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| license: cc-by-nc-4.0 |
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| # Food Portion Benchmark (FPB) Dataset |
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| The **Food Portion Benchmark (FPB)** is a comprehensive dataset and benchmark suite for multi-task food scene understanding, combining **food detection** and **portion size (weight) estimation**. It was introduced to support research in dietary analysis, nutrition tracking, and food computing. The dataset is built with high-quality annotations and evaluated using an extended YOLOv12-based multi-task model . |
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| ## 📦 Dataset Overview |
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| - **Total images**: 14,083 |
| - **Food classes**: 138 |
| - **Annotations**: Bounding boxes + Ground-truth weights (in grams) |
| - **Image angles**: Top-down and four side views |
| - **Cameras**: Intel RealSense D455 + smartphones |
| - **Split**: Train (9,521) / Validation (2,365) / Test (2,197) |
| - **Collection setting**: Controlled lab environment using local Central Asian cuisine |
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| Each food item was weighed and categorized into small, medium, or large portions. Images were captured from different angles to enable robust volume and weight estimation. |
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| ## 📁 Dataset Structure and Format |
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| The FPB dataset follows the **YOLO annotation format**, with a custom 6th column for **food weight (in grams)**. |
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| ### 🧾 Label Format (YOLO-style with weight) |
| - `class_id`: ID of the food class (0–137) |
| - `x_center, y_center, width, height`: Bounding box coordinates (normalized to [0, 1]) |
| - `weight`: Ground truth weight in grams (used for regression) |
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| Each `.txt` file matches the name of its corresponding image file. |
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| ## 📥 Dataset Access & Benchmarking |
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| - 📦 **Download Dataset**: [Hugging Face link](https://huggingface.co/datasets/issai/Food_Portion_Benchmark) |
| - 🚀 **Evaluate Your Model**: Submit predictions on the test set using the [automated score-checker](https://huggingface.co/datasets/issai/Food_Portion_Benchmark/tree/main/score-checker) |
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| Test labels are hidden to ensure fair evaluation. |
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| ## 🧠 Model Overview |
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| The baseline model is a **YOLOv12** multitask variant, extended with a **regression head** for predicting food weight (see Figure below). It was designed to be **agnostic to missing labels**, making it compatible with datasets that do not have weight annotations. |
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| Github Source Code: [Multitask-Food-Portion-Estimation](https://github.com/IS2AI/Multitask-Food-Portion-Estimation) |
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| ### Best Model (YOLOv12-M @ 640×640): |
| - **Detection**: mAP50 = 0.974, mAP50-95 = 0.948 |
| - **Weight Estimation**: MAE = 90.95g |
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| ## 🧪 Performance Tables |
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| ### Table 1: Performance of YOLOv12M at different resolutions |
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| ### Table 2: YOLOv8 vs YOLOv12 on FPB |
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| ## 🏋️♂️ Training |
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| Train the multi-task YOLOv12 model using `train.py` |
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| ## 🔍 Inference |
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| Download the trained best models from the [drive link](https://drive.google.com/drive/folders/1XbgdXzfX73PxUUxthcbcqbY-1TNRK51d?usp=sharing) and run inference on test images using `test.py` |
| - Provide path to your images folder or image file |
| - Replace `model` with the path to the downloaded model |
| - Set `show=True` to save annotated images with bounding boxes and predicted weights |
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| ## 📚 In case of using our work in your research, please cite this paper |
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| <pre> @article{Sanatbyek_2025, |
| title={A multitask deep learning model for food scene recognition and portion estimation—the Food Portion Benchmark (FPB) dataset}, |
| volume={13}, |
| DOI={10.1109/access.2025.3603287}, |
| journal={IEEE Access}, |
| author={Sanatbyek, Aibota and Rakhimzhanova, Tomiris and Nurmanova, Bibinur and Omarova, Zhuldyz and Rakhmankulova, Aidana and Orazbayev, Rustem and Varol, Huseyin Atakan and Chan, Mei Yen}, |
| year={2025}, |
| pages={152033–152045} |
| } |
| </pre> |
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| ## References |
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| [1] Tian, Y., Ye, Q., & Doermann, D. (2025). YOLOv12: Attention-centric real-time object detectors. arXiv. https://arxiv.org/abs/2502.12524 |
| [2] https://github.com/ultralytics/ultralytics |
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