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| title: Food Weight Benchmark | |
| emoji: 🥇 | |
| colorFrom: green | |
| colorTo: indigo | |
| sdk: gradio | |
| app_file: app.py | |
| pinned: true | |
| license: cc-by-nc-4.0 | |
| short_description: Food detection and weight prediction benchmark | |
| sdk_version: 5.19.0 | |
| # Configuration | |
| This leaderboard is designed to evaluate CSV submissions containing predictions for object detection and food weight estimation. | |
| ## Submission Format | |
| Submissions should be provided as CSV files and must include the following columns: | |
| `image_id, xmin, ymin, xmax, ymax, weight` | |
| - **image_id**: Unique identifier for the image. | |
| - **xmin, ymin, xmax, ymax**: Coordinates for the predicted bounding box. | |
| - **weight**: Predicted food weight in grams. | |
| ## Evaluation Process | |
| - **Ground Truth:** | |
| The hidden ground truth labels are stored in a CSV file located at `data/ground_truth.csv` and should have the same column format as above. | |
| - **Evaluation Steps:** | |
| 1. The application uploads the user’s submission CSV file. | |
| 2. It merges the submission with the ground truth data based on `image_id`. | |
| 3. Computes the Intersection over Union (IoU) for the bounding boxes. | |
| 4. Computes the absolute error for food weight predictions. | |
| 5. Calculates overall metrics such as: | |
| - **Mean IoU** | |
| - **Mean Weight Error (in grams)** | |
| - **Combined Score** (by default, defined as: | |
| `mean_iou - (mean_weight_error / 100.0)` — adjust as needed) | |
| - **Leaderboard Persistence:** | |
| Evaluation results are stored in a CSV file (default filename: `evaluation_results.csv`) with the following columns: | |
| `submission_id, mean_iou, mean_weight_error, combined_score` | |
| - **submission_id:** A unique timestamp-based identifier for each submission. | |
| - **mean_iou:** Average IoU calculated across all predictions. | |
| - **mean_weight_error:** Average absolute error (in grams) between predicted and true weights. | |
| - **combined_score:** A custom score that reflects both detection quality and weight prediction accuracy. | |
| ## Restarting the Space | |
| If you encounter any issues (for example, if the evaluation queues or result folders become problematic), please restart the space. This action will clear the directories such as `eval-queue`, `eval-queue-bk`, `eval-results`, and `eval-results-bk`. | |
| # Code Logic for Advanced Configuration | |
| For more complex edits or customizations, consider the following: | |
| - **Evaluation Logic:** | |
| The functions for reading the CSV submissions, computing IoU, and weight error are defined in `app.py`. Adjust these functions if you need to change how metrics are computed. | |
| - **Leaderboard Persistence:** | |
| The leaderboard is maintained by appending evaluation results to the `evaluation_results.csv` file. You can modify this behavior in `app.py` if you wish to use a different persistence method. | |
| - **User Interface:** | |
| The Gradio interface (submission upload, leaderboard refresh, etc.) is also implemented in `app.py`. You can enhance or simplify the UI by modifying this file. | |
| This README should help you configure and customize your leaderboard for object detection and food weight evaluation. Feel free to adjust any sections to better fit your project needs. | |