Update README.md

README.md

@@ -22,8 +22,33 @@ The **Food Portion Benchmark (FPB)** is a comprehensive dataset and benchmark su
 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)
 
+
+## 📁 Dataset Structure and Format
+
+The FPB dataset follows the **YOLO annotation format**, with a custom 6th column for **food weight (in grams)**.
+
+### 🧾 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)
+
+Each `.txt` file matches the name of its corresponding image file.
+
+
+---
+
+## 📥 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.
+
+
 ---
 
+
+
 ## 🧠 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.
@@ -47,16 +72,25 @@ The baseline model is a **YOLOv12** multitask variant, extended with a **regress
 
 
 
----
 
-## 📥 Dataset Access & Benchmarking
+## 🏋️‍♂️ Training
 
-- 📦 **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)
+Train the multi-task YOLOv12 model using:
 
-Test labels are hidden to ensure fair evaluation.
+</pre>
+python train.py
+</pre>
 
+## 🔍 Inference
 
+Download the trained best model from the [drive link](https://huggingface.co/datasets/issai/Food_Portion_Benchmark) and run inference on test images using:
+
+</pre>
+python test.py
+</pre>
+- 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
 
 
 ---