Create README.md

README.md

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+# 🥭 FruitBench: A Multimodal Benchmark for Fruit Growth Understanding
+
+**Paper**: *FruitBench: A Multimodal Benchmark for Comprehensive Fruit Growth Understanding in Real-World Agriculture*  
+**Conference**: NeurIPS 2025 (submitted)  
+**Authors**: Jihao Li*, Jincheng Hu*, Pengyu Fu*, Ming Liu, et al.
+
+---
+
+## 📌 Dataset Summary
+
+**FruitBench** is the first large-scale multimodal benchmark designed to evaluate vision-language models on real-world agricultural understanding. It focuses on **fruit growth modeling**, supporting:
+
+- 🍎 Fruit Type Classification  
+- 🌱 Growth Stage Recognition (`unripe`, `pest-damaged`, `mature`, `rotten`)  
+- 🌾 Agricultural Action Recommendation  
+- 🍽️ Consumer Score Prediction (1–100)
+
+The dataset contains **3,200 high-quality expert-annotated images** covering **16 fruit categories**, each across **4 growth stages**.
+
+---
+
+## 🗂️ Dataset Structure
+
+- 16 Fruit Types: `strawberry`, `tomato`, `guava`, `dragon fruit`, `orange`, `pear`, `lychee`, `mango`, `kiwi`, `papaya`, `apple`, `grape`, `pomegranate`, `peach`, `banana`, `pomelo`
+- 4 Growth Stages: `unripe`, `mature`, `pest-damaged`, `rotten`
+- Each image is annotated with:
+  - Fruit type  
+  - Growth stage  
+  - Recommended action (`keep for further growth`, `try to recover it`, `discard it`)  
+  - Consumer score (1–100 rating from 30 human raters)
+
+---
+
+## 🔍 Tasks
+
+1. **Type Classification**  
+2. **Growth Stage Identification**  
+3. **Action Recommendation**  
+4. **Consumer Score Prediction**
+
+---
+
+## 📦 Data Format
+
+Organized in `imagefolder` style:
+