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---
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]
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