Update README.md

README.md

@@ -89,7 +89,7 @@ configs:
 
 - 🍎 Fruit Type Classification  
 - 🌱 Growth Stage Recognition (`unripe`, `pest-damaged`, `mature`, `rotten`)  
-- 🌾 Agricultural Action Recommendation  
+- 🌾 Agricultural Action Recommendation  ('keep for further growth', 'picking it', 'try to recover it', 'discard it')
 - 🍽️ Consumer Score Prediction (1–100)
 
 The dataset contains **3,200 high-quality expert-annotated images** covering **16 fruit categories**, each across **4 growth stages**.
@@ -105,7 +105,6 @@ The dataset contains **3,200 high-quality expert-annotated images** covering **1
   - Growth stage  
   - Recommended action (`keep for further growth`, `try to recover it`, `discard it`)  
   - Consumer score (1–100 rating from 30 human raters)
-
 ---
 
 ## 🔍 Tasks
@@ -114,10 +113,51 @@ The dataset contains **3,200 high-quality expert-annotated images** covering **1
 2. **Growth Stage Identification**  
 3. **Action Recommendation**  
 4. **Consumer Score Prediction**
+   
+All tasks are evaluated under both **zero-shot** and **one-shot** settings using multimodal large language models (MLLMs).
+
+---
+
+## ⚙️ Environment Setup
+
+We provide both `conda` and `pip` setup options (Python 3.11 recommended).
+
+### ✅ Option A: Conda (Recommended)
+
+```bash
+conda env create -f environment.yml
+conda activate fruitbench
+```
 
+### ✅ Option B: pip
+
+```bash
+pip install -r requirements.txt
+```
 ---
 
-## 📦 Data Format
+## 🚀 Usage
+
+### 1. Clone the Repository
+
+```bash
+git lfs install
+git clone https://huggingface.co/datasets/TJIET/FruitBench
+
+```
+
+### 3. Evaluate Models
+
+```bash
+python scripts/evaluate_models.py
+```
+
+## 📊 Benchmark Details
 
-Organized in `imagefolder` style:1
+- ✅ 3,200 annotated fruit images
+- 📦 16 fruit types: strawberry, tomato, guava, dragon fruit, orange, pear, lychee, mango, kiwi, papaya, apple, grape, pomegranate, peach, banana, pomelo
+- 🌱 4 growth stages: unripe, pest-damaged, mature, rotten
+- 🧑‍🌾 Expert action labels: keep for growth / pick it / recover / discard
+- 🎯 Consumer scores: average of 30 human ratings (range: 1–100)
 
+---