MedicalVision/detr_nih_300ep

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 ] = 0.002
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.008
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.012
 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 ] = 0.012

After training result

IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.026
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.055
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.019
 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 ] = 0.050
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.026
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.063
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.147
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.169
 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 ] = 0.050
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.170

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: 885

Logging

Training process

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{'training_loss': tensor(0.3078, device='cuda:0'), 'train_loss_ce': tensor(0.0203, device='cuda:0'), 'train_loss_bbox': tensor(0.0168, device='cuda:0'), 'train_loss_giou': tensor(0.1017, device='cuda:0'), 'train_cardinality_error': tensor(0.6000, device='cuda:0'), 'validation_loss': tensor(2.6650, device='cuda:0'), 'validation_loss_ce': tensor(1.0812, device='cuda:0'), 'validation_loss_bbox': tensor(0.1282, device='cuda:0'), 'validation_loss_giou': tensor(0.4714, device='cuda:0'), 'validation_cardinality_error': tensor(0.6263, device='cuda:0')}
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{'training_loss': tensor(0.2247, device='cuda:0'), 'train_loss_ce': tensor(0.0170, device='cuda:0'), 'train_loss_bbox': tensor(0.0141, device='cuda:0'), 'train_loss_giou': tensor(0.0687, device='cuda:0'), 'train_cardinality_error': tensor(0.2000, device='cuda:0'), 'validation_loss': tensor(2.6213, device='cuda:0'), 'validation_loss_ce': tensor(0.9984, device='cuda:0'), 'validation_loss_bbox': tensor(0.1304, device='cuda:0'), 'validation_loss_giou': tensor(0.4855, device='cuda:0'), 'validation_cardinality_error': tensor(0.5556, device='cuda:0')}
{'training_loss': tensor(0.4333, device='cuda:0'), 'train_loss_ce': tensor(0.0187, device='cuda:0'), 'train_loss_bbox': tensor(0.0165, device='cuda:0'), 'train_loss_giou': tensor(0.1661, device='cuda:0'), 'train_cardinality_error': tensor(0.4000, device='cuda:0'), 'validation_loss': tensor(2.6650, device='cuda:0'), 'validation_loss_ce': tensor(1.0726, device='cuda:0'), 'validation_loss_bbox': tensor(0.1280, device='cuda:0'), 'validation_loss_giou': tensor(0.4762, device='cuda:0'), 'validation_cardinality_error': tensor(0.6061, device='cuda:0')}
{'training_loss': tensor(0.2787, device='cuda:0'), 'train_loss_ce': tensor(0.0088, device='cuda:0'), 'train_loss_bbox': tensor(0.0217, device='cuda:0'), 'train_loss_giou': tensor(0.0806, device='cuda:0'), 'train_cardinality_error': tensor(0.2000, device='cuda:0'), 'validation_loss': tensor(2.6841, device='cuda:0'), 'validation_loss_ce': tensor(1.0708, device='cuda:0'), 'validation_loss_bbox': tensor(0.1343, device='cuda:0'), 'validation_loss_giou': tensor(0.4709, device='cuda:0'), 'validation_cardinality_error': tensor(0.6162, device='cuda:0')}
{'training_loss': tensor(0.2062, device='cuda:0'), 'train_loss_ce': tensor(0.0132, device='cuda:0'), 'train_loss_bbox': tensor(0.0106, device='cuda:0'), 'train_loss_giou': tensor(0.0701, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(2.6858, device='cuda:0'), 'validation_loss_ce': tensor(1.1130, device='cuda:0'), 'validation_loss_bbox': tensor(0.1273, device='cuda:0'), 'validation_loss_giou': tensor(0.4681, device='cuda:0'), 'validation_cardinality_error': tensor(0.5556, device='cuda:0')}
{'training_loss': tensor(0.2068, device='cuda:0'), 'train_loss_ce': tensor(0.0406, device='cuda:0'), 'train_loss_bbox': tensor(0.0130, device='cuda:0'), 'train_loss_giou': tensor(0.0505, device='cuda:0'), 'train_cardinality_error': tensor(0.8000, device='cuda:0'), 'validation_loss': tensor(2.7074, device='cuda:0'), 'validation_loss_ce': tensor(1.1161, device='cuda:0'), 'validation_loss_bbox': tensor(0.1279, device='cuda:0'), 'validation_loss_giou': tensor(0.4760, device='cuda:0'), 'validation_cardinality_error': tensor(0.5354, device='cuda:0')}
{'training_loss': tensor(0.4069, device='cuda:0'), 'train_loss_ce': tensor(0.0209, device='cuda:0'), 'train_loss_bbox': tensor(0.0218, device='cuda:0'), 'train_loss_giou': tensor(0.1385, device='cuda:0'), 'train_cardinality_error': tensor(0.6000, device='cuda:0'), 'validation_loss': tensor(2.6848, device='cuda:0'), 'validation_loss_ce': tensor(1.0661, device='cuda:0'), 'validation_loss_bbox': tensor(0.1305, device='cuda:0'), 'validation_loss_giou': tensor(0.4831, device='cuda:0'), 'validation_cardinality_error': tensor(0.6869, device='cuda:0')}
{'training_loss': tensor(0.3041, device='cuda:0'), 'train_loss_ce': tensor(0.0029, device='cuda:0'), 'train_loss_bbox': tensor(0.0116, device='cuda:0'), 'train_loss_giou': tensor(0.1215, device='cuda:0'), 'train_cardinality_error': tensor(0.6000, device='cuda:0'), 'validation_loss': tensor(2.7376, device='cuda:0'), 'validation_loss_ce': tensor(1.1461, device='cuda:0'), 'validation_loss_bbox': tensor(0.1302, device='cuda:0'), 'validation_loss_giou': tensor(0.4703, device='cuda:0'), 'validation_cardinality_error': tensor(0.5960, device='cuda:0')}
{'training_loss': tensor(0.3142, device='cuda:0'), 'train_loss_ce': tensor(0.0070, device='cuda:0'), 'train_loss_bbox': tensor(0.0146, device='cuda:0'), 'train_loss_giou': tensor(0.1172, device='cuda:0'), 'train_cardinality_error': tensor(0.2000, device='cuda:0'), 'validation_loss': tensor(2.6885, device='cuda:0'), 'validation_loss_ce': tensor(1.1088, device='cuda:0'), 'validation_loss_bbox': tensor(0.1281, device='cuda:0'), 'validation_loss_giou': tensor(0.4695, device='cuda:0'), 'validation_cardinality_error': tensor(0.5758, device='cuda:0')}
{'training_loss': tensor(0.2394, device='cuda:0'), 'train_loss_ce': tensor(0.0141, device='cuda:0'), 'train_loss_bbox': tensor(0.0133, device='cuda:0'), 'train_loss_giou': tensor(0.0793, device='cuda:0'), 'train_cardinality_error': tensor(0.4000, device='cuda:0'), 'validation_loss': tensor(2.6745, device='cuda:0'), 'validation_loss_ce': tensor(1.0608, device='cuda:0'), 'validation_loss_bbox': tensor(0.1291, device='cuda:0'), 'validation_loss_giou': tensor(0.4840, device='cuda:0'), 'validation_cardinality_error': tensor(0.6263, device='cuda:0')}

Examples

{'size': tensor([800, 800]), 'image_id': tensor([1]), 'class_labels': tensor([4]), 'boxes': tensor([[0.2622, 0.5729, 0.0847, 0.0773]]), 'area': tensor([4194.1973]), 'iscrowd': tensor([0]), 'orig_size': tensor([1024, 1024])}