# 🧠 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.

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## ✨ 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

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## 🧠 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

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## 🚫 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

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📏 Input & Output

- Input: RGB image of a bean leaf (expected size 224x224)
- Output: Predicted class label — angular_leaf_spot, bean_rust, or healthy

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## 🏋️‍♂️ 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                 |

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## 📊 Evaluation Metrics


| Metric                                          | Score |
| ----------------------------------------------- | ----- |
| Accuracy                                        | 0.98  |
| F1-Score                                        | 0.99  |
| Precision                                       | 0.98  |
| Recall                                          | 0.99  |


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🚀 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"))


```
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- 🧩 Quantization
- Post-training static quantization applied using PyTorch to reduce model size and accelerate inference on edge devices.

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🗂 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

```
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🤝 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.