Instructions to use MedicalVision/test_remove_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MedicalVision/test_remove_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="MedicalVision/test_remove_2")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("MedicalVision/test_remove_2") model = AutoModelForObjectDetection.from_pretrained("MedicalVision/test_remove_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| tags: [] | |
| ## Original result | |
| ``` | |
| Not provided | |
| ``` | |
| ## After training result | |
| ``` | |
| IoU metric: bbox | |
| Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.006 | |
| Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.016 | |
| Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.004 | |
| Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 | |
| Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 | |
| Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006 | |
| Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.041 | |
| Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.077 | |
| Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.083 | |
| Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 | |
| Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 | |
| Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.085 | |
| ``` | |
| ## Config | |
| - dataset: VinXray | |
| - original model: hustvl/yolos-tiny | |
| - lr: 0.0001 | |
| - dropout_rate: 0.1 | |
| - weight_decay: 0.0001 | |
| - max_epochs: 1 | |
| - train samples: 67234 | |
| ## Logging | |
| ### Training process | |
| ``` | |
| {'validation_loss': tensor(7.8284, device='cuda:1'), 'validation_loss_ce': tensor(2.7671, device='cuda:1'), 'validation_loss_bbox': tensor(0.5730, device='cuda:1'), 'validation_loss_giou': tensor(1.0983, device='cuda:1'), 'validation_cardinality_error': tensor(98.8125, device='cuda:1')} | |
| {'training_loss': tensor(1.3821, device='cuda:1'), 'train_loss_ce': tensor(0.1972, device='cuda:1'), 'train_loss_bbox': tensor(0.0681, device='cuda:1'), 'train_loss_giou': tensor(0.4223, device='cuda:1'), 'train_cardinality_error': tensor(0.4118, device='cuda:1'), 'validation_loss': tensor(1.6166, device='cuda:1'), 'validation_loss_ce': tensor(0.2388, device='cuda:1'), 'validation_loss_bbox': tensor(0.0936, device='cuda:1'), 'validation_loss_giou': tensor(0.4548, device='cuda:1'), 'validation_cardinality_error': tensor(0.5118, device='cuda:1')} | |
| ``` | |
| ## Examples | |
| {'size': tensor([560, 512]), 'image_id': tensor([1]), 'class_labels': tensor([], dtype=torch.int64), 'boxes': tensor([], size=(0, 4)), 'area': tensor([]), 'iscrowd': tensor([], dtype=torch.int64), 'orig_size': tensor([2580, 2332])} | |
|  | |