| --- |
| library_name: transformers |
| tags: [] |
| --- |
| |
| ## Original result |
| ``` |
| IoU metric: bbox |
| Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005 |
| Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.005 |
| Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.005 |
| Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.203 |
| Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.068 |
| Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005 |
| Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.029 |
| Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.029 |
| Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.029 |
| Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.200 |
| Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.067 |
| Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.029 |
| ``` |
|
|
| ## After training result |
| ``` |
| IoU metric: bbox |
| Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.009 |
| Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.020 |
| Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.008 |
| 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 ] = 0.009 |
| Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.043 |
| Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.076 |
| Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.087 |
| 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 ] = 0.089 |
| ``` |
|
|
| ## 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(8.5927, device='cuda:0'), 'validation_loss_ce': tensor(3.4775, device='cuda:0'), 'validation_loss_bbox': tensor(0.5756, device='cuda:0'), 'validation_loss_giou': tensor(1.1184, device='cuda:0'), 'validation_cardinality_error': tensor(99.5938, device='cuda:0')} |
| {'training_loss': tensor(1.3630, device='cuda:0'), 'train_loss_ce': tensor(0.2593, device='cuda:0'), 'train_loss_bbox': tensor(0.0803, device='cuda:0'), 'train_loss_giou': tensor(0.3511, device='cuda:0'), 'train_cardinality_error': tensor(0.5294, device='cuda:0'), 'validation_loss': tensor(1.5262, device='cuda:0'), 'validation_loss_ce': tensor(0.2351, device='cuda:0'), 'validation_loss_bbox': tensor(0.0827, device='cuda:0'), 'validation_loss_giou': tensor(0.4389, device='cuda:0'), 'validation_cardinality_error': tensor(0.4794, device='cuda:0')} |
| ``` |
| |
| ## 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])} |
| |
|  |
| |
