MedicalVision/detr_test_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 ] = 1.000
 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 ] = 1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 1.000
 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 ] = 1.000

Config

• dataset: NIH
• original model: facebook/detr-resnet-50
• 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(0.3079, device='cuda:0'), 'train_loss_ce': tensor(0.1691, device='cuda:0'), 'train_loss_bbox': tensor(0.0052, device='cuda:0'), 'train_loss_giou': tensor(0.0564, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(0.2645, device='cuda:0'), 'validation_loss_ce': tensor(0.1671, device='cuda:0'), 'validation_loss_bbox': tensor(0.0049, device='cuda:0'), 'validation_loss_giou': tensor(0.0366, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')}
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{'training_loss': tensor(0.3412, device='cuda:0'), 'train_loss_ce': tensor(0.1660, device='cuda:0'), 'train_loss_bbox': tensor(0.0074, device='cuda:0'), 'train_loss_giou': tensor(0.0690, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(0.3066, device='cuda:0'), 'validation_loss_ce': tensor(0.1650, device='cuda:0'), 'validation_loss_bbox': tensor(0.0061, device='cuda:0'), 'validation_loss_giou': tensor(0.0554, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')}
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{'training_loss': tensor(0.2413, device='cuda:0'), 'train_loss_ce': tensor(0.1640, device='cuda:0'), 'train_loss_bbox': tensor(0.0045, device='cuda:0'), 'train_loss_giou': tensor(0.0274, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(0.2695, device='cuda:0'), 'validation_loss_ce': tensor(0.1624, device='cuda:0'), 'validation_loss_bbox': tensor(0.0040, device='cuda:0'), 'validation_loss_giou': tensor(0.0435, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')}
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{'training_loss': tensor(0.2887, device='cuda:0'), 'train_loss_ce': tensor(0.1601, device='cuda:0'), 'train_loss_bbox': tensor(0.0067, device='cuda:0'), 'train_loss_giou': tensor(0.0476, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(0.2860, device='cuda:0'), 'validation_loss_ce': tensor(0.1577, device='cuda:0'), 'validation_loss_bbox': tensor(0.0063, device='cuda:0'), 'validation_loss_giou': tensor(0.0485, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')}
{'training_loss': tensor(0.2860, device='cuda:0'), 'train_loss_ce': tensor(0.1577, device='cuda:0'), 'train_loss_bbox': tensor(0.0063, device='cuda:0'), 'train_loss_giou': tensor(0.0485, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(0.2240, device='cuda:0'), 'validation_loss_ce': tensor(0.1560, device='cuda:0'), 'validation_loss_bbox': tensor(0.0036, device='cuda:0'), 'validation_loss_giou': tensor(0.0250, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')}
{'training_loss': tensor(0.2240, device='cuda:0'), 'train_loss_ce': tensor(0.1560, device='cuda:0'), 'train_loss_bbox': tensor(0.0036, device='cuda:0'), 'train_loss_giou': tensor(0.0250, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(0.2140, device='cuda:0'), 'validation_loss_ce': tensor(0.1546, device='cuda:0'), 'validation_loss_bbox': tensor(0.0028, device='cuda:0'), 'validation_loss_giou': tensor(0.0227, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')}
{'training_loss': tensor(0.2140, device='cuda:0'), 'train_loss_ce': tensor(0.1546, device='cuda:0'), 'train_loss_bbox': tensor(0.0028, device='cuda:0'), 'train_loss_giou': tensor(0.0227, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(0.2635, device='cuda:0'), 'validation_loss_ce': tensor(0.1530, device='cuda:0'), 'validation_loss_bbox': tensor(0.0049, device='cuda:0'), 'validation_loss_giou': tensor(0.0430, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')}
{'training_loss': tensor(0.2635, device='cuda:0'), 'train_loss_ce': tensor(0.1530, device='cuda:0'), 'train_loss_bbox': tensor(0.0049, device='cuda:0'), 'train_loss_giou': tensor(0.0430, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(0.2690, device='cuda:0'), 'validation_loss_ce': tensor(0.1504, device='cuda:0'), 'validation_loss_bbox': tensor(0.0053, device='cuda:0'), 'validation_loss_giou': tensor(0.0460, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')}
{'training_loss': tensor(0.2690, device='cuda:0'), 'train_loss_ce': tensor(0.1504, device='cuda:0'), 'train_loss_bbox': tensor(0.0053, device='cuda:0'), 'train_loss_giou': tensor(0.0460, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(0.2354, device='cuda:0'), 'validation_loss_ce': tensor(0.1478, device='cuda:0'), 'validation_loss_bbox': tensor(0.0050, device='cuda:0'), 'validation_loss_giou': tensor(0.0313, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')}
{'training_loss': tensor(0.2354, device='cuda:0'), 'train_loss_ce': tensor(0.1478, device='cuda:0'), 'train_loss_bbox': tensor(0.0050, device='cuda:0'), 'train_loss_giou': tensor(0.0313, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(0.2619, device='cuda:0'), 'validation_loss_ce': tensor(0.1454, device='cuda:0'), 'validation_loss_bbox': tensor(0.0062, device='cuda:0'), 'validation_loss_giou': tensor(0.0428, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')}
{'training_loss': tensor(0.2619, device='cuda:0'), 'train_loss_ce': tensor(0.1454, device='cuda:0'), 'train_loss_bbox': tensor(0.0062, device='cuda:0'), 'train_loss_giou': tensor(0.0428, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(0.2330, device='cuda:0'), 'validation_loss_ce': tensor(0.1426, device='cuda:0'), 'validation_loss_bbox': tensor(0.0057, device='cuda:0'), 'validation_loss_giou': tensor(0.0310, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')}

Examples

{'size': tensor([800, 800]), 'image_id': tensor([735]), 'class_labels': tensor([5]), 'boxes': tensor([[0.7651, 0.5307, 0.0952, 0.0942]]), 'area': tensor([5740.4888]), 'iscrowd': tensor([0]), 'orig_size': tensor([1024, 1024])}