Original result
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
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 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
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 ] = 0.004
After training result
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.016
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.028
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.020
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
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 ] = 0.016
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.068
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.123
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.135
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
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 ] = 0.136
Config
- dataset: NIH
- original model: hustvl/yolos-tiny
- lr: 5e-06
- dropout_rate: 0.1
- weight_decay: 0.05
- max_epochs: 300
- train samples: 885
Logging
Training process
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{'training_loss': tensor(0.4228, device='cuda:0'), 'train_loss_ce': tensor(0.2680, device='cuda:0'), 'train_loss_bbox': tensor(0.0133, device='cuda:0'), 'train_loss_giou': tensor(0.0442, device='cuda:0'), 'train_cardinality_error': tensor(0.8000, device='cuda:0'), 'validation_loss': tensor(2.0364, device='cuda:0'), 'validation_loss_ce': tensor(0.3581, device='cuda:0'), 'validation_loss_bbox': tensor(0.1353, device='cuda:0'), 'validation_loss_giou': tensor(0.5009, device='cuda:0'), 'validation_cardinality_error': tensor(0.9394, device='cuda:0')}
{'training_loss': tensor(0.5344, device='cuda:0'), 'train_loss_ce': tensor(0.3058, device='cuda:0'), 'train_loss_bbox': tensor(0.0090, device='cuda:0'), 'train_loss_giou': tensor(0.0917, device='cuda:0'), 'train_cardinality_error': tensor(0.8000, device='cuda:0'), 'validation_loss': tensor(2.0196, device='cuda:0'), 'validation_loss_ce': tensor(0.3588, device='cuda:0'), 'validation_loss_bbox': tensor(0.1336, device='cuda:0'), 'validation_loss_giou': tensor(0.4964, device='cuda:0'), 'validation_cardinality_error': tensor(0.9394, device='cuda:0')}
Examples
{'size': tensor([512, 512]), 'image_id': tensor([1]), 'class_labels': tensor([4]), 'boxes': tensor([[0.2622, 0.5729, 0.0847, 0.0773]]), 'area': tensor([1717.9431]), 'iscrowd': tensor([0]), 'orig_size': tensor([1024, 1024])}
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