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# Models Directory
This directory will contain the ONNX models after conversion.
## Structure
After running the conversion scripts, this directory will contain:
```
models/
β”œβ”€β”€ classifier_model_compressed/
β”‚ β”œβ”€β”€ model.onnx # ONNX model for embryo detection
β”‚ β”œβ”€β”€ config.json # Model configuration
β”‚ └── preprocessor_config.json # Image preprocessing config
β”‚
β”œβ”€β”€ poor_good_compressed/
β”‚ β”œβ”€β”€ model.onnx # ONNX model for quality assessment
β”‚ β”œβ”€β”€ config.json # Model configuration
β”‚ └── preprocessor_config.json # Image preprocessing config
β”‚
β”œβ”€β”€ grader_model_compressed/
β”‚ β”œβ”€β”€ model.onnx # ONNX model for Gardner grading
β”‚ β”œβ”€β”€ config.json # Model configuration
β”‚ └── preprocessor_config.json # Image preprocessing config
β”‚
└── yolo-cropper/
└── best.onnx # ONNX model for embryo detection
```
## How to Generate Models
Run the conversion scripts from the parent directory:
```bash
# Convert SigLIP models
python convert_to_onnx.py
# Convert YOLO model
python convert_yolo_to_onnx.py
```
## Model Sizes (Approximate)
- Classifier: ~95 MB
- Poor/Good: ~95 MB
- Grader: ~95 MB
- YOLO: ~6 MB
Total: ~290 MB
## Note
These models are not included in the repository due to their size.
You must convert them from the original PyTorch models.