---
language: en
license: mit
tags:
- pytorch
- plant-disease
- image-classification
- agriculture
- computer-vision
datasets:
- plant-village
---

# Plant Disease Classification Model

🌱 EfficientNet-B2 based model for classifying plant diseases in apples, tomatoes, and corn (maize).

## Model Details

### Architecture
- **Backbone**: EfficientNet-B2 (pretrained)
- **Custom Head**: 
  - Attention mechanism
  - 3 dense layers (512, 256, num_classes)
  - Dropout regularization (0.3)

### Training Data
- **Dataset**: PlantVillage Dataset
- **Classes**: 14 total (4 Apple, 6 Tomato, 4 Corn diseases + healthy)
- **Train/Val/Test Split**: 80%/10%/10%
- **Image Size**: 224x224

### Performance Metrics
| Metric       | Value   |
|--------------|---------|
| Train Accuracy | 98.66% |
| Val Accuracy   | 99.24% |
| Test Accuracy  | 98.91% |

## How to Use

### Inference
```python
from transformers import AutoModelForImageClassification
from PIL import Image
import torch
import torchvision.transforms as transforms

# Load model
model = AutoModelForImageClassification.from_pretrained("Abuzaid01/plant-disease-classifier")

# Preprocess image
transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# Load and transform image
image = Image.open("plant.jpg")
inputs = transform(image).unsqueeze(0)

# Predict
with torch.no_grad():
    outputs = model(inputs)
    prediction = torch.argmax(outputs.logits, dim=1).item()

print(f"Predicted class: {model.config.id2label[prediction]}")