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README.md

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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]