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README.md

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-license: cc-by-nc-4.0
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+---
+license: cc-by-nc-4.0
+---
+
+
+# Food Portion Benchmark (FPB) Dataset
+
+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 .
+
+---
+
+## 📦 Dataset Overview
+
+- **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
+
+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.
+![Portion examples](figures/portion_sizes.png)
+
+---
+
+## 🧠 Model Overview
+
+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.
+![Alt text](figures/model.png)
+
+
+
+### Best Model (YOLOv12-M @ 640×640):
+- **Detection**: mAP50 = 0.974, mAP50-95 = 0.948
+- **Weight Estimation**: MAE = 90.95g
+
+---
+
+## 🧪 Performance Tables
+
+### Table 1: Performance of YOLOv12M at different resolutions
+> Include from paper (Table 3)
+
+### Table 2: YOLOv8 vs YOLOv12 on FPB
+> Include from paper (Table 4)
+
+
+
+---
+
+## 📥 Dataset Access & Benchmarking
+
+- 📦 **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)
+
+Test labels are hidden to ensure fair evaluation.
+
+
+
+
+---
+
+## 📚 In case of using our work in your research, please cite this paper
+
+@article{sanatbyeka2025multitask,
+  title={A Multitask Deep Learning Model for Food Scene Recognition and Portion Estimation},
+  author={Sanatbyeka, Aibota and Rakhimzhanova, Tomiris and Varol, Huseyin Atakan and Chan, Mei Yen},
+  journal={AI Open},
+  year={2025},
+  note={Preprint submitted April 7, 2025}
+}
+
+
+## References
+
+[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
+