MedicalVision/yolos_mini_overfitting

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 ] = -1.000
 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 ] = 0.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
 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 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000

After training result

IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.900
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 1.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 1.000
 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 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.900
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.900
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.900
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.900
 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 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.900

Config

• dataset: NIH
• original model: hustvl/yolos-tiny
• lr: 0.0001
• dropout_rate: 0.1
• weight_decay: 0.0001
• max_epochs: 300
• train samples: 2

Logging

Training process

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{'training_loss': tensor(0.2686, device='cuda:0'), 'train_loss_ce': tensor(0.0033, device='cuda:0'), 'train_loss_bbox': tensor(0.0093, device='cuda:0'), 'train_loss_giou': tensor(0.1093, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.3041, device='cuda:0'), 'validation_loss_ce': tensor(0.0033, device='cuda:0'), 'validation_loss_bbox': tensor(0.0127, device='cuda:0'), 'validation_loss_giou': tensor(0.1188, device='cuda:0'), 'validation_cardinality_error': tensor(0., device='cuda:0')}
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{'training_loss': tensor(0.2773, device='cuda:0'), 'train_loss_ce': tensor(0.0033, device='cuda:0'), 'train_loss_bbox': tensor(0.0107, device='cuda:0'), 'train_loss_giou': tensor(0.1103, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.1628, device='cuda:0'), 'validation_loss_ce': tensor(0.0033, device='cuda:0'), 'validation_loss_bbox': tensor(0.0071, device='cuda:0'), 'validation_loss_giou': tensor(0.0619, device='cuda:0'), 'validation_cardinality_error': tensor(0., device='cuda:0')}
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{'training_loss': tensor(0.2424, device='cuda:0'), 'train_loss_ce': tensor(0.0033, device='cuda:0'), 'train_loss_bbox': tensor(0.0100, device='cuda:0'), 'train_loss_giou': tensor(0.0945, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.3781, device='cuda:0'), 'validation_loss_ce': tensor(0.0033, device='cuda:0'), 'validation_loss_bbox': tensor(0.0131, device='cuda:0'), 'validation_loss_giou': tensor(0.1547, device='cuda:0'), 'validation_cardinality_error': tensor(0., device='cuda:0')}
{'training_loss': tensor(0.3781, device='cuda:0'), 'train_loss_ce': tensor(0.0033, device='cuda:0'), 'train_loss_bbox': tensor(0.0131, device='cuda:0'), 'train_loss_giou': tensor(0.1547, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.3194, device='cuda:0'), 'validation_loss_ce': tensor(0.0033, device='cuda:0'), 'validation_loss_bbox': tensor(0.0100, device='cuda:0'), 'validation_loss_giou': tensor(0.1330, device='cuda:0'), 'validation_cardinality_error': tensor(0., device='cuda:0')}
{'training_loss': tensor(0.3194, device='cuda:0'), 'train_loss_ce': tensor(0.0033, device='cuda:0'), 'train_loss_bbox': tensor(0.0100, device='cuda:0'), 'train_loss_giou': tensor(0.1330, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.2167, device='cuda:0'), 'validation_loss_ce': tensor(0.0033, device='cuda:0'), 'validation_loss_bbox': tensor(0.0094, device='cuda:0'), 'validation_loss_giou': tensor(0.0832, device='cuda:0'), 'validation_cardinality_error': tensor(0., device='cuda:0')}
{'training_loss': tensor(0.2167, device='cuda:0'), 'train_loss_ce': tensor(0.0033, device='cuda:0'), 'train_loss_bbox': tensor(0.0094, device='cuda:0'), 'train_loss_giou': tensor(0.0832, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.1862, device='cuda:0'), 'validation_loss_ce': tensor(0.0033, device='cuda:0'), 'validation_loss_bbox': tensor(0.0097, device='cuda:0'), 'validation_loss_giou': tensor(0.0672, device='cuda:0'), 'validation_cardinality_error': tensor(0., device='cuda:0')}
{'training_loss': tensor(0.1862, device='cuda:0'), 'train_loss_ce': tensor(0.0033, device='cuda:0'), 'train_loss_bbox': tensor(0.0097, device='cuda:0'), 'train_loss_giou': tensor(0.0672, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.2871, device='cuda:0'), 'validation_loss_ce': tensor(0.0033, device='cuda:0'), 'validation_loss_bbox': tensor(0.0123, device='cuda:0'), 'validation_loss_giou': tensor(0.1112, device='cuda:0'), 'validation_cardinality_error': tensor(0., device='cuda:0')}
{'training_loss': tensor(0.2871, device='cuda:0'), 'train_loss_ce': tensor(0.0033, device='cuda:0'), 'train_loss_bbox': tensor(0.0123, device='cuda:0'), 'train_loss_giou': tensor(0.1112, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.2668, device='cuda:0'), 'validation_loss_ce': tensor(0.0033, device='cuda:0'), 'validation_loss_bbox': tensor(0.0117, device='cuda:0'), 'validation_loss_giou': tensor(0.1025, device='cuda:0'), 'validation_cardinality_error': tensor(0., device='cuda:0')}
{'training_loss': tensor(0.2668, device='cuda:0'), 'train_loss_ce': tensor(0.0033, device='cuda:0'), 'train_loss_bbox': tensor(0.0117, device='cuda:0'), 'train_loss_giou': tensor(0.1025, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.1305, device='cuda:0'), 'validation_loss_ce': tensor(0.0032, device='cuda:0'), 'validation_loss_bbox': tensor(0.0065, device='cuda:0'), 'validation_loss_giou': tensor(0.0475, device='cuda:0'), 'validation_cardinality_error': tensor(0., device='cuda:0')}
{'training_loss': tensor(0.1305, device='cuda:0'), 'train_loss_ce': tensor(0.0032, device='cuda:0'), 'train_loss_bbox': tensor(0.0065, device='cuda:0'), 'train_loss_giou': tensor(0.0475, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.2405, device='cuda:0'), 'validation_loss_ce': tensor(0.0031, device='cuda:0'), 'validation_loss_bbox': tensor(0.0114, device='cuda:0'), 'validation_loss_giou': tensor(0.0903, device='cuda:0'), 'validation_cardinality_error': tensor(0., device='cuda:0')}

Examples

{'size': tensor([512, 512]), 'image_id': tensor([735]), 'class_labels': tensor([5]), 'boxes': tensor([[0.7651, 0.5307, 0.0952, 0.0942]]), 'area': tensor([2351.3042]), 'iscrowd': tensor([0]), 'orig_size': tensor([1024, 1024])}