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library_name: transformers
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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 ] = 0.000
 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 ] = 0.000
 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 ] = 0.007
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.019
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.003
 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 ] = 0.008
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.060
 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 ] = 0.160
 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 ] = 0.167

Config

  • dataset: NIH
  • original model: facebook/detr-resnet-50
  • lr: 0.0001
  • max_epochs: 100

Logging

Training process 

{'validation_loss': tensor(6.3390, device='cuda:0'), 'validation_loss_ce': tensor(1.9257, device='cuda:0'), 'validation_loss_bbox': tensor(0.5244, device='cuda:0'), 'validation_loss_giou': tensor(0.8958, device='cuda:0'), 'validation_cardinality_error': tensor(60.5938, device='cuda:0')}
{'training_loss': tensor(3.2533, device='cuda:0'), 'train_loss_ce': tensor(0.6592, device='cuda:0'), 'train_loss_bbox': tensor(0.2086, device='cuda:0'), 'train_loss_giou': tensor(0.7757, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(3.0453, device='cuda:0'), 'validation_loss_ce': tensor(0.5371, device='cuda:0'), 'validation_loss_bbox': tensor(0.2398, device='cuda:0'), 'validation_loss_giou': tensor(0.6546, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')}
{'training_loss': tensor(2.6515, device='cuda:0'), 'train_loss_ce': tensor(0.3552, device='cuda:0'), 'train_loss_bbox': tensor(0.1901, device='cuda:0'), 'train_loss_giou': tensor(0.6729, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.3760, device='cuda:0'), 'validation_loss_ce': tensor(0.4638, device='cuda:0'), 'validation_loss_bbox': tensor(0.1636, device='cuda:0'), 'validation_loss_giou': tensor(0.5470, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')}
{'training_loss': tensor(2.2251, device='cuda:0'), 'train_loss_ce': tensor(0.4881, device='cuda:0'), 'train_loss_bbox': tensor(0.1581, device='cuda:0'), 'train_loss_giou': tensor(0.4732, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.3159, device='cuda:0'), 'validation_loss_ce': tensor(0.4545, device='cuda:0'), 'validation_loss_bbox': tensor(0.1584, device='cuda:0'), 'validation_loss_giou': tensor(0.5348, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')}
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