README.md

---
library_name: pytorch
tags:
- anomaly-detection
- autoencoder
- plant-detection
- computer-vision
- pytorch-lightning
datasets:
- custom-plant-dataset
metrics:
- reconstruction-error
- threshold-based-classification
pipeline_tag: image-classification
---

# plant-detector

## Model Description

Convolutional Autoencoder for plant anomaly detection

This is a Convolutional Autoencoder (CAE) trained for plant anomaly detection. The model learns to reconstruct plant images and detects anomalies based on reconstruction error.

## Model Details

- **Model Type**: Convolutional Autoencoder
- **Framework**: PyTorch Lightning
- **Task**: Anomaly Detection / Plant Classification
- **Input**: RGB images (224x224)
- **Output**: Reconstruction + anomaly score

## Training Details

- **Architecture**: Encoder-Decoder with skip connections
- **Loss Function**: Mean Squared Error (MSE)
- **Optimizer**: AdamW
- **Learning Rate**: 0.0001
- **Batch Size**: 32
- **Epochs**: N/A
- **Dataset Size**: N/A images

## Performance Metrics

- **Validation Loss**: N/A
- **Threshold**: 0.5687
- **Mean Reconstruction Error**: N/A
- **Std Reconstruction Error**: N/A
- **Anomaly Rate**: N/A

## Normalization Statistics

The model expects input images to be normalized with:
- **Mean**: [0.4682, 0.4865, 0.3050]
- **Std**: [0.2064, 0.1995, 0.1961]

## Usage

### PyTorch Lightning Checkpoint

```python
from annomallyDet.models.lit_models.lit_cae import LitCAE

# Load the model
model = LitCAE.load_from_checkpoint("plant_anomaly_detector.ckpt")
model.eval()

# Make prediction
reconstruction_error = model.get_reconstruction_error(input_tensor)
is_anomaly = reconstruction_error > 0.5687
```

### Mobile Deployment (TorchScript Lite)

```python
import torch

# Load mobile model
model = torch.jit.load("plant_anomaly_detector.ptl")
reconstruction = model(input_tensor)

# Calculate reconstruction error
error = torch.mean((input_tensor - reconstruction) ** 2)
is_anomaly = error > 0.5687
```

### Flutter Integration

See the included `flutter_integration_example.dart` for complete Flutter app integration using `flutter_pytorch_lite`.

## Files Included

- `plant_anomaly_detector.ckpt`: PyTorch Lightning checkpoint
- `plant_anomaly_detector.ptl`: TorchScript Lite model for mobile deployment
- `config.json`: Model configuration and metadata
- `flutter_integration_example.dart`: Flutter integration example
- `normalization_stats.json`: Dataset normalization statistics

## Model Architecture

```
Input (3, 224, 224)
    ↓
Encoder: Conv2d → BatchNorm → LeakyReLU → Dropout
    [32, 64, 128, 256] channels
    ↓
Latent Space (128 dimensions)
    ↓
Decoder: ConvTranspose2d → BatchNorm → LeakyReLU → Dropout
    [256, 128, 64, 32] channels
    ↓
Output (3, 224, 224)
```

## Anomaly Detection Logic

1. **Training**: Model learns to reconstruct normal plant images
2. **Inference**: Calculate reconstruction error (MSE)
3. **Decision**: If error > threshold → Anomaly (not a plant)
4. **Confidence**: Distance from threshold indicates confidence

## Limitations

- Trained specifically on plant images
- Performance depends on similarity to training data
- May struggle with novel plant species not in training set
- Threshold may need adjustment for different use cases

## Citation

```bibtex
@misc{plant_anomaly_detector,
  title={Plant Anomaly Detection using Convolutional Autoencoder},
  author={Your Name},
  year={2024},
  howpublished={\url{https://huggingface.co/YOUR_USERNAME/plant-detector}},
}
```

## License

[Specify your license here]