---
language: en
tags:
- pytorch
- computer-vision
- image-classification
- rice-disease
license: mit
---

# VGG16-CNN Rice Disease Classification Model

This model is designed for classifying rice plant diseases using a modified VGG16 architecture with additional CNN layers.

## Model Description

### Architecture
- Base model: VGG16 (pretrained on ImageNet)
- Additional custom CNN layer with:
  - Conv2d(512, 64, kernel_size=3)
  - ReLU activation
  - BatchNorm2d
  - MaxPool2d
- Custom classifier with:
  - Linear layers (32*3*6 → 1024 → 5)
  - Dropout (0.4)

### Task
Image classification for rice plant diseases

### Classes
1. Bacterialblight
2. Blast
3. Brownspot
4. Healthy
5. Tungro

## Training

The model uses transfer learning with a frozen VGG16 backbone.

## Intended Use
- Primary intended use: Rice disease diagnosis through leaf image analysis
- Out-of-scope use: Should not be used for critical agricultural decisions without expert verification

## Input
- RGB images
- Required size: 224x224 pixels
- Preprocessing:
  - Normalization (mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])

## Limitations
Please note that this model should be used as a supportive tool and not as a sole decision-maker for disease diagnosis.

## Model Author
[Your Name/Organization]

## Citation
If you use this model, please cite:
```
@software{vgg_cnn_rice_disease,
  title={VGG16-CNN Rice Disease Classification Model},
  version={0.1.0},
  year={2024}
}
```