id,model_tag,device_label,duration_s,total_events,events_per_second,detection_rate,mean_confidence,class_diversity,classes,mean_bbox_area 2 2 + 1775898625937_yolo11n_dataset_dataset,yolo11n_dataset_dataset,samsung SM-A366B,60.00,419,6.983,1.0000,0.7191,2,basketball|basketball_hoop,531.3 3 3 + 1775898703073_yolo26n_dataset_v11_dataset_v11_20260410_201918__best_float32,yolo26n_dataset_v11_dataset_v11_20260410_201918__best_float32,samsung SM-A366B,60.00,461,7.683,1.0000,0.6393,2,basketball|basketball_hoop,523.3 4 4 + 1775898781050_yolo26n_v11_rect_384x288_fp16_20260411_083350__best_float16,yolo26n_v11_rect_384x288_fp16_20260411_083350__best_float16,samsung SM-A366B,60.00,678,11.300,1.0000,0.5896,2,basketball|basketball_hoop,570.4 5 5 + 1775898858800_yolo26n_v11_rect_512x384_fp32_20260411_090924__best_float32,yolo26n_v11_rect_512x384_fp32_20260411_090924__best_float32,samsung SM-A366B,60.00,402,6.700,1.0000,0.7956,2,basketball|basketball_hoop,535.5 6 6 + 1775898936159_yolo26n_v11_rect_512x384_fp16_20260411_090924__best_float16,yolo26n_v11_rect_512x384_fp16_20260411_090924__best_float16,samsung SM-A366B,60.00,404,6.733,1.0000,0.7766,2,basketball|basketball_hoop,533.3

+1

sample/composeApp/src/androidMain/AndroidManifest.xml

Reviewed

··· 14 14 android:configChanges="orientation|screenSize|screenLayout|keyboardHidden" 15 15 android:exported="true" 16 16 android:launchMode="singleInstance" 17 17 + android:screenOrientation="landscape" 17 18 android:windowSoftInputMode="adjustPan"> 18 19 <intent-filter> 19 20 <action android:name="android.intent.action.MAIN" />

sample/composeApp/src/androidMain/assets/yolo11n_dataset640_20260410_123550__best_float32.tflite

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sample/composeApp/src/androidMain/assets/yolo11n_dataset_lb416_20260410_145848__best_float32.tflite

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sample/composeApp/src/androidMain/assets/yolo11n_dataset_v10_20260410_185942__best_float32.tflite

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sample/composeApp/src/androidMain/assets/yolo11n_dataset_v11_dataset_v11_20260410_201918__best_float32.tflite

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sample/composeApp/src/androidMain/assets/yolo11s_dataset_v11_dataset_v11_20260410_201918__best_float32.tflite

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sample/composeApp/src/androidMain/assets/yolo26n_dataset_v11_dataset_v11_20260410_201918__best_float32.tflite

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sample/composeApp/src/androidMain/assets/yolo26n_v11_320_fp16_20260410_215716__best_float16.tflite

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sample/composeApp/src/androidMain/assets/yolo26n_v11_512_fp16_20260410_211853__best_float16.tflite

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sample/composeApp/src/androidMain/assets/yolo26n_v11_rect_384x288_fp16_20260411_075952__best_float16.tflite

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sample/composeApp/src/androidMain/assets/yolo26n_v11_rect_512x384_fp16_20260411_090924__best_float16.tflite

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+35 -1

sample/composeApp/src/androidMain/kotlin/com/nate/posedetection/App.android.kt

Reviewed

··· 1 1 package com.nate.posedetection 2 2 3 3 + import android.content.Intent 3 4 import android.os.Bundle 4 5 import androidx.activity.ComponentActivity 5 6 import androidx.activity.compose.setContent 6 7 import androidx.activity.enableEdgeToEdge 7 8 import androidx.compose.runtime.Composable 9 9 + import androidx.compose.runtime.CompositionLocalProvider 8 10 import androidx.compose.ui.tooling.preview.Preview 9 11 import com.performancecoachlab.posedetection.recording.VideoExtractionContext 10 12 import io.github.vinceglb.filekit.FileKit ··· 16 18 FileKit.init(this) 17 19 VideoExtractionContext.setUp(applicationContext) 18 20 enableEdgeToEdge() 19 19 - setContent { App() } 21 21 + val autoSpec = parseAutoSpec(intent) 22 22 + setContent { 23 23 + CompositionLocalProvider(LocalExperimentAutoSpec provides autoSpec) { 24 24 + App() 25 25 + } 26 26 + } 20 27 } 28 28 + } 29 29 + 30 30 + /** 31 31 + * Parses the unattended-experiment extras placed on the launch intent by 32 32 + * `tools/run_auto_experiment.sh`. Returns null if the intent does not carry 33 33 + * the `experiment_auto` flag, in which case the app behaves normally. 34 34 + * 35 35 + * Required when `experiment_auto` is true: 36 36 + * --es model_name "<display name or filename, case-insensitive>" 37 37 + * --el start_at_wall_ms <System.currentTimeMillis target> 38 38 + * --el duration_ms <buffer duration> 39 39 + * Optional: 40 40 + * --ez finish_on_stop <true|false> (default true) 41 41 + */ 42 42 + private fun parseAutoSpec(intent: Intent?): ExperimentLaunchSpec? { 43 43 + if (intent == null || !intent.getBooleanExtra("experiment_auto", false)) return null 44 44 + val modelName = intent.getStringExtra("model_name").orEmpty() 45 45 + val startAtWallMs = intent.getLongExtra("start_at_wall_ms", 0L) 46 46 + val durationMs = intent.getLongExtra("duration_ms", 0L) 47 47 + val finishOnStop = intent.getBooleanExtra("finish_on_stop", true) 48 48 + if (modelName.isBlank() || durationMs <= 0L) return null 49 49 + return ExperimentLaunchSpec( 50 50 + modelName = modelName, 51 51 + startAtWallMs = startAtWallMs, 52 52 + durationMs = durationMs, 53 53 + finishOnStop = finishOnStop, 54 54 + ) 21 55 } 22 56 23 57 @Preview

+27

sample/composeApp/src/androidMain/kotlin/com/nate/posedetection/ExperimentAuto.android.kt

Reviewed

··· 1 1 + package com.nate.posedetection 2 2 + 3 3 + import android.app.Activity 4 4 + import android.util.Log 5 5 + import androidx.compose.runtime.Composable 6 6 + import androidx.compose.runtime.remember 7 7 + import androidx.compose.ui.platform.LocalContext 8 8 + 9 9 + private const val TAG = "ExperimentAuto" 10 10 + 11 11 + private class AndroidExperimentAutoBridge(private val activity: Activity?) : ExperimentAutoBridge { 12 12 + override fun log(msg: String) { 13 13 + Log.i(TAG, msg) 14 14 + } 15 15 + 16 16 + override fun finishActivity() { 17 17 + activity?.finish() 18 18 + } 19 19 + } 20 20 + 21 21 + @Composable 22 22 + actual fun rememberExperimentAutoBridge(): ExperimentAutoBridge { 23 23 + val context = LocalContext.current 24 24 + return remember(context) { 25 25 + AndroidExperimentAutoBridge(context as? Activity) 26 26 + } 27 27 + }

+33

sample/composeApp/src/androidMain/kotlin/com/nate/posedetection/ExperimentLogger.android.kt

Reviewed

··· 1 1 + package com.nate.posedetection 2 2 + 3 3 + import android.os.Build 4 4 + import androidx.compose.runtime.Composable 5 5 + import androidx.compose.runtime.remember 6 6 + import androidx.compose.ui.platform.LocalContext 7 7 + import java.io.File 8 8 + 9 9 + private class AndroidExperimentLogger( 10 10 + private val externalFilesDir: File?, 11 11 + ) : ExperimentLogger { 12 12 + override val deviceLabel: String = "${Build.MANUFACTURER} ${Build.MODEL}".trim() 13 13 + 14 14 + override fun write(filename: String, json: String): String? { 15 15 + val baseDir = externalFilesDir ?: return null 16 16 + val logsDir = File(baseDir, "experiment_logs").apply { mkdirs() } 17 17 + val outFile = File(logsDir, filename) 18 18 + return try { 19 19 + outFile.writeText(json) 20 20 + outFile.absolutePath 21 21 + } catch (t: Throwable) { 22 22 + null 23 23 + } 24 24 + } 25 25 + } 26 26 + 27 27 + @Composable 28 28 + actual fun rememberExperimentLogger(): ExperimentLogger { 29 29 + val context = LocalContext.current 30 30 + return remember(context) { 31 31 + AndroidExperimentLogger(context.getExternalFilesDir(null)) 32 32 + } 33 33 + }

+200

sample/composeApp/src/commonMain/kotlin/com/nate/posedetection/App.kt

Reviewed

··· 95 95 import kotlinx.coroutines.CompletableDeferred 96 96 import kotlinx.coroutines.Job 97 97 import kotlinx.coroutines.delay 98 98 + import kotlinx.coroutines.flow.drop 99 99 + import kotlinx.coroutines.flow.first 98 100 import kotlinx.coroutines.launch 101 101 + import kotlinx.coroutines.withTimeoutOrNull 99 102 import kotlin.math.roundToLong 100 103 import kotlin.time.Clock 101 104 import kotlin.time.ExperimentalTime ··· 845 848 var menuExpanded by remember { mutableStateOf(false) } 846 849 var detectMode by remember { mutableStateOf(DetectMode.BOTH) } 847 850 val controller = remember { CameraViewControllerImpl() } 851 851 + 852 852 + // Experiment Mode state — buffers per-detection events into a JSON log 853 853 + // that the Mac orchestrator pulls back for the model comparison report. 854 854 + val experimentLogger = rememberExperimentLogger() 855 855 + val autoBridge = rememberExperimentAutoBridge() 856 856 + val autoSpec = LocalExperimentAutoSpec.current 857 857 + var experimentMode by remember { mutableStateOf(false) } 858 858 + var experimentRunning by remember { mutableStateOf(false) } 859 859 + var experimentStartedWallMs by remember { mutableStateOf(0L) } 860 860 + var experimentCapturedCount by remember { mutableStateOf(0) } 861 861 + var lastExperimentLogPath by remember { mutableStateOf<String?>(null) } 862 862 + val experimentEvents = remember { mutableListOf<ExperimentEvent>() } 863 863 + 864 864 + LaunchedEffect(experimentRunning) { 865 865 + if (!experimentRunning) return@LaunchedEffect 866 866 + // drop(1) skips the StateFlow's stale replay value so we only log 867 867 + // detections that arrive after Start was tapped. 868 868 + customObjectRespository.customObjectFlow.drop(1).collect { objs -> 869 869 + experimentEvents.add( 870 870 + ExperimentEvent( 871 871 + wallMs = Clock.System.now().toEpochMilliseconds(), 872 872 + objects = objs ?: emptyList(), 873 873 + ) 874 874 + ) 875 875 + experimentCapturedCount = experimentEvents.size 876 876 + } 877 877 + } 878 878 + 879 879 + // Auto-mode driver: when the activity was launched with an 880 880 + // ExperimentLaunchSpec (via intent extras → AppActivity → CompositionLocal), 881 881 + // automatically pick the model, wait until the orchestrator's wall-clock 882 882 + // target, run the buffer for the requested duration, write the JSON, and 883 883 + // (optionally) finish the activity. The orchestrator scrapes Logcat 884 884 + // (tag "ExperimentAuto") to track progress and to learn the saved path. 885 885 + LaunchedEffect(Unit) { 886 886 + val spec = autoSpec ?: return@LaunchedEffect 887 887 + autoBridge.log("entered modelName=${spec.modelName} startAt=${spec.startAtWallMs} duration=${spec.durationMs}") 888 888 + 889 889 + if (availableModels.isEmpty()) { 890 890 + autoBridge.log("error: no models discovered in assets") 891 891 + autoBridge.finishActivity() 892 892 + return@LaunchedEffect 893 893 + } 894 894 + val target = availableModels.firstOrNull { m -> 895 895 + val n = m.name 896 896 + n.equals(spec.modelName, ignoreCase = true) || 897 897 + n.replace(' ', '_').equals(spec.modelName, ignoreCase = true) || 898 898 + n.replace(' ', '_').equals(spec.modelName.replace(' ', '_'), ignoreCase = true) 899 899 + } 900 900 + if (target == null) { 901 901 + autoBridge.log("error: model not found: ${spec.modelName} (available: ${availableModels.joinToString { it.name }})") 902 902 + autoBridge.finishActivity() 903 903 + return@LaunchedEffect 904 904 + } 905 905 + experimentMode = true 906 906 + selectedModel = target 907 907 + 908 908 + // Wait for the pipeline to actually start emitting. drop(1) skips the 909 909 + // StateFlow's stale replay value; first() returns as soon as a fresh 910 910 + // emission lands (i.e. the model loaded, camera is producing frames, 911 911 + // and the first inference completed). Bounded by an 8s timeout in 912 912 + // case the model conflates empty results (e.g. nothing in frame). 913 913 + val firstEmission = withTimeoutOrNull(8000) { 914 914 + customObjectRespository.customObjectFlow.drop(1).first() 915 915 + } 916 916 + autoBridge.log("ready model=${target.name} firstEmissionObjects=${firstEmission?.size ?: -1}") 917 917 + 918 918 + val nowMs = Clock.System.now().toEpochMilliseconds() 919 919 + if (spec.startAtWallMs > nowMs) { 920 920 + delay(spec.startAtWallMs - nowMs) 921 921 + } 922 922 + 923 923 + // Programmatic Start — same effect as tapping the on-screen button. 924 924 + experimentEvents.clear() 925 925 + experimentCapturedCount = 0 926 926 + experimentStartedWallMs = Clock.System.now().toEpochMilliseconds() 927 927 + lastExperimentLogPath = null 928 928 + experimentRunning = true 929 929 + autoBridge.log("started wall_ms=$experimentStartedWallMs") 930 930 + 931 931 + delay(spec.durationMs) 932 932 + 933 933 + // Programmatic Stop — duplicates the manual button's serialise/write 934 934 + // sequence so both code paths produce identical JSON files. 935 935 + experimentRunning = false 936 936 + val stoppedMs = Clock.System.now().toEpochMilliseconds() 937 937 + val safeModelName = (selectedModel?.name ?: "unknown") 938 938 + .replace(' ', '_') 939 939 + .replace(Regex("[^A-Za-z0-9._-]"), "") 940 940 + .ifBlank { "unknown" } 941 941 + val runId = experimentStartedWallMs 942 942 + val filename = "${safeModelName}__${runId}.json" 943 943 + val json = buildExperimentLogJson( 944 944 + deviceModel = experimentLogger.deviceLabel, 945 945 + modelName = safeModelName, 946 946 + runId = runId, 947 947 + startedWallMs = experimentStartedWallMs, 948 948 + stoppedWallMs = stoppedMs, 949 949 + events = experimentEvents.toList(), 950 950 + ) 951 951 + val writtenPath = experimentLogger.write(filename, json) 952 952 + lastExperimentLogPath = writtenPath 953 953 + autoBridge.log("stopped path=$writtenPath events=${experimentEvents.size}") 954 954 + 955 955 + // Brief delay so the Logcat write flushes before we tear down. 956 956 + delay(500) 957 957 + if (spec.finishOnStop) { 958 958 + autoBridge.finishActivity() 959 959 + } 960 960 + } 961 961 + 848 962 PermissionProvider().apply { 849 963 if (!hasCameraPermission()) RequestCameraPermission(onGranted = { 850 964 permissionGranted = true ··· 1065 1179 1066 1180 HorizontalDivider() 1067 1181 1182 1182 + // Experiment Mode toggle — when on, the bottom-of-screen 1183 1183 + // Start/Stop overlay is visible and tapping Start begins 1184 1184 + // buffering per-detection events for the comparison report. 1185 1185 + DropdownMenuItem( 1186 1186 + text = { 1187 1187 + Row( 1188 1188 + horizontalArrangement = Arrangement.SpaceBetween, 1189 1189 + verticalAlignment = Alignment.CenterVertically, 1190 1190 + modifier = Modifier.fillMaxWidth() 1191 1191 + ) { 1192 1192 + Text("Experiment Mode") 1193 1193 + Spacer(Modifier.width(12.dp)) 1194 1194 + Switch(checked = experimentMode, onCheckedChange = null) 1195 1195 + } 1196 1196 + }, 1197 1197 + onClick = { 1198 1198 + val next = !experimentMode 1199 1199 + experimentMode = next 1200 1200 + if (!next && experimentRunning) { 1201 1201 + // Toggling off mid-run aborts without saving. 1202 1202 + experimentRunning = false 1203 1203 + experimentEvents.clear() 1204 1204 + experimentCapturedCount = 0 1205 1205 + } 1206 1206 + } 1207 1207 + ) 1208 1208 + 1209 1209 + HorizontalDivider() 1210 1210 + 1068 1211 // Start/End recording button 1069 1212 DropdownMenuItem( 1070 1213 text = { Text(if (recordingId != null) "End recording" else "Start recording") }, ··· 1078 1221 menuExpanded = false 1079 1222 } 1080 1223 ) 1224 1224 + } 1225 1225 + } 1226 1226 + // Experiment Mode overlay — bottom-center Start/Stop button. 1227 1227 + // Visible only when Experiment Mode is toggled on in the menu. 1228 1228 + if (experimentMode) { 1229 1229 + Column( 1230 1230 + modifier = Modifier 1231 1231 + .align(Alignment.BottomCenter) 1232 1232 + .padding(bottom = 24.dp), 1233 1233 + horizontalAlignment = Alignment.CenterHorizontally, 1234 1234 + ) { 1235 1235 + Button(onClick = { 1236 1236 + if (!experimentRunning) { 1237 1237 + experimentEvents.clear() 1238 1238 + experimentCapturedCount = 0 1239 1239 + experimentStartedWallMs = Clock.System.now().toEpochMilliseconds() 1240 1240 + lastExperimentLogPath = null 1241 1241 + experimentRunning = true 1242 1242 + } else { 1243 1243 + experimentRunning = false 1244 1244 + val stoppedMs = Clock.System.now().toEpochMilliseconds() 1245 1245 + val safeModelName = (selectedModel?.name ?: "unknown") 1246 1246 + .replace(' ', '_') 1247 1247 + .replace(Regex("[^A-Za-z0-9._-]"), "") 1248 1248 + .ifBlank { "unknown" } 1249 1249 + val runId = experimentStartedWallMs 1250 1250 + val filename = "${safeModelName}__${runId}.json" 1251 1251 + val json = buildExperimentLogJson( 1252 1252 + deviceModel = experimentLogger.deviceLabel, 1253 1253 + modelName = safeModelName, 1254 1254 + runId = runId, 1255 1255 + startedWallMs = experimentStartedWallMs, 1256 1256 + stoppedWallMs = stoppedMs, 1257 1257 + events = experimentEvents.toList(), 1258 1258 + ) 1259 1259 + lastExperimentLogPath = experimentLogger.write(filename, json) 1260 1260 + } 1261 1261 + }) { 1262 1262 + Text(if (experimentRunning) "Stop Experiment" else "Start Experiment") 1263 1263 + } 1264 1264 + if (experimentRunning) { 1265 1265 + Text( 1266 1266 + "Captured: $experimentCapturedCount events", 1267 1267 + color = Color.White, 1268 1268 + fontSize = 12.sp, 1269 1269 + modifier = Modifier.padding(top = 4.dp), 1270 1270 + ) 1271 1271 + } 1272 1272 + lastExperimentLogPath?.let { p -> 1273 1273 + Text( 1274 1274 + "Saved: $p", 1275 1275 + color = Color.White, 1276 1276 + fontSize = 11.sp, 1277 1277 + textAlign = TextAlign.Center, 1278 1278 + modifier = Modifier.padding(top = 4.dp, start = 8.dp, end = 8.dp), 1279 1279 + ) 1280 1280 + } 1081 1281 } 1082 1282 } 1083 1283 }

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sample/composeApp/src/commonMain/kotlin/com/nate/posedetection/ExperimentAuto.kt

Reviewed

··· 1 1 + package com.nate.posedetection 2 2 + 3 3 + import androidx.compose.runtime.Composable 4 4 + import androidx.compose.runtime.compositionLocalOf 5 5 + 6 6 + /** 7 7 + * Parameters for an unattended ("auto") experiment-mode run, supplied by the 8 8 + * Mac orchestrator (`tools/run_auto_experiment.sh`) via Android intent extras 9 9 + * and surfaced to Compose code through [LocalExperimentAutoSpec]. 10 10 + * 11 11 + * The phone schedules its experiment buffer to start at [startAtWallMs] 12 12 + * (a `System.currentTimeMillis()` target chosen by the orchestrator) and 13 13 + * automatically stops it [durationMs] later. The orchestrator picks the same 14 14 + * wall-clock target for the QuickTime play moment, so the two start in 15 15 + * approximate sync without needing any explicit handshake. Any constant 16 16 + * Mac↔phone clock skew cancels out across runs and does not affect model A 17 17 + * vs. model B comparison. 18 18 + */ 19 19 + data class ExperimentLaunchSpec( 20 20 + /** Display name (or filename) of the model to select before starting. */ 21 21 + val modelName: String, 22 22 + /** Wall-clock target (System.currentTimeMillis()) for the buffer start. */ 23 23 + val startAtWallMs: Long, 24 24 + /** How long the buffer should run before auto-stop and JSON write. */ 25 25 + val durationMs: Long, 26 26 + /** If true, the activity calls finish() after the JSON has been written. */ 27 27 + val finishOnStop: Boolean = true, 28 28 + ) 29 29 + 30 30 + /** 31 31 + * Composition-scoped slot for an [ExperimentLaunchSpec]. Android sets this 32 32 + * from `AppActivity.onCreate` when the launch intent carries the auto-mode 33 33 + * extras; iOS leaves it null. 34 34 + */ 35 35 + val LocalExperimentAutoSpec = compositionLocalOf<ExperimentLaunchSpec?> { null } 36 36 + 37 37 + /** 38 38 + * Bridge for platform-specific side effects the auto driver needs: 39 39 + * 40 40 + * * [log] writes a tagged line that the orchestrator scrapes via 41 41 + * `adb logcat -s ExperimentAuto:I` to track progress (ready / started / 42 42 + * stopped / error). 43 43 + * * [finishActivity] tears the activity down once the JSON is written so 44 44 + * the next run starts cold. 45 45 + */ 46 46 + interface ExperimentAutoBridge { 47 47 + fun log(msg: String) 48 48 + fun finishActivity() 49 49 + } 50 50 + 51 51 + @Composable 52 52 + expect fun rememberExperimentAutoBridge(): ExperimentAutoBridge

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sample/composeApp/src/commonMain/kotlin/com/nate/posedetection/ExperimentLogger.kt

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··· 1 1 + package com.nate.posedetection 2 2 + 3 3 + import androidx.compose.runtime.Composable 4 4 + import com.performancecoachlab.posedetection.recording.AnalysisObject 5 5 + 6 6 + /** 7 7 + * Persists experiment-mode capture buffers from the sample app to a platform- 8 8 + * specific location so the Mac orchestrator can `adb pull` them back for the 9 9 + * model-comparison report. Android writes JSON to the app's external files 10 10 + * directory under `experiment_logs/`. iOS is a no-op stub (out of scope). 11 11 + */ 12 12 + interface ExperimentLogger { 13 13 + /** Human-readable device label, e.g. "Google Pixel 7". Embedded in the log. */ 14 14 + val deviceLabel: String 15 15 + 16 16 + /** 17 17 + * Writes [json] to a platform file named [filename] inside the experiment 18 18 + * logs directory and returns its absolute path, or null if writing failed 19 19 + * or is unsupported on this platform. 20 20 + */ 21 21 + fun write(filename: String, json: String): String? 22 22 + } 23 23 + 24 24 + @Composable 25 25 + expect fun rememberExperimentLogger(): ExperimentLogger 26 26 + 27 27 + internal data class ExperimentEvent( 28 28 + val wallMs: Long, 29 29 + val objects: List<AnalysisObject>, 30 30 + ) 31 31 + 32 32 + /** 33 33 + * Hand-builds the experiment log JSON document. Format matches the schema in 34 34 + * the closed-loop training plan: device + model identification, start/stop 35 35 + * wall-clock bracket, and an `events` array. The `skeleton` field per event 36 36 + * is reserved but always emitted as null since this MVP compares object 37 37 + * detection models only. 38 38 + */ 39 39 + internal fun buildExperimentLogJson( 40 40 + deviceModel: String, 41 41 + modelName: String, 42 42 + runId: Long, 43 43 + startedWallMs: Long, 44 44 + stoppedWallMs: Long, 45 45 + events: List<ExperimentEvent>, 46 46 + ): String { 47 47 + val sb = StringBuilder() 48 48 + sb.append('{') 49 49 + sb.append("\"device_model\":\"").append(jsonEscape(deviceModel)).append("\",") 50 50 + sb.append("\"model_name\":\"").append(jsonEscape(modelName)).append("\",") 51 51 + sb.append("\"run_id\":").append(runId).append(',') 52 52 + sb.append("\"started_wall_ms\":").append(startedWallMs).append(',') 53 53 + sb.append("\"stopped_wall_ms\":").append(stoppedWallMs).append(',') 54 54 + sb.append("\"events\":[") 55 55 + events.forEachIndexed { idx, e -> 56 56 + if (idx > 0) sb.append(',') 57 57 + sb.append('{') 58 58 + sb.append("\"wall_ms\":").append(e.wallMs).append(',') 59 59 + sb.append("\"skeleton\":null,") 60 60 + sb.append("\"objects\":[") 61 61 + e.objects.forEachIndexed { i, obj -> 62 62 + if (i > 0) sb.append(',') 63 63 + val top = obj.labels.maxByOrNull { it.confidence } 64 64 + sb.append('{') 65 65 + sb.append("\"label\":\"").append(jsonEscape(top?.text ?: "")).append("\",") 66 66 + sb.append("\"confidence\":").append(jsonNumber(top?.confidence ?: 0f)).append(',') 67 67 + sb.append("\"bbox\":[") 68 68 + .append(jsonNumber(obj.boundingBox.left)).append(',') 69 69 + .append(jsonNumber(obj.boundingBox.top)).append(',') 70 70 + .append(jsonNumber(obj.boundingBox.right)).append(',') 71 71 + .append(jsonNumber(obj.boundingBox.bottom)) 72 72 + .append("],") 73 73 + sb.append("\"frame_size\":[") 74 74 + .append(obj.frameSize.width).append(',') 75 75 + .append(obj.frameSize.height) 76 76 + .append(']') 77 77 + sb.append('}') 78 78 + } 79 79 + sb.append(']') 80 80 + sb.append('}') 81 81 + } 82 82 + sb.append(']') 83 83 + sb.append('}') 84 84 + return sb.toString() 85 85 + } 86 86 + 87 87 + private fun jsonEscape(s: String): String { 88 88 + val sb = StringBuilder(s.length + 2) 89 89 + for (c in s) { 90 90 + when (c) { 91 91 + '"' -> sb.append("\\\"") 92 92 + '\\' -> sb.append("\\\\") 93 93 + '\n' -> sb.append("\\n") 94 94 + '\r' -> sb.append("\\r") 95 95 + '\t' -> sb.append("\\t") 96 96 + else -> if (c.code < 0x20) { 97 97 + sb.append("\\u").append(c.code.toString(16).padStart(4, '0')) 98 98 + } else sb.append(c) 99 99 + } 100 100 + } 101 101 + return sb.toString() 102 102 + } 103 103 + 104 104 + private fun jsonNumber(f: Float): String = 105 105 + if (f.isFinite()) f.toString() else "0"

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sample/composeApp/src/iosMain/kotlin/com/nate/posedetection/ExperimentAuto.ios.kt

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··· 1 1 + package com.nate.posedetection 2 2 + 3 3 + import androidx.compose.runtime.Composable 4 4 + import androidx.compose.runtime.remember 5 5 + 6 6 + private object IosExperimentAutoBridge : ExperimentAutoBridge { 7 7 + override fun log(msg: String) {} 8 8 + override fun finishActivity() {} 9 9 + } 10 10 + 11 11 + @Composable 12 12 + actual fun rememberExperimentAutoBridge(): ExperimentAutoBridge = 13 13 + remember { IosExperimentAutoBridge }

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sample/composeApp/src/iosMain/kotlin/com/nate/posedetection/ExperimentLogger.ios.kt

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··· 1 1 + package com.nate.posedetection 2 2 + 3 3 + import androidx.compose.runtime.Composable 4 4 + import androidx.compose.runtime.remember 5 5 + 6 6 + private object IosExperimentLogger : ExperimentLogger { 7 7 + override val deviceLabel: String = "ios" 8 8 + override fun write(filename: String, json: String): String? = null 9 9 + } 10 10 + 11 11 + @Composable 12 12 + actual fun rememberExperimentLogger(): ExperimentLogger = remember { IosExperimentLogger }

sample/iosApp/iosApp/models/yolo26n_v11_rect_512x384.mlpackage/Data/com.apple.CoreML/model.mlmodel

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sample/iosApp/iosApp/models/yolo26n_v11_rect_512x384.mlpackage/Data/com.apple.CoreML/weights/weight.bin

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sample/iosApp/iosApp/models/yolo26n_v11_rect_512x384.mlpackage/Manifest.json

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··· 1 1 + { 2 2 + "fileFormatVersion": "1.0.0", 3 3 + "itemInfoEntries": { 4 4 + "18ba14b4-753b-45e3-9fb2-68cec8f7110b": { 5 5 + "author": "com.apple.CoreML", 6 6 + "description": "CoreML Model Weights", 7 7 + "name": "weights", 8 8 + "path": "com.apple.CoreML/weights" 9 9 + }, 10 10 + "5192aff5-6847-4a09-98db-c5ed17988277": { 11 11 + "author": "com.apple.CoreML", 12 12 + "description": "CoreML Model Specification", 13 13 + "name": "model.mlmodel", 14 14 + "path": "com.apple.CoreML/model.mlmodel" 15 15 + } 16 16 + }, 17 17 + "rootModelIdentifier": "5192aff5-6847-4a09-98db-c5ed17988277" 18 18 + }

sample/iosApp/iosApp/models/yolo26n_v11_rect_640x480.mlpackage/Data/com.apple.CoreML/model.mlmodel

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sample/iosApp/iosApp/models/yolo26n_v11_rect_640x480.mlpackage/Data/com.apple.CoreML/weights/weight.bin

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sample/iosApp/iosApp/models/yolo26n_v11_rect_640x480.mlpackage/Manifest.json

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··· 1 1 + { 2 2 + "fileFormatVersion": "1.0.0", 3 3 + "itemInfoEntries": { 4 4 + "0dd5a52c-fa82-4dae-8775-fe06d157127d": { 5 5 + "author": "com.apple.CoreML", 6 6 + "description": "CoreML Model Weights", 7 7 + "name": "weights", 8 8 + "path": "com.apple.CoreML/weights" 9 9 + }, 10 10 + "ca543875-0854-4de4-8d98-72b69258f5a4": { 11 11 + "author": "com.apple.CoreML", 12 12 + "description": "CoreML Model Specification", 13 13 + "name": "model.mlmodel", 14 14 + "path": "com.apple.CoreML/model.mlmodel" 15 15 + } 16 16 + }, 17 17 + "rootModelIdentifier": "ca543875-0854-4de4-8d98-72b69258f5a4" 18 18 + }

sample/iosApp/iosApp/models/yolo26n_v11_rect_960x736.mlpackage/Data/com.apple.CoreML/model.mlmodel

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sample/iosApp/iosApp/models/yolo26n_v11_rect_960x736.mlpackage/Manifest.json

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··· 1 1 + { 2 2 + "fileFormatVersion": "1.0.0", 3 3 + "itemInfoEntries": { 4 4 + "7ff8395c-6cac-4c82-a673-d6c45b49b4e7": { 5 5 + "author": "com.apple.CoreML", 6 6 + "description": "CoreML Model Specification", 7 7 + "name": "model.mlmodel", 8 8 + "path": "com.apple.CoreML/model.mlmodel" 9 9 + }, 10 10 + "a94ab001-e4e2-4b39-939a-21ab438b9fa9": { 11 11 + "author": "com.apple.CoreML", 12 12 + "description": "CoreML Model Weights", 13 13 + "name": "weights", 14 14 + "path": "com.apple.CoreML/weights" 15 15 + } 16 16 + }, 17 17 + "rootModelIdentifier": "7ff8395c-6cac-4c82-a673-d6c45b49b4e7" 18 18 + }

+268

tools/README.md

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··· 1 1 + # Closed-loop training & comparison tools 2 2 + 3 3 + These scripts close the loop between Colab-trained TFLite object-detection 4 4 + models and on-device measurement on a real Android phone, so we can 5 5 + objectively compare model A vs. model B by running them against the same 6 6 + video on the same hardware. 7 7 + 8 8 + ``` 9 9 + collab/Model Training.ipynb (edit cells, retrain in Colab) 10 10 + │ trained .tflite via Drive sync 11 11 + ▼ 12 12 + collab/output/<run-id>/*.tflite 13 13 + │ tools/sync_models.py 14 14 + ▼ 15 15 + sample/composeApp/.../assets/<run-id>__<model>.tflite 16 16 + │ ./gradlew :sample:composeApp:assembleDebug 17 17 + │ adb install -r 18 18 + ▼ 19 19 + Phone (Experiment Mode) 20 20 + │ JSON log per detection 21 21 + ▼ 22 22 + /sdcard/Android/data/com.nate.posedetection.androidApp/files/experiment_logs/ 23 23 + │ tools/run_experiment.sh (manual orchestration) 24 24 + │ adb pull 25 25 + ▼ 26 26 + experiments/<run-id>/{log.json, manifest.json} 27 27 + │ tools/compare_logs.py 28 28 + ▼ 29 29 + report.html + summary.csv 30 30 + ``` 31 31 + 32 32 + The package id is `com.nate.posedetection.androidApp` (note the `.androidApp` 33 33 + suffix — the Kotlin namespace is `com.nate.posedetection` without it). Every 34 34 + adb command targets the suffixed name. 35 35 + 36 36 + --- 37 37 + 38 38 + ## Prerequisites 39 39 + 40 40 + * **macOS** with QuickTime Player (preinstalled). 41 41 + * **adb** at `/Users/virtualintern/Library/Android/sdk/platform-tools/adb`. 42 42 + * **Phone** with USB debugging on, paired with this Mac, camera permission 43 43 + pre-granted to the sample app. Verify with `adb devices`. 44 44 + * **Python 3** for the orchestration scripts. `compare_logs.py` optionally 45 45 + uses **matplotlib** for embedded timeline charts — install with 46 46 + `uv pip install matplotlib` if you want them; the report still renders 47 47 + without. 48 48 + 49 49 + --- 50 50 + 51 51 + ## tools/sync_models.py 52 52 + 53 53 + Copies trained `.tflite` files from `collab/output/<run-id>/` into the 54 54 + sample app's Android assets dir. Synced files are renamed 55 55 + `<run-id>__<model-base>.tflite` (double underscore deliberate — the picker 56 56 + in `DiscoverModels.android.kt` replaces single underscores with spaces, so 57 57 + the double underscore renders as a clear visual break in the dropdown). 58 58 + 59 59 + ```bash 60 60 + tools/sync_models.py # default: sync the latest run 61 61 + tools/sync_models.py --list # show available runs newest-first 62 62 + tools/sync_models.py --run v0_smoke # sync a specific run 63 63 + tools/sync_models.py --clean # remove synced models 64 64 + ``` 65 65 + 66 66 + `--clean` preserves the three baseline models that ship with the repo 67 67 + (`yolo11n_dataset_dataset.tflite`, `yolo11n_su_416.tflite`, 68 68 + `yolov10n_float16.tflite`). 69 69 + 70 70 + After syncing, rebuild and reinstall the app: 71 71 + 72 72 + ```bash 73 73 + ./gradlew :sample:composeApp:assembleDebug 74 74 + adb install -r sample/composeApp/build/outputs/apk/debug/composeApp-debug.apk 75 75 + ``` 76 76 + 77 77 + If install fails with `INSTALL_FAILED_UPDATE_INCOMPATIBLE` (signature 78 78 + mismatch), the on-device app was signed by a different debug key. Run 79 79 + `adb uninstall com.nate.posedetection.androidApp` first; this wipes any 80 80 + data the app stored locally. 81 81 + 82 82 + --- 83 83 + 84 84 + ## tools/run_auto_experiment.sh — fully unattended orchestration 85 85 + 86 86 + Drives one experiment run end-to-end with no human at the phone. The 87 87 + orchestrator launches the sample app via `am start` with intent extras that 88 88 + tell the in-app auto-driver to select a model, wait until a wall-clock 89 89 + target, run the experiment buffer for a fixed duration, write the JSON, 90 90 + and finish the activity. The Mac side races to the same wall-clock target 91 91 + and plays the video in QuickTime at the configured offset. 92 92 + 93 93 + ```bash 94 94 + tools/run_auto_experiment.sh \ 95 95 + --video ~/Downloads/clip.mp4 \ 96 96 + --duration 10 \ 97 97 + --model-tag yolo11n_su_416 \ 98 98 + [--start-offset 10] \ 99 99 + [--device <serial>] 100 100 + ``` 101 101 + 102 102 + Flow: 103 103 + 104 104 + 1. Snapshots existing experiment-log files on the device for the diff-pull. 105 105 + 2. **Pre-opens** the video in QuickTime, positions the cursor at 106 106 + `--start-offset` seconds, and pauses. This step is wrapped in a 600 s 107 107 + AppleEvent timeout so very large 4K files don't blow up. The slow 108 108 + indexing happens here, *before* any wall-clock timing matters. 109 109 + 3. Force-stops the sample app and clears Logcat. 110 110 + 4. Computes `start_at_wall_ms = now + 9 s` (configurable via the 111 111 + `APP_BOOT_PAD_SECONDS` env var). Launches `AppActivity` via `am start` 112 112 + with `--ez experiment_auto true --es model_name <X> 113 113 + --el start_at_wall_ms <Y> --el duration_ms <Z>`. 114 114 + 5. Sleeps locally until `start_at_wall_ms`, then issues 115 115 + `osascript ... play front document` (sub-second since the file is 116 116 + already loaded). `t0_wall_ms` is captured immediately before the play. 117 117 + 6. Sleeps `--duration` seconds, then pauses QuickTime. 118 118 + 7. Diff-polls `/sdcard/.../experiment_logs` for the new JSON file (15 s 119 119 + timeout) and pulls it into `experiments/<run-id>/`. 120 120 + 8. Captures the `ExperimentAuto` Logcat excerpt and writes 121 121 + `experiments/<run-id>/manifest.json` with `t0_wall_ms`, 122 122 + `start_at_wall_ms`, model tag, video offset, device label, and the 123 123 + captured log lines. 124 124 + 125 125 + The Compose auto-driver inside `CameraSample` reads 126 126 + `LocalExperimentAutoSpec`, finds the model whose display name (or 127 127 + underscore-equivalent) matches `model_name` case-insensitively, waits for 128 128 + `customObjectFlow.drop(1).first()` (the pipeline's first real emission, 129 129 + bounded by an 8 s timeout), waits until `start_at_wall_ms`, runs the 130 130 + buffer for `duration_ms`, writes the JSON via the same `ExperimentLogger` 131 131 + the manual button uses, logs the saved path, and finishes the activity. 132 132 + 133 133 + Logcat tag is `ExperimentAuto`. To watch a run live: 134 134 + 135 135 + ```bash 136 136 + adb logcat -c && adb logcat -s ExperimentAuto:I 137 137 + ``` 138 138 + 139 139 + Tuning knobs (env vars): 140 140 + 141 141 + * `APP_BOOT_PAD_SECONDS` (default 9) — wall-clock budget for cold start + 142 142 + model load + camera spin-up. Bump if you see `start_at_wall_ms` firing 143 143 + before the phone is `ready`. 144 144 + * `PULL_TIMEOUT_SECONDS` (default 15) — how long to wait for the new JSON 145 145 + to appear on the phone after the buffer should have stopped. 146 146 + 147 147 + --- 148 148 + 149 149 + ## tools/run_experiment.sh — manual orchestration 150 150 + 151 151 + Drives one experiment run when you can be at the phone to tap Start/Stop. 152 152 + 153 153 + ```bash 154 154 + tools/run_experiment.sh \ 155 155 + --video ~/Downloads/test.mp4 \ 156 156 + --duration 10 \ 157 157 + --model-tag yolo11n_su_416 158 158 + ``` 159 159 + 160 160 + Flow: 161 161 + 162 162 + 1. Snapshots the existing experiment-log files on the phone so it can 163 163 + diff-pull only the new ones afterwards. 164 164 + 2. Prompts you: *"Phone ready? Model picked, Experiment Mode on, Start 165 165 + tapped?"* 166 166 + 3. Records `t0_wall_ms` and plays the video in QuickTime (`osascript`). 167 167 + 4. Sleeps for `--duration` seconds. 168 168 + 5. Pauses QuickTime. 169 169 + 6. Prompts you to tap Stop on the phone. 170 170 + 7. Pulls only the newly-written log file(s) into 171 171 + `experiments/<run-id>/`. 172 172 + 8. Writes `experiments/<run-id>/manifest.json` with `t0_wall_ms`, 173 173 + model tag, device label, video path, duration. 174 174 + 175 175 + `--device <serial>` is only needed if `adb devices` shows more than one 176 176 + device (otherwise the script auto-selects the single connected one). 177 177 + `--no-confirm` skips the interactive prompts for unattended dry-runs. 178 178 + 179 179 + --- 180 180 + 181 181 + ## tools/compare_logs.py — model comparison report 182 182 + 183 183 + Reads every `experiments/<run-id>/manifest.json` + log files and produces 184 184 + a self-contained HTML report and a CSV summary. 185 185 + 186 186 + ```bash 187 187 + tools/compare_logs.py experiments/ # report.html in cwd 188 188 + tools/compare_logs.py experiments/ -o ./reports/ # write into reports/ 189 189 + tools/compare_logs.py experiments/ --bucket 250 # 250ms buckets 190 190 + ``` 191 191 + 192 192 + The report contains: 193 193 + 194 194 + * **Per-run scorecard**: detection rate, mean confidence, events/sec, 195 195 + classes seen, mean bbox area. 196 196 + * **Per-class jitter table**: stddev of bbox-center deltas across 197 197 + consecutive frames. Lower = more stable. (Note: a moving subject also 198 198 + produces high jitter — useful for spotting *flapping* detections more 199 199 + than for absolute model quality.) 200 200 + * **Per-bucket comparison table**: for each time bucket (default 100 ms, 201 201 + configurable via `--bucket`), each model's frame count, top class, and 202 202 + max confidence. Aligned to each run's `t0_wall_ms` from the manifest so 203 203 + model A and model B share the same x-axis when run against the same 204 204 + video. 205 205 + * **Per-class confidence timelines**: matplotlib PNG charts embedded as 206 206 + base64 data URIs (omitted with a warning banner if matplotlib is not 207 207 + installed). 208 208 + 209 209 + `summary.csv` has one row per run with the same scorecard fields, for 210 210 + spreadsheet exploration. 211 211 + 212 212 + --- 213 213 + 214 214 + ## Typical comparison workflow 215 215 + 216 216 + ```bash 217 217 + # 1. Train a new model in collab/Model Training.ipynb (via Colab MCP). 218 218 + # Output lands at collab/output/<run-id>/<name>.tflite via Drive sync. 219 219 + 220 220 + # 2. Sync the trained model into the app. 221 221 + tools/sync_models.py --run <run-id> 222 222 + 223 223 + # 3. Build and install. 224 224 + ./gradlew :sample:composeApp:assembleDebug && \ 225 225 + adb install -r sample/composeApp/build/outputs/apk/debug/composeApp-debug.apk 226 226 + 227 227 + # 4. Run experiment A against the trained model. 228 228 + tools/run_experiment.sh \ 229 229 + --video ~/Movies/test_clip.mp4 \ 230 230 + --duration 10 \ 231 231 + --model-tag <run-id>__<name> 232 232 + 233 233 + # 5. (At the phone) Switch to a baseline model in the picker, tap Start 234 234 + # Experiment, and re-run against the same video. 235 235 + tools/run_experiment.sh \ 236 236 + --video ~/Movies/test_clip.mp4 \ 237 237 + --duration 10 \ 238 238 + --model-tag yolo11n_su_416 239 239 + 240 240 + # 6. Generate the comparison report. 241 241 + tools/compare_logs.py experiments/ -o reports/ 242 242 + 243 243 + # 7. Open reports/report.html in a browser. 244 244 + ``` 245 245 + 246 246 + --- 247 247 + 248 248 + ## Layout 249 249 + 250 250 + ``` 251 251 + tools/ 252 252 + ├── README.md (this file) 253 253 + ├── sync_models.py (Phase 2) 254 254 + ├── run_experiment.sh (Phase 5, manual orchestration) 255 255 + ├── run_auto_experiment.sh (unattended orchestration via intent extras) 256 256 + └── compare_logs.py (Phase 6) 257 257 + 258 258 + experiments/ (created by run_experiment.sh; gitignored) 259 259 + └── <t0_wall_ms>_<model_tag>/ 260 260 + ├── manifest.json 261 261 + └── <model>__<runId>.json 262 262 + 263 263 + collab/ 264 264 + ├── Model Training.ipynb (Drive-synced; iterated via Colab MCP) 265 265 + └── output/ (Drive-synced; tflites land here) 266 266 + └── <run-id>/ 267 267 + └── *.tflite 268 268 + ```

+427

tools/compare_logs.py

Reviewed

··· 1 1 + #!/usr/bin/env python3 2 2 + """ 3 3 + Generate a model-comparison report from experiments/<run-id>/ directories. 4 4 + 5 5 + Each ``experiments/<run-id>/`` directory is expected to contain: 6 6 + 7 7 + * ``manifest.json`` with at least ``run_id``, ``model_tag``, ``t0_wall_ms``, 8 8 + ``duration_seconds``, ``device_label`` (written by ``tools/run_experiment.sh``). 9 9 + * One or more ``*.json`` files matching the experiment-log schema produced by 10 10 + the sample app's Experiment Mode (``device_model``, ``model_name``, ``run_id``, 11 11 + ``started_wall_ms``, ``stopped_wall_ms``, ``events``). 12 12 + 13 13 + For every run we compute a scorecard (detection rate, mean confidence, class 14 14 + diversity, per-class jitter), a per-bucket comparison table aligned to each 15 15 + run's ``t0_wall_ms`` so model A and model B share the same x-axis when run 16 16 + against the same video, and one matplotlib chart per class plotting confidence 17 17 + over time. matplotlib is optional — if it's not installed the report still 18 18 + renders, just without the timeline images. 19 19 + 20 20 + Usage: 21 21 + tools/compare_logs.py experiments/ # report.html + summary.csv in cwd 22 22 + tools/compare_logs.py experiments/ -o ./reports/ # write into ./reports/ 23 23 + tools/compare_logs.py experiments/ --bucket 200 # use 200ms buckets 24 24 + """ 25 25 + 26 26 + from __future__ import annotations 27 27 + 28 28 + import argparse 29 29 + import base64 30 30 + import csv 31 31 + import io 32 32 + import json 33 33 + import math 34 34 + import statistics 35 35 + import sys 36 36 + from collections import defaultdict 37 37 + from html import escape 38 38 + from pathlib import Path 39 39 + 40 40 + # matplotlib is optional. If unavailable, the report omits timeline charts. 41 41 + try: 42 42 + import matplotlib 43 43 + 44 44 + matplotlib.use("Agg") 45 45 + import matplotlib.pyplot as plt 46 46 + 47 47 + HAS_MPL = True 48 48 + except ImportError: 49 49 + HAS_MPL = False 50 50 + 51 51 + 52 52 + # ----------------------------------------------------------------------------- run loading 53 53 + 54 54 + def load_runs(experiments_dir: Path): 55 55 + """Yield (manifest, events_sorted_by_wall_ms) for each run subdir.""" 56 56 + for run_dir in sorted(experiments_dir.iterdir()): 57 57 + if not run_dir.is_dir(): 58 58 + continue 59 59 + manifest_path = run_dir / "manifest.json" 60 60 + if not manifest_path.exists(): 61 61 + print(f" skipping {run_dir.name}: no manifest.json", file=sys.stderr) 62 62 + continue 63 63 + try: 64 64 + with open(manifest_path) as f: 65 65 + manifest = json.load(f) 66 66 + except json.JSONDecodeError as e: 67 67 + print(f" skipping {run_dir.name}: bad manifest.json ({e})", file=sys.stderr) 68 68 + continue 69 69 + log_files = [f for f in run_dir.glob("*.json") if f.name != "manifest.json"] 70 70 + if not log_files: 71 71 + print(f" skipping {run_dir.name}: no log files", file=sys.stderr) 72 72 + continue 73 73 + events: list[dict] = [] 74 74 + for log in log_files: 75 75 + try: 76 76 + with open(log) as f: 77 77 + data = json.load(f) 78 78 + except json.JSONDecodeError as e: 79 79 + print(f" warning: bad log {log.name} ({e})", file=sys.stderr) 80 80 + continue 81 81 + events.extend(data.get("events", [])) 82 82 + events.sort(key=lambda e: e.get("wall_ms", 0)) 83 83 + yield manifest, events 84 84 + 85 85 + 86 86 + # ----------------------------------------------------------------------------- per-run metrics 87 87 + 88 88 + def compute_scorecard(manifest: dict, events: list[dict]) -> dict: 89 89 + """Return summary metrics for one run.""" 90 90 + t0 = manifest["t0_wall_ms"] 91 91 + duration_s = float(manifest.get("duration_seconds") or 0) 92 92 + 93 93 + total_events = len(events) 94 94 + events_with_objects = sum(1 for e in events if e.get("objects")) 95 95 + 96 96 + all_confs: list[float] = [] 97 97 + classes: set[str] = set() 98 98 + bbox_areas: list[float] = [] 99 99 + for e in events: 100 100 + for obj in e.get("objects", []): 101 101 + all_confs.append(float(obj.get("confidence", 0))) 102 102 + classes.add(obj.get("label", "")) 103 103 + bbox = obj.get("bbox", [0, 0, 0, 0]) 104 104 + if len(bbox) >= 4: 105 105 + w = max(0.0, float(bbox[2]) - float(bbox[0])) 106 106 + h = max(0.0, float(bbox[3]) - float(bbox[1])) 107 107 + bbox_areas.append(w * h) 108 108 + 109 109 + # Jitter per class: stddev of consecutive bbox-center deltas. High jitter 110 110 + # means the box is bouncing around frame-to-frame; low jitter means a 111 111 + # stable lock. We sum stdev_dx + stdev_dy as a coarse single-number score. 112 112 + jitter_per_class: dict[str, float] = {} 113 113 + for cls in classes: 114 114 + centers: list[tuple[float, float]] = [] 115 115 + for e in events: 116 116 + for obj in e.get("objects", []): 117 117 + if obj.get("label") == cls: 118 118 + bbox = obj.get("bbox", [0, 0, 0, 0]) 119 119 + if len(bbox) >= 4: 120 120 + cx = (float(bbox[0]) + float(bbox[2])) / 2 121 121 + cy = (float(bbox[1]) + float(bbox[3])) / 2 122 122 + centers.append((cx, cy)) 123 123 + break # at most one center per frame for each class 124 124 + if len(centers) >= 3: 125 125 + dxs = [centers[i + 1][0] - centers[i][0] for i in range(len(centers) - 1)] 126 126 + dys = [centers[i + 1][1] - centers[i][1] for i in range(len(centers) - 1)] 127 127 + jitter_per_class[cls] = statistics.stdev(dxs) + statistics.stdev(dys) 128 128 + 129 129 + return { 130 130 + "run_id": manifest["run_id"], 131 131 + "model_tag": manifest.get("model_tag", "unknown"), 132 132 + "device_label": manifest.get("device_label", "unknown"), 133 133 + "video_path": manifest.get("video_path", ""), 134 134 + "duration_s": duration_s, 135 135 + "t0_wall_ms": t0, 136 136 + "total_events": total_events, 137 137 + "events_with_objects": events_with_objects, 138 138 + "detection_rate": events_with_objects / total_events if total_events else 0.0, 139 139 + "events_per_second": total_events / duration_s if duration_s else 0.0, 140 140 + "mean_confidence": statistics.mean(all_confs) if all_confs else 0.0, 141 141 + "class_diversity": len(classes), 142 142 + "classes": sorted(c for c in classes if c), 143 143 + "mean_bbox_area": statistics.mean(bbox_areas) if bbox_areas else 0.0, 144 144 + "jitter_per_class": jitter_per_class, 145 145 + } 146 146 + 147 147 + 148 148 + # ----------------------------------------------------------------------------- bucketing 149 149 + 150 150 + def bucket_events(events: list[dict], t0: int, bucket_ms: int) -> dict[int, list[dict]]: 151 151 + """Group events into buckets keyed by floor(relative_ms / bucket_ms).""" 152 152 + buckets: dict[int, list[dict]] = defaultdict(list) 153 153 + for e in events: 154 154 + rel = int(e.get("wall_ms", 0)) - t0 155 155 + if rel < 0: 156 156 + continue 157 157 + buckets[rel // bucket_ms].append(e) 158 158 + return buckets 159 159 + 160 160 + 161 161 + def bucket_summary(events_in_bucket: list[dict]) -> dict: 162 162 + """Per-bucket / per-model aggregation: count, top-class, top-confidence.""" 163 163 + confs: list[float] = [] 164 164 + label_counts: dict[str, int] = defaultdict(int) 165 165 + for e in events_in_bucket: 166 166 + for obj in e.get("objects", []): 167 167 + confs.append(float(obj.get("confidence", 0))) 168 168 + label_counts[obj.get("label", "")] += 1 169 169 + top_label = max(label_counts.items(), key=lambda kv: kv[1])[0] if label_counts else "" 170 170 + return { 171 171 + "frames": len(events_in_bucket), 172 172 + "detections": sum(label_counts.values()), 173 173 + "top_label": top_label, 174 174 + "max_conf": max(confs) if confs else 0.0, 175 175 + "mean_conf": statistics.mean(confs) if confs else 0.0, 176 176 + } 177 177 + 178 178 + 179 179 + # ----------------------------------------------------------------------------- charts 180 180 + 181 181 + def render_timeline_png(per_run_series: dict[str, list[tuple[float, float]]], title: str) -> str | None: 182 182 + """Build a single matplotlib chart and return it as a base64 data URI.""" 183 183 + if not HAS_MPL: 184 184 + return None 185 185 + fig, ax = plt.subplots(figsize=(9, 3)) 186 186 + has_data = False 187 187 + for model_tag, series in per_run_series.items(): 188 188 + if not series: 189 189 + continue 190 190 + xs = [s[0] / 1000.0 for s in series] 191 191 + ys = [s[1] for s in series] 192 192 + ax.plot(xs, ys, marker=".", linestyle="-", linewidth=1, label=model_tag) 193 193 + has_data = True 194 194 + if not has_data: 195 195 + plt.close(fig) 196 196 + return None 197 197 + ax.set_xlabel("seconds since t0") 198 198 + ax.set_ylabel("confidence") 199 199 + ax.set_ylim(0, 1) 200 200 + ax.set_title(title) 201 201 + ax.legend(loc="upper right", fontsize=8) 202 202 + ax.grid(True, alpha=0.3) 203 203 + buf = io.BytesIO() 204 204 + fig.tight_layout() 205 205 + fig.savefig(buf, format="png", dpi=110) 206 206 + plt.close(fig) 207 207 + return "data:image/png;base64," + base64.b64encode(buf.getvalue()).decode("ascii") 208 208 + 209 209 + 210 210 + def class_timeline_data(manifest: dict, events: list[dict], cls: str) -> list[tuple[float, float]]: 211 211 + """Return [(relative_ms, top_confidence_for_class)] for one run + class.""" 212 212 + t0 = manifest["t0_wall_ms"] 213 213 + out: list[tuple[float, float]] = [] 214 214 + for e in events: 215 215 + rel = int(e.get("wall_ms", 0)) - t0 216 216 + if rel < 0: 217 217 + continue 218 218 + best = 0.0 219 219 + found = False 220 220 + for obj in e.get("objects", []): 221 221 + if obj.get("label") == cls: 222 222 + c = float(obj.get("confidence", 0)) 223 223 + if c > best: 224 224 + best = c 225 225 + found = True 226 226 + if found: 227 227 + out.append((rel, best)) 228 228 + return out 229 229 + 230 230 + 231 231 + # ----------------------------------------------------------------------------- HTML rendering 232 232 + 233 233 + def fmt_pct(x: float) -> str: 234 234 + return f"{x * 100:.1f}%" 235 235 + 236 236 + def fmt_num(x: float, places: int = 2) -> str: 237 237 + return f"{x:.{places}f}" 238 238 + 239 239 + 240 240 + def render_html(scorecards: list[dict], runs: list[tuple[dict, list[dict]]], bucket_ms: int) -> str: 241 241 + style = """ 242 242 + body { font-family: -apple-system, sans-serif; margin: 24px; color: #222; } 243 243 + h1, h2 { font-weight: 600; } 244 244 + table { border-collapse: collapse; margin: 8px 0 24px 0; font-size: 13px; } 245 245 + th, td { border: 1px solid #ddd; padding: 4px 8px; text-align: right; } 246 246 + th { background: #f5f5f5; text-align: left; } 247 247 + td.left, th.left { text-align: left; } 248 248 + .scorecard td:first-child { font-weight: 600; } 249 249 + .small { color: #888; font-size: 11px; } 250 250 + img { max-width: 100%; border: 1px solid #ddd; margin: 8px 0; } 251 251 + .warn { background: #fff7e0; border: 1px solid #f0c000; padding: 8px 12px; border-radius: 4px; } 252 252 + """ 253 253 + 254 254 + parts: list[str] = [] 255 255 + parts.append("<!doctype html><html><head><meta charset='utf-8'>") 256 256 + parts.append("<title>Model comparison report</title>") 257 257 + parts.append(f"<style>{style}</style></head><body>") 258 258 + parts.append("<h1>Model comparison report</h1>") 259 259 + parts.append(f"<p class='small'>{len(scorecards)} run(s), bucket size {bucket_ms} ms</p>") 260 260 + 261 261 + if not HAS_MPL: 262 262 + parts.append( 263 263 + "<p class='warn'>matplotlib is not installed — timeline charts are omitted. " 264 264 + "Install with <code>uv pip install matplotlib</code> (or pip) and re-run.</p>" 265 265 + ) 266 266 + 267 267 + # ----- per-run scorecards 268 268 + parts.append("<h2>Per-run scorecard</h2>") 269 269 + parts.append("<table class='scorecard'>") 270 270 + headers = ["metric"] + [s["model_tag"] for s in scorecards] 271 271 + parts.append("<tr>" + "".join(f"<th class='left'>{escape(h)}</th>" for h in headers) + "</tr>") 272 272 + 273 273 + def row(label: str, values: list[str]) -> str: 274 274 + return "<tr><td class='left'>" + escape(label) + "</td>" + "".join( 275 275 + f"<td>{escape(v)}</td>" for v in values 276 276 + ) + "</tr>" 277 277 + 278 278 + parts.append(row("run_id", [s["run_id"] for s in scorecards])) 279 279 + parts.append(row("device", [s["device_label"] for s in scorecards])) 280 280 + parts.append(row("duration (s)", [fmt_num(s["duration_s"], 1) for s in scorecards])) 281 281 + parts.append(row("total events", [str(s["total_events"]) for s in scorecards])) 282 282 + parts.append(row("events / second", [fmt_num(s["events_per_second"], 2) for s in scorecards])) 283 283 + parts.append(row("frames with detection", [fmt_pct(s["detection_rate"]) for s in scorecards])) 284 284 + parts.append(row("mean confidence", [fmt_num(s["mean_confidence"], 3) for s in scorecards])) 285 285 + parts.append(row("classes seen", [str(s["class_diversity"]) for s in scorecards])) 286 286 + parts.append(row("mean bbox area (px²)", [fmt_num(s["mean_bbox_area"], 0) for s in scorecards])) 287 287 + parts.append(row("class list", [", ".join(s["classes"]) for s in scorecards])) 288 288 + parts.append("</table>") 289 289 + 290 290 + # ----- per-class jitter table 291 291 + all_classes = sorted({c for s in scorecards for c in s["classes"]}) 292 292 + if all_classes: 293 293 + parts.append("<h2>Jitter per class (lower = more stable)</h2>") 294 294 + parts.append("<table>") 295 295 + parts.append("<tr><th class='left'>class</th>" + "".join(f"<th>{escape(s['model_tag'])}</th>" for s in scorecards) + "</tr>") 296 296 + for cls in all_classes: 297 297 + row_vals = [] 298 298 + for s in scorecards: 299 299 + v = s["jitter_per_class"].get(cls) 300 300 + row_vals.append(fmt_num(v, 1) if v is not None else "—") 301 301 + parts.append("<tr><td class='left'>" + escape(cls) + "</td>" + "".join(f"<td>{v}</td>" for v in row_vals) + "</tr>") 302 302 + parts.append("</table>") 303 303 + 304 304 + # ----- per-bucket comparison table 305 305 + parts.append(f"<h2>Per-bucket comparison ({bucket_ms} ms bins)</h2>") 306 306 + bucketed_per_run = [] 307 307 + for s, (manifest, events) in zip(scorecards, runs): 308 308 + bucketed_per_run.append(bucket_events(events, manifest["t0_wall_ms"], bucket_ms)) 309 309 + all_buckets = sorted({b for d in bucketed_per_run for b in d.keys()}) 310 310 + parts.append("<table>") 311 311 + header_cells = ["<th class='left'>bucket (ms)</th>"] 312 312 + for s in scorecards: 313 313 + tag = escape(s["model_tag"]) 314 314 + header_cells.append(f"<th>{tag}<br><span class='small'>frames</span></th>") 315 315 + header_cells.append(f"<th>{tag}<br><span class='small'>top class</span></th>") 316 316 + header_cells.append(f"<th>{tag}<br><span class='small'>max conf</span></th>") 317 317 + parts.append("<tr>" + "".join(header_cells) + "</tr>") 318 318 + for b in all_buckets: 319 319 + cells = [f"<td class='left'>{b * bucket_ms}–{(b + 1) * bucket_ms}</td>"] 320 320 + for run_buckets in bucketed_per_run: 321 321 + evs = run_buckets.get(b, []) 322 322 + if evs: 323 323 + summ = bucket_summary(evs) 324 324 + cells.append(f"<td>{summ['frames']}</td>") 325 325 + cells.append(f"<td>{escape(summ['top_label'])}</td>") 326 326 + cells.append(f"<td>{fmt_num(summ['max_conf'], 2)}</td>") 327 327 + else: 328 328 + cells.append("<td>—</td><td>—</td><td>—</td>") 329 329 + parts.append("<tr>" + "".join(cells) + "</tr>") 330 330 + parts.append("</table>") 331 331 + 332 332 + # ----- per-class timeline charts 333 333 + if HAS_MPL: 334 334 + parts.append("<h2>Per-class confidence timelines</h2>") 335 335 + for cls in all_classes: 336 336 + per_run_series: dict[str, list[tuple[float, float]]] = {} 337 337 + for s, (manifest, events) in zip(scorecards, runs): 338 338 + per_run_series[s["model_tag"]] = class_timeline_data(manifest, events, cls) 339 339 + data_uri = render_timeline_png(per_run_series, f"class: {cls}") 340 340 + if data_uri: 341 341 + parts.append(f"<img alt='{escape(cls)} timeline' src='{data_uri}'/>") 342 342 + 343 343 + parts.append("</body></html>") 344 344 + return "".join(parts) 345 345 + 346 346 + 347 347 + # ----------------------------------------------------------------------------- summary csv 348 348 + 349 349 + def write_summary_csv(scorecards: list[dict], path: Path) -> None: 350 350 + fieldnames = [ 351 351 + "run_id", 352 352 + "model_tag", 353 353 + "device_label", 354 354 + "duration_s", 355 355 + "total_events", 356 356 + "events_per_second", 357 357 + "detection_rate", 358 358 + "mean_confidence", 359 359 + "class_diversity", 360 360 + "classes", 361 361 + "mean_bbox_area", 362 362 + ] 363 363 + with open(path, "w", newline="") as f: 364 364 + w = csv.DictWriter(f, fieldnames=fieldnames) 365 365 + w.writeheader() 366 366 + for s in scorecards: 367 367 + w.writerow( 368 368 + { 369 369 + "run_id": s["run_id"], 370 370 + "model_tag": s["model_tag"], 371 371 + "device_label": s["device_label"], 372 372 + "duration_s": f"{s['duration_s']:.2f}", 373 373 + "total_events": s["total_events"], 374 374 + "events_per_second": f"{s['events_per_second']:.3f}", 375 375 + "detection_rate": f"{s['detection_rate']:.4f}", 376 376 + "mean_confidence": f"{s['mean_confidence']:.4f}", 377 377 + "class_diversity": s["class_diversity"], 378 378 + "classes": "|".join(s["classes"]), 379 379 + "mean_bbox_area": f"{s['mean_bbox_area']:.1f}", 380 380 + } 381 381 + ) 382 382 + 383 383 + 384 384 + # ----------------------------------------------------------------------------- main 385 385 + 386 386 + def main(argv: list[str]) -> int: 387 387 + parser = argparse.ArgumentParser( 388 388 + description="Build a model comparison report from experiments/<run-id>/ directories.", 389 389 + ) 390 390 + parser.add_argument("experiments_dir", type=Path, help="path to the experiments/ directory") 391 391 + parser.add_argument("-o", "--output-dir", type=Path, default=Path.cwd(), 392 392 + help="where report.html and summary.csv should be written (default: cwd)") 393 393 + parser.add_argument("--bucket", type=int, default=100, help="bucket size in milliseconds (default: 100)") 394 394 + args = parser.parse_args(argv) 395 395 + 396 396 + if not args.experiments_dir.is_dir(): 397 397 + print(f"error: not a directory: {args.experiments_dir}", file=sys.stderr) 398 398 + return 2 399 399 + 400 400 + runs = list(load_runs(args.experiments_dir)) 401 401 + if not runs: 402 402 + print("error: no usable runs found.", file=sys.stderr) 403 403 + return 2 404 404 + 405 405 + scorecards = [compute_scorecard(m, e) for m, e in runs] 406 406 + 407 407 + args.output_dir.mkdir(parents=True, exist_ok=True) 408 408 + html_path = args.output_dir / "report.html" 409 409 + csv_path = args.output_dir / "summary.csv" 410 410 + 411 411 + html_path.write_text(render_html(scorecards, runs, args.bucket)) 412 412 + write_summary_csv(scorecards, csv_path) 413 413 + 414 414 + print(f"wrote {html_path}") 415 415 + print(f"wrote {csv_path}") 416 416 + print(f"runs: {len(runs)}") 417 417 + for s in scorecards: 418 418 + print( 419 419 + f" {s['model_tag']}: {s['total_events']} events, " 420 420 + f"{s['detection_rate'] * 100:.1f}% with detection, " 421 421 + f"mean conf {s['mean_confidence']:.3f}" 422 422 + ) 423 423 + return 0 424 424 + 425 425 + 426 426 + if __name__ == "__main__": 427 427 + sys.exit(main(sys.argv[1:]))

+252

tools/run_auto_experiment.sh

Reviewed

··· 1 1 + #!/usr/bin/env bash 2 2 + # 3 3 + # Drives one fully unattended closed-loop experiment run: 4 4 + # 5 5 + # 1. Force-stops the sample app and clears Logcat so the cold start is 6 6 + # clean and the only ExperimentAuto: log lines are from this run. 7 7 + # 2. Picks a wall-clock target start_at_wall_ms = now + APP_BOOT_PAD seconds. 8 8 + # 3. Launches AppActivity via `am start` with the auto-mode intent extras 9 9 + # (model_name, start_at_wall_ms, duration_ms). The app's Compose 10 10 + # auto-driver selects the model, waits for the same wall-clock target, 11 11 + # runs the experiment buffer for duration_ms, writes the JSON, and 12 12 + # finishes the activity. 13 13 + # 4. Sleeps until start_at_wall_ms locally on the Mac, then plays the 14 14 + # video in QuickTime at the requested --start-offset (seconds). Both 15 15 + # sides hit the same wall-clock instant. 16 16 + # 5. Sleeps for the buffer duration, pauses QuickTime. 17 17 + # 6. Diff-polls /sdcard/.../experiment_logs for the new JSON file (with a 18 18 + # 10s timeout) and pulls it into experiments/<run-id>/. 19 19 + # 7. Captures the ExperimentAuto Logcat lines for the manifest, writes 20 20 + # experiments/<run-id>/manifest.json with t0_wall_ms, video offset, 21 21 + # device label, model tag, and the captured log lines. 22 22 + # 23 23 + # Usage: 24 24 + # tools/run_auto_experiment.sh \ 25 25 + # --video ~/Downloads/clip.mp4 \ 26 26 + # --duration 10 \ 27 27 + # --model-tag yolo11n_su_416 \ 28 28 + # [--start-offset 10] \ 29 29 + # [--device <serial>] 30 30 + # 31 31 + # After at least two runs (model A then model B against the same video, 32 32 + # same --start-offset) point tools/compare_logs.py at experiments/ to 33 33 + # build the comparison report. 34 34 + 35 35 + set -euo pipefail 36 36 + 37 37 + ADB="${ADB:-/Users/virtualintern/Library/Android/sdk/platform-tools/adb}" 38 38 + PKG="com.nate.posedetection.androidApp" 39 39 + ACTIVITY="$PKG/com.nate.posedetection.AppActivity" 40 40 + REPO_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" 41 41 + EXPERIMENTS_DIR="$REPO_ROOT/experiments" 42 42 + REMOTE_LOGS_DIR="/sdcard/Android/data/$PKG/files/experiment_logs" 43 43 + APP_BOOT_PAD_SECONDS="${APP_BOOT_PAD_SECONDS:-9}" 44 44 + PULL_TIMEOUT_SECONDS="${PULL_TIMEOUT_SECONDS:-15}" 45 45 + 46 46 + usage() { 47 47 + sed -n '3,33p' "$0" 48 48 + exit "${1:-1}" 49 49 + } 50 50 + 51 51 + VIDEO="" 52 52 + DURATION="" 53 53 + MODEL_TAG="" 54 54 + START_OFFSET=0 55 55 + DEVICE="" 56 56 + 57 57 + while [ $# -gt 0 ]; do 58 58 + case "$1" in 59 59 + --video) VIDEO="$2"; shift 2 ;; 60 60 + --duration) DURATION="$2"; shift 2 ;; 61 61 + --model-tag) MODEL_TAG="$2"; shift 2 ;; 62 62 + --start-offset) START_OFFSET="$2"; shift 2 ;; 63 63 + --device) DEVICE="$2"; shift 2 ;; 64 64 + -h|--help) usage 0 ;; 65 65 + *) echo "unknown arg: $1" >&2; usage ;; 66 66 + esac 67 67 + done 68 68 + 69 69 + [ -n "$VIDEO" ] || { echo "error: --video is required" >&2; exit 2; } 70 70 + [ -n "$DURATION" ] || { echo "error: --duration is required" >&2; exit 2; } 71 71 + [ -n "$MODEL_TAG" ] || { echo "error: --model-tag is required" >&2; exit 2; } 72 72 + 73 73 + if [ ! -f "$VIDEO" ]; then 74 74 + echo "error: video file not found: $VIDEO" >&2 75 75 + exit 2 76 76 + fi 77 77 + VIDEO_ABS="$(cd "$(dirname "$VIDEO")" && pwd)/$(basename "$VIDEO")" 78 78 + 79 79 + # Resolve device. 80 80 + if [ -z "$DEVICE" ]; then 81 81 + CONNECTED="$("$ADB" devices | awk 'NR>1 && $2=="device" {print $1}')" 82 82 + NUM_CONNECTED="$(printf '%s\n' "$CONNECTED" | grep -c . || true)" 83 83 + if [ "$NUM_CONNECTED" -eq 0 ]; then 84 84 + echo "error: no devices connected." >&2 85 85 + exit 3 86 86 + elif [ "$NUM_CONNECTED" -gt 1 ]; then 87 87 + echo "error: multiple devices connected — pass --device <serial>." >&2 88 88 + printf ' %s\n' $CONNECTED >&2 89 89 + exit 3 90 90 + fi 91 91 + DEVICE="$CONNECTED" 92 92 + echo "==> auto-selected device: $DEVICE" 93 93 + fi 94 94 + 95 95 + now_ms() { python3 -c 'import time; print(int(time.time()*1000))'; } 96 96 + 97 97 + DURATION_MS=$(python3 -c "print(int(float('$DURATION') * 1000))") 98 98 + 99 99 + echo "==> auto experiment" 100 100 + echo " model_tag = $MODEL_TAG" 101 101 + echo " duration = ${DURATION}s ($DURATION_MS ms)" 102 102 + echo " video = $VIDEO_ABS" 103 103 + echo " start_offset = ${START_OFFSET}s" 104 104 + 105 105 + # 1. Snapshot the existing logs so we can diff-pull only the new file. 106 106 + LOGS_BEFORE="$("$ADB" -s "$DEVICE" shell "ls -1 $REMOTE_LOGS_DIR 2>/dev/null" \ 107 107 + | tr -d '\r' | grep '\.json$' || true)" 108 108 + EXISTING_COUNT=$(printf '%s\n' "$LOGS_BEFORE" | grep -c '\.json$' || true) 109 109 + echo "==> $EXISTING_COUNT existing log(s) on device (will be ignored)" 110 110 + 111 111 + # 2. Pre-open the video in QuickTime *first* and position to start_offset 112 112 + # while paused. Large 4K files can take 10-60s to index, so we do this 113 113 + # before any wall-clock timing matters. The 600s timeout covers extreme 114 114 + # cases (default AppleEvent timeout is 60s and can fire on big files). 115 115 + echo "==> pre-opening video in QuickTime (large files may take a moment)..." 116 116 + osascript <<APPLESCRIPT 117 117 + with timeout of 600 seconds 118 118 + tell application "QuickTime Player" 119 119 + activate 120 120 + if (count of documents) > 0 then 121 121 + try 122 122 + close every document saving no 123 123 + end try 124 124 + end if 125 125 + open POSIX file "$VIDEO_ABS" 126 126 + repeat while (count of documents) is 0 127 127 + delay 0.1 128 128 + end repeat 129 129 + tell front document 130 130 + pause 131 131 + set current time to $START_OFFSET 132 132 + present 133 133 + end tell 134 134 + end tell 135 135 + end timeout 136 136 + APPLESCRIPT 137 137 + 138 138 + # 3. Cold-start the app: kill any existing instance and clear Logcat. 139 139 + echo "==> force-stopping app and clearing Logcat..." 140 140 + "$ADB" -s "$DEVICE" shell am force-stop "$PKG" >/dev/null 141 141 + "$ADB" -s "$DEVICE" logcat -c >/dev/null 142 142 + 143 143 + # 4. Compute the wall-clock target and launch via intent with auto-mode extras. 144 144 + NOW_MS=$(now_ms) 145 145 + START_AT_WALL_MS=$((NOW_MS + APP_BOOT_PAD_SECONDS * 1000)) 146 146 + RUN_ID="${START_AT_WALL_MS}_$(printf '%s' "$MODEL_TAG" | tr -c 'A-Za-z0-9._-' '_' | sed 's/^_*//; s/_*$//')" 147 147 + RUN_DIR="$EXPERIMENTS_DIR/$RUN_ID" 148 148 + echo " start_at_wall = $START_AT_WALL_MS (in ${APP_BOOT_PAD_SECONDS}s)" 149 149 + echo " run_dir = $RUN_DIR" 150 150 + 151 151 + echo "==> launching $ACTIVITY with auto-mode extras..." 152 152 + "$ADB" -s "$DEVICE" shell am start -n "$ACTIVITY" \ 153 153 + --ez experiment_auto true \ 154 154 + --es model_name "$MODEL_TAG" \ 155 155 + --el start_at_wall_ms "$START_AT_WALL_MS" \ 156 156 + --el duration_ms "$DURATION_MS" >/dev/null 157 157 + 158 158 + # 5. Wait until the wall-clock target, then play (already pre-loaded so this 159 159 + # is sub-second). t0_wall_ms is captured immediately before the play call. 160 160 + WAIT_MS=$((START_AT_WALL_MS - $(now_ms))) 161 161 + if [ "$WAIT_MS" -gt 0 ]; then 162 162 + python3 -c "import time; time.sleep($WAIT_MS/1000.0)" 163 163 + fi 164 164 + 165 165 + T0_WALL_MS=$(now_ms) 166 166 + echo "==> playing video at offset ${START_OFFSET}s (t0_wall_ms=$T0_WALL_MS)" 167 167 + osascript -e 'tell application "QuickTime Player" to play front document' >/dev/null 168 168 + 169 169 + # 5. Sleep the buffer duration plus a small safety margin then pause. 170 170 + echo "==> sleeping ${DURATION}s for buffer to fill..." 171 171 + sleep "$DURATION" 172 172 + osascript -e 'tell application "QuickTime Player" to pause front document' >/dev/null 173 173 + 174 174 + # 6. Poll for the new log file with a timeout. 175 175 + echo "==> polling for new log file (timeout ${PULL_TIMEOUT_SECONDS}s)..." 176 176 + NEW_LOGS="" 177 177 + DEADLINE=$(( $(now_ms) + PULL_TIMEOUT_SECONDS * 1000 )) 178 178 + while [ "$(now_ms)" -lt "$DEADLINE" ]; do 179 179 + LOGS_AFTER="$("$ADB" -s "$DEVICE" shell "ls -1 $REMOTE_LOGS_DIR 2>/dev/null" \ 180 180 + | tr -d '\r' | grep '\.json$' || true)" 181 181 + NEW_LOGS="$(comm -13 \ 182 182 + <(printf '%s\n' "$LOGS_BEFORE" | sort -u) \ 183 183 + <(printf '%s\n' "$LOGS_AFTER" | sort -u) \ 184 184 + | grep '\.json$' || true)" 185 185 + if [ -n "$NEW_LOGS" ]; then 186 186 + break 187 187 + fi 188 188 + sleep 0.25 189 189 + done 190 190 + 191 191 + mkdir -p "$RUN_DIR" 192 192 + 193 193 + if [ -z "$NEW_LOGS" ]; then 194 194 + echo "error: no new log file appeared on device after ${PULL_TIMEOUT_SECONDS}s" >&2 195 195 + echo " recent ExperimentAuto Logcat:" >&2 196 196 + "$ADB" -s "$DEVICE" logcat -d -s ExperimentAuto:I 2>/dev/null | tail -30 >&2 || true 197 197 + exit 4 198 198 + fi 199 199 + 200 200 + echo "==> pulling new log(s) into $RUN_DIR/" 201 201 + while IFS= read -r f; do 202 202 + [ -z "$f" ] && continue 203 203 + "$ADB" -s "$DEVICE" pull "$REMOTE_LOGS_DIR/$f" "$RUN_DIR/" >/dev/null 204 204 + echo " pulled $f" 205 205 + done <<< "$NEW_LOGS" 206 206 + 207 207 + # 7. Capture the ExperimentAuto log lines (for the manifest). 208 208 + LOGCAT_EXCERPT="$("$ADB" -s "$DEVICE" logcat -d -s ExperimentAuto:I 2>/dev/null \ 209 209 + | grep ExperimentAuto || true)" 210 210 + 211 211 + DEVICE_MANUFACTURER="$("$ADB" -s "$DEVICE" shell getprop ro.product.manufacturer 2>/dev/null | tr -d '\r' || true)" 212 212 + DEVICE_MODEL_NAME="$("$ADB" -s "$DEVICE" shell getprop ro.product.model 2>/dev/null | tr -d '\r' || true)" 213 213 + DEVICE_LABEL="$DEVICE_MANUFACTURER $DEVICE_MODEL_NAME" 214 214 + 215 215 + NEW_LOGS_JOINED="$(printf '%s\n' "$NEW_LOGS" | tr '\n' ',' | sed 's/,$//')" 216 216 + 217 217 + RUN_ID="$RUN_ID" \ 218 218 + MODEL_TAG="$MODEL_TAG" \ 219 219 + VIDEO_ABS="$VIDEO_ABS" \ 220 220 + START_OFFSET="$START_OFFSET" \ 221 221 + DURATION="$DURATION" \ 222 222 + T0_WALL_MS="$T0_WALL_MS" \ 223 223 + START_AT_WALL_MS="$START_AT_WALL_MS" \ 224 224 + DEVICE="$DEVICE" \ 225 225 + DEVICE_LABEL="$DEVICE_LABEL" \ 226 226 + NEW_LOGS_JOINED="$NEW_LOGS_JOINED" \ 227 227 + LOGCAT_EXCERPT="$LOGCAT_EXCERPT" \ 228 228 + python3 -c ' 229 229 + import json, os 230 230 + files = [f for f in os.environ["NEW_LOGS_JOINED"].split(",") if f] 231 231 + logcat = [l for l in os.environ["LOGCAT_EXCERPT"].splitlines() if l.strip()] 232 232 + manifest = { 233 233 + "run_id": os.environ["RUN_ID"], 234 234 + "mode": "auto", 235 235 + "model_tag": os.environ["MODEL_TAG"], 236 236 + "video_path": os.environ["VIDEO_ABS"], 237 237 + "video_start_offset_seconds": float(os.environ["START_OFFSET"]), 238 238 + "duration_seconds": float(os.environ["DURATION"]), 239 239 + "t0_wall_ms": int(os.environ["T0_WALL_MS"]), 240 240 + "start_at_wall_ms": int(os.environ["START_AT_WALL_MS"]), 241 241 + "device_serial": os.environ["DEVICE"], 242 242 + "device_label": os.environ["DEVICE_LABEL"].strip(), 243 243 + "log_files": files, 244 244 + "logcat_excerpt": logcat, 245 245 + } 246 246 + print(json.dumps(manifest, indent=2)) 247 247 + ' > "$RUN_DIR/manifest.json" 248 248 + 249 249 + echo "==> wrote $RUN_DIR/manifest.json" 250 250 + echo 251 251 + echo "Run dir: $RUN_DIR" 252 252 + ls -la "$RUN_DIR"

+219

tools/run_experiment.sh

Reviewed

··· 1 1 + #!/usr/bin/env bash 2 2 + # 3 3 + # Drives one closed-loop experiment run for the model comparison harness: 4 4 + # 5 5 + # 1. Snapshots existing experiment logs on the phone (so we know which file 6 6 + # is the "new" one this run produces). 7 7 + # 2. Prompts you to confirm Experiment Mode is armed and Start has been 8 8 + # tapped on the phone (skipped with --no-confirm). 9 9 + # 3. Records t0_wall_ms in milliseconds, then plays the test video from the 10 10 + # start in QuickTime Player via osascript. 11 11 + # 4. Sleeps for --duration seconds. 12 12 + # 5. Pauses QuickTime. 13 13 + # 6. Prompts you to tap Stop on the phone (skipped with --no-confirm). 14 14 + # 7. Diffs the post-run file list against the snapshot, pulls only the 15 15 + # newly-written JSON file(s) into experiments/<run-id>/. 16 16 + # 8. Writes experiments/<run-id>/manifest.json with t0_wall_ms, the video 17 17 + # path, model tag, device info, and duration. 18 18 + # 19 19 + # Usage: 20 20 + # tools/run_experiment.sh \ 21 21 + # --video /abs/or/rel/path/test.mp4 \ 22 22 + # --duration 10 \ 23 23 + # --model-tag yolo11n_su_416 \ 24 24 + # [--device RFCX10EM9LR] \ 25 25 + # [--no-confirm] 26 26 + # 27 27 + # After two runs (model A then model B against the same video) point 28 28 + # tools/compare_logs.py at experiments/ to produce the report. 29 29 + 30 30 + set -euo pipefail 31 31 + 32 32 + ADB="${ADB:-/Users/virtualintern/Library/Android/sdk/platform-tools/adb}" 33 33 + # DEVICE may be passed via --device or EXP_DEVICE. Empty means "auto-detect: 34 34 + # if exactly one device is connected, use it; otherwise error and require 35 35 + # explicit --device". 36 36 + DEVICE="${EXP_DEVICE:-}" 37 37 + PKG="com.nate.posedetection.androidApp" 38 38 + REPO_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" 39 39 + EXPERIMENTS_DIR="$REPO_ROOT/experiments" 40 40 + REMOTE_LOGS_DIR="/sdcard/Android/data/$PKG/files/experiment_logs" 41 41 + 42 42 + usage() { 43 43 + sed -n '3,28p' "$0" 44 44 + exit "${1:-1}" 45 45 + } 46 46 + 47 47 + VIDEO="" 48 48 + DURATION="" 49 49 + MODEL_TAG="" 50 50 + NO_CONFIRM=0 51 51 + 52 52 + while [ $# -gt 0 ]; do 53 53 + case "$1" in 54 54 + --video) VIDEO="$2"; shift 2 ;; 55 55 + --duration) DURATION="$2"; shift 2 ;; 56 56 + --model-tag) MODEL_TAG="$2"; shift 2 ;; 57 57 + --device) DEVICE="$2"; shift 2 ;; 58 58 + --no-confirm) NO_CONFIRM=1; shift ;; 59 59 + -h|--help) usage 0 ;; 60 60 + *) echo "unknown arg: $1" >&2; usage ;; 61 61 + esac 62 62 + done 63 63 + 64 64 + [ -n "$VIDEO" ] || { echo "error: --video is required" >&2; exit 2; } 65 65 + [ -n "$DURATION" ] || { echo "error: --duration is required" >&2; exit 2; } 66 66 + [ -n "$MODEL_TAG" ] || { echo "error: --model-tag is required" >&2; exit 2; } 67 67 + 68 68 + if [ ! -f "$VIDEO" ]; then 69 69 + echo "error: video file not found: $VIDEO" >&2 70 70 + exit 2 71 71 + fi 72 72 + VIDEO_ABS="$(cd "$(dirname "$VIDEO")" && pwd)/$(basename "$VIDEO")" 73 73 + 74 74 + # Sanitize model tag for filesystem use (run-id slug). 75 75 + SAFE_TAG="$(printf '%s' "$MODEL_TAG" | tr -c 'A-Za-z0-9._-' '_' | sed 's/^_*//; s/_*$//')" 76 76 + [ -n "$SAFE_TAG" ] || SAFE_TAG="unnamed" 77 77 + 78 78 + # Resolve / verify device. 79 79 + if [ -z "$DEVICE" ]; then 80 80 + # Auto-detect: only succeed if exactly one device is attached. 81 81 + CONNECTED="$("$ADB" devices | awk 'NR>1 && $2=="device" {print $1}')" 82 82 + NUM_CONNECTED="$(printf '%s\n' "$CONNECTED" | grep -c . || true)" 83 83 + if [ "$NUM_CONNECTED" -eq 0 ]; then 84 84 + echo "error: no devices connected. Plug in the phone and run 'adb devices'." >&2 85 85 + exit 3 86 86 + elif [ "$NUM_CONNECTED" -gt 1 ]; then 87 87 + echo "error: multiple devices connected — pass --device <serial> to disambiguate." >&2 88 88 + echo " connected:" >&2 89 89 + printf ' %s\n' $CONNECTED >&2 90 90 + exit 3 91 91 + fi 92 92 + DEVICE="$CONNECTED" 93 93 + echo "==> auto-selected device: $DEVICE" 94 94 + fi 95 95 + if ! "$ADB" -s "$DEVICE" get-state >/dev/null 2>&1; then 96 96 + echo "error: device $DEVICE is not connected." >&2 97 97 + echo " adb devices output:" >&2 98 98 + "$ADB" devices >&2 99 99 + exit 3 100 100 + fi 101 101 + 102 102 + now_ms() { python3 -c 'import time; print(int(time.time()*1000))'; } 103 103 + 104 104 + confirm() { 105 105 + if [ "$NO_CONFIRM" -eq 1 ]; then return 0; fi 106 106 + printf '%s [y/N] ' "$1" 107 107 + local response 108 108 + read -r response 109 109 + case "$response" in 110 110 + [yY]|[yY][eE][sS]) return 0 ;; 111 111 + *) return 1 ;; 112 112 + esac 113 113 + } 114 114 + 115 115 + list_remote_logs() { 116 116 + "$ADB" -s "$DEVICE" shell "ls -1 $REMOTE_LOGS_DIR 2>/dev/null" \ 117 117 + | tr -d '\r' \ 118 118 + | grep '\.json$' \ 119 119 + || true 120 120 + } 121 121 + 122 122 + # 1. Snapshot existing logs so we can diff after the run. 123 123 + echo "==> Snapshotting existing logs on $DEVICE..." 124 124 + LOGS_BEFORE="$(list_remote_logs)" 125 125 + EXISTING_COUNT=$(printf '%s\n' "$LOGS_BEFORE" | grep -c '\.json$' || true) 126 126 + echo " ($EXISTING_COUNT existing log file(s))" 127 127 + 128 128 + # 2. Confirm the phone side is armed. 129 129 + if ! confirm "Phone ready? Model picked, Experiment Mode on, Start tapped?"; then 130 130 + echo "Aborted by user." 131 131 + exit 0 132 132 + fi 133 133 + 134 134 + # 3. Record t0 and play the video. 135 135 + T0_WALL_MS="$(now_ms)" 136 136 + echo "==> t0_wall_ms = $T0_WALL_MS" 137 137 + echo "==> Playing $VIDEO_ABS in QuickTime..." 138 138 + osascript \ 139 139 + -e 'tell application "QuickTime Player" to activate' \ 140 140 + -e "tell application \"QuickTime Player\" to open POSIX file \"$VIDEO_ABS\"" \ 141 141 + -e 'tell application "QuickTime Player" to set current time of front document to 0' \ 142 142 + -e 'tell application "QuickTime Player" to play front document' >/dev/null 143 143 + 144 144 + # 4. Wait the requested duration. 145 145 + echo "==> Sleeping ${DURATION}s..." 146 146 + sleep "$DURATION" 147 147 + 148 148 + # 5. Pause playback. 149 149 + echo "==> Pausing QuickTime..." 150 150 + osascript -e 'tell application "QuickTime Player" to pause front document' >/dev/null 151 151 + 152 152 + STOPPED_WALL_MS="$(now_ms)" 153 153 + 154 154 + # 6. Prompt the user to tap Stop on the phone. 155 155 + if [ "$NO_CONFIRM" -eq 0 ]; then 156 156 + echo 157 157 + echo "==> Tap 'Stop Experiment' on the phone now." 158 158 + printf " Press Enter when done... " 159 159 + read -r _ 160 160 + fi 161 161 + 162 162 + # 7. Diff the file list, pull anything new. 163 163 + LOGS_AFTER="$(list_remote_logs)" 164 164 + NEW_LOGS="$(comm -13 <(printf '%s\n' "$LOGS_BEFORE" | sort -u) <(printf '%s\n' "$LOGS_AFTER" | sort -u) | grep '\.json$' || true)" 165 165 + 166 166 + RUN_ID="${T0_WALL_MS}_${SAFE_TAG}" 167 167 + RUN_DIR="$EXPERIMENTS_DIR/$RUN_ID" 168 168 + mkdir -p "$RUN_DIR" 169 169 + 170 170 + if [ -z "$NEW_LOGS" ]; then 171 171 + echo "warning: no new log files appeared on the phone. Was Experiment Mode" >&2 172 172 + echo " actually started/stopped? Was a model selected? The manifest" >&2 173 173 + echo " will still be written so you can debug, but the run is empty." >&2 174 174 + else 175 175 + echo "==> Pulling new log(s) into $RUN_DIR/" 176 176 + while IFS= read -r f; do 177 177 + [ -z "$f" ] && continue 178 178 + "$ADB" -s "$DEVICE" pull "$REMOTE_LOGS_DIR/$f" "$RUN_DIR/" >/dev/null 179 179 + echo " pulled $f" 180 180 + done <<< "$NEW_LOGS" 181 181 + fi 182 182 + 183 183 + # 8. Write the manifest. Use python for JSON to avoid quoting headaches. 184 184 + DEVICE_MANUFACTURER="$("$ADB" -s "$DEVICE" shell getprop ro.product.manufacturer 2>/dev/null | tr -d '\r' || true)" 185 185 + DEVICE_MODEL_NAME="$("$ADB" -s "$DEVICE" shell getprop ro.product.model 2>/dev/null | tr -d '\r' || true)" 186 186 + DEVICE_LABEL="$DEVICE_MANUFACTURER $DEVICE_MODEL_NAME" 187 187 + 188 188 + NEW_LOGS_JOINED="$(printf '%s\n' "$NEW_LOGS" | tr '\n' ',' | sed 's/,$//')" 189 189 + 190 190 + RUN_ID="$RUN_ID" \ 191 191 + MODEL_TAG="$MODEL_TAG" \ 192 192 + VIDEO_ABS="$VIDEO_ABS" \ 193 193 + DURATION="$DURATION" \ 194 194 + T0_WALL_MS="$T0_WALL_MS" \ 195 195 + STOPPED_WALL_MS="$STOPPED_WALL_MS" \ 196 196 + DEVICE="$DEVICE" \ 197 197 + DEVICE_LABEL="$DEVICE_LABEL" \ 198 198 + NEW_LOGS_JOINED="$NEW_LOGS_JOINED" \ 199 199 + python3 -c ' 200 200 + import json, os 201 201 + files = [f for f in os.environ["NEW_LOGS_JOINED"].split(",") if f] 202 202 + manifest = { 203 203 + "run_id": os.environ["RUN_ID"], 204 204 + "model_tag": os.environ["MODEL_TAG"], 205 205 + "video_path": os.environ["VIDEO_ABS"], 206 206 + "duration_seconds": float(os.environ["DURATION"]), 207 207 + "t0_wall_ms": int(os.environ["T0_WALL_MS"]), 208 208 + "stopped_wall_ms": int(os.environ["STOPPED_WALL_MS"]), 209 209 + "device_serial": os.environ["DEVICE"], 210 210 + "device_label": os.environ["DEVICE_LABEL"].strip(), 211 211 + "log_files": files, 212 212 + } 213 213 + print(json.dumps(manifest, indent=2)) 214 214 + ' > "$RUN_DIR/manifest.json" 215 215 + 216 216 + echo "==> Wrote $RUN_DIR/manifest.json" 217 217 + echo 218 218 + echo "Run dir: $RUN_DIR" 219 219 + echo "Next: tools/compare_logs.py experiments/ (after at least 2 runs against the same video)"

+161

tools/sync_models.py

Reviewed

··· 1 1 + #!/usr/bin/env python3 2 2 + """ 3 3 + Sync trained .tflite models from Colab output into the sample app's assets dir. 4 4 + 5 5 + The Colab notebook writes trained models to ``collab/output/<run-id>/*.tflite`` 6 6 + (via Drive sync). This script copies those models into the sample app's 7 7 + Android assets directory so ``DiscoverModels.android.kt`` will pick them up 8 8 + on the next build. 9 9 + 10 10 + Usage:: 11 11 + 12 12 + tools/sync_models.py # sync the latest run (default) 13 13 + tools/sync_models.py --list # list available runs newest-first 14 14 + tools/sync_models.py --run <run-id> # sync a specific run 15 15 + tools/sync_models.py --clean # remove previously-synced models 16 16 + 17 17 + Synced files are renamed ``<run-id>__<model-base>.tflite`` (double underscore). 18 18 + The double underscore is deliberate: ``DiscoverModels.android.kt`` replaces 19 19 + single underscores with spaces in the picker, so ``__`` becomes a clear visual 20 20 + break between the run-id and the model name in the dropdown. 21 21 + 22 22 + ``--clean`` preserves the three baseline models bundled with the repo. 23 23 + """ 24 24 + 25 25 + from __future__ import annotations 26 26 + 27 27 + import argparse 28 28 + import re 29 29 + import shutil 30 30 + import sys 31 31 + from pathlib import Path 32 32 + 33 33 + REPO_ROOT = Path(__file__).resolve().parent.parent 34 34 + COLAB_OUTPUT_DIR = REPO_ROOT / "collab" / "output" 35 35 + ASSETS_DIR = REPO_ROOT / "sample" / "composeApp" / "src" / "androidMain" / "assets" 36 36 + 37 37 + # Baseline models that ship with the repo. --clean preserves these; everything 38 38 + # else under ASSETS_DIR with a .tflite extension is treated as a synced file 39 39 + # and removed by --clean. 40 40 + BASELINE_MODELS = frozenset( 41 41 + { 42 42 + "yolo11n_dataset_dataset.tflite", 43 43 + "yolo11n_su_416.tflite", 44 44 + "yolov10n_float16.tflite", 45 45 + } 46 46 + ) 47 47 + 48 48 + SAFE_NAME = re.compile(r"[^A-Za-z0-9._-]") 49 49 + 50 50 + 51 51 + def sanitize(name: str) -> str: 52 52 + """Replace anything that isn't safe for an Android asset filename.""" 53 53 + return SAFE_NAME.sub("_", name).strip("_") or "unnamed" 54 54 + 55 55 + 56 56 + def list_runs() -> list[Path]: 57 57 + """Return run-id directories under collab/output, newest first by mtime.""" 58 58 + if not COLAB_OUTPUT_DIR.exists(): 59 59 + return [] 60 60 + runs = [p for p in COLAB_OUTPUT_DIR.iterdir() if p.is_dir()] 61 61 + runs.sort(key=lambda p: p.stat().st_mtime, reverse=True) 62 62 + return runs 63 63 + 64 64 + 65 65 + def cmd_list() -> int: 66 66 + runs = list_runs() 67 67 + if not runs: 68 68 + print(f"(no runs found under {COLAB_OUTPUT_DIR.relative_to(REPO_ROOT)})") 69 69 + return 0 70 70 + print(f"Runs under {COLAB_OUTPUT_DIR.relative_to(REPO_ROOT)} (newest first):") 71 71 + for run in runs: 72 72 + tflites = sorted(run.glob("*.tflite")) 73 73 + if tflites: 74 74 + tflite_summary = ", ".join(p.name for p in tflites) 75 75 + else: 76 76 + tflite_summary = "(no .tflite files)" 77 77 + print(f" {run.name} — {tflite_summary}") 78 78 + return 0 79 79 + 80 80 + 81 81 + def cmd_sync(run_id: str) -> int: 82 82 + run_dir = COLAB_OUTPUT_DIR / run_id 83 83 + if not run_dir.is_dir(): 84 84 + print(f"error: no such run directory: {run_dir}", file=sys.stderr) 85 85 + available = [r.name for r in list_runs()] 86 86 + if available: 87 87 + print(f" available: {', '.join(available)}", file=sys.stderr) 88 88 + return 2 89 89 + 90 90 + tflites = sorted(run_dir.glob("*.tflite")) 91 91 + if not tflites: 92 92 + print(f"error: no .tflite files in {run_dir}", file=sys.stderr) 93 93 + return 2 94 94 + 95 95 + if not ASSETS_DIR.exists(): 96 96 + print(f"error: assets dir does not exist: {ASSETS_DIR}", file=sys.stderr) 97 97 + return 2 98 98 + 99 99 + safe_run = sanitize(run_id) 100 100 + print(f"Syncing run '{run_id}' ({len(tflites)} model(s)) -> {ASSETS_DIR.relative_to(REPO_ROOT)}") 101 101 + for src in tflites: 102 102 + base = sanitize(src.stem) 103 103 + dest_name = f"{safe_run}__{base}.tflite" 104 104 + if dest_name in BASELINE_MODELS: 105 105 + print(f" skip {src.name} (would shadow baseline {dest_name})") 106 106 + continue 107 107 + dest = ASSETS_DIR / dest_name 108 108 + shutil.copy2(src, dest) 109 109 + size_mb = dest.stat().st_size / (1024 * 1024) 110 110 + print(f" copied {src.name} -> {dest.name} ({size_mb:.1f} MB)") 111 111 + return 0 112 112 + 113 113 + 114 114 + def cmd_clean() -> int: 115 115 + if not ASSETS_DIR.exists(): 116 116 + print(f"error: assets dir does not exist: {ASSETS_DIR}", file=sys.stderr) 117 117 + return 2 118 118 + removed = 0 119 119 + for f in sorted(ASSETS_DIR.glob("*.tflite")): 120 120 + if f.name in BASELINE_MODELS: 121 121 + continue 122 122 + f.unlink() 123 123 + print(f" removed {f.name}") 124 124 + removed += 1 125 125 + if removed == 0: 126 126 + print("(nothing to clean — only baseline models present)") 127 127 + else: 128 128 + print(f"removed {removed} synced model(s); baselines preserved") 129 129 + return 0 130 130 + 131 131 + 132 132 + def cmd_latest() -> int: 133 133 + runs = list_runs() 134 134 + if not runs: 135 135 + print(f"error: no runs under {COLAB_OUTPUT_DIR}", file=sys.stderr) 136 136 + return 2 137 137 + return cmd_sync(runs[0].name) 138 138 + 139 139 + 140 140 + def main(argv: list[str]) -> int: 141 141 + parser = argparse.ArgumentParser( 142 142 + description="Sync Colab-trained .tflite models into the sample app's assets dir.", 143 143 + ) 144 144 + group = parser.add_mutually_exclusive_group() 145 145 + group.add_argument("--list", action="store_true", help="list available runs newest-first") 146 146 + group.add_argument("--run", metavar="RUN_ID", help="sync the named run from collab/output/") 147 147 + group.add_argument("--clean", action="store_true", help="remove previously-synced models (preserve baselines)") 148 148 + args = parser.parse_args(argv) 149 149 + 150 150 + if args.list: 151 151 + return cmd_list() 152 152 + if args.clean: 153 153 + return cmd_clean() 154 154 + if args.run: 155 155 + return cmd_sync(args.run) 156 156 + # Default: sync the latest run. 157 157 + return cmd_latest() 158 158 + 159 159 + 160 160 + if __name__ == "__main__": 161 161 + sys.exit(main(sys.argv[1:]))