| --- |
| license: mit |
| library_name: pytorch |
| tags: |
| - computer-vision |
| - image-classification |
| - efficientnet |
| - surgical-tools |
| - surgical-instruments |
| - binary-classification |
| datasets: |
| - joonhaim/hinged-state-classifier-crops |
| --- |
| |
| # hinged-state-classifier-efficientnet-b0 |
|
|
| EfficientNet-B0 image classification model for surgical instrument state recognition. |
|
|
| ## Task |
| Binary classification of hinged surgical instrument state: |
| - closed |
| - open |
|
|
| ## Model details |
| - Architecture: EfficientNet-B0 |
| - Framework: PyTorch |
| - Checkpoint: `best.pt` |
| - Input image size: 224 × 224 |
|
|
| ## Dataset |
| Trained on: |
| - `joonhaim/hinged-state-classifier-crops` |
|
|
| ## Class mapping |
| - 0: closed |
| - 1: open |
|
|
| ## Notes |
| This is a custom PyTorch checkpoint for classifying the state of hinged surgical instruments from cropped images. |
|
|
| The class order must match the order used during training. |
