Create README.md

README.md

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+# 🧠 Beans-Image-Classification-AI-Model
+
+A fine-tuned image classification model trained on the Beans dataset with 3 classes: angular_leaf_spot, bean_rust, and healthy. This model is built using Hugging Face Transformers and the ViT (Vision Transformer) architecture and is suitable for educational use, plant disease classification tasks, and image classification experiments.
+
+---
+
+
+## ✨ Model Highlights
+
+- 📌 Base Model: google/vit-base-patch16-224-in21k
+- 📚 Fine-tuned:  Beans dataset
+- 🌿 Classes: angular_leaf_spot, bean_rust, healthy
+- 🔧 Framework: Hugging Face Transformers + PyTorch
+- 📦 Preprocessing: AutoImageProcessor from Transformers
+
+---
+
+## 🧠 Intended Uses
+
+- ✅ Educational tools for training and evaluation in agriculture and plant disease detection
+- ✅ Benchmarking vision transformer models on small datasets
+- ✅ Demonstration of fine-tuning workflows with Hugging Face
+
+---
+
+## 🚫 Limitations
+
+- ❌ Not suitable for real-world diagnosis in agriculture without further domain validation
+- ❌ Not robust to significant background noise or occlusion in images
+- ❌ Trained on small dataset, may not generalize beyond bean leaf diseases
+
+---
+
+📏 Input & Output
+
+- Input: RGB image of a bean leaf (expected size 224x224)
+- Output: Predicted class label — angular_leaf_spot, bean_rust, or healthy
+
+---
+
+## 🏋️‍♂️ Training Details
+
+| Attribute          | Value                            |
+|--------------------|----------------------------------|
+| Base Model         |`google/vit-base-patch16-224-in21k|
+| Dataset            |Beans Dataset (train/val/test)    |
+| Task Type          | Image Classification             |
+| Image Size         |  224 × 224                       |
+| Epochs             | 3                                |
+| Batch Size         | 16                               |
+| Optimizer          | AdamW                            |
+| Loss Function      | CrossEntropyLoss                 |
+| Framework          | PyTorch + Transformers           |
+| Hardware           | CUDA-enabled GPU                 |
+
+---
+
+## 📊 Evaluation Metrics
+
+
+| Metric                                          | Score |
+| ----------------------------------------------- | ----- |
+| Accuracy                                        | 0.98  |
+| F1-Score                                        | 0.99  |
+| Precision                                       | 0.98  |
+| Recall                                          | 0.99  |
+
+
+---
+
+---
+🚀 Usage
+```python
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+from PIL import Image
+import torch
+
+model_name = "AventIQ-AI/Beans-Image-Classification-AI-Model"
+
+processor = AutoImageProcessor.from_pretrained(model_name)
+model = AutoModelForImageClassification.from_pretrained(model_name)
+model.eval()
+
+def predict(image_path):
+    image = Image.open(image_path).convert("RGB")
+    inputs = processor(images=image, return_tensors="pt").to(model.device)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    preds = torch.argmax(outputs.logits, dim=1)
+    return model.config.id2label[preds.item()]
+
+# Example
+print(predict("example_leaf.jpg"))
+
+
+```
+---
+
+- 🧩 Quantization
+- Post-training static quantization applied using PyTorch to reduce model size and accelerate inference on edge devices.
+
+----
+
+🗂 Repository Structure
+```
+.
+beans-vit-finetuned/
+├── config.json               ✅ Model architecture & config
+├── pytorch_model.bin         ✅ Model weights
+├── preprocessor_config.json  ✅ Image processor config
+├── special_tokens_map.json   ✅ (Auto-generated, not critical for ViT)
+├── training_args.bin         ✅ Training metadata
+├── README.md                 ✅ Model card
+
+```
+---
+🤝 Contributing
+
+Open to improvements and feedback! Feel free to submit a pull request or open an issue if you find any bugs or want to enhance the model.
+
+