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library_name: transformers
tags: []

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(0.1400, device='cuda:0'), 'train_loss_ce': tensor(0.0021, device='cuda:0'), 'train_loss_bbox': tensor(0.0048, device='cuda:0'), 'train_loss_giou': tensor(0.0569, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.1314, device='cuda:0'), 'validation_loss_ce': tensor(0.0020, device='cuda:0'), 'validation_loss_bbox': tensor(0.0045, device='cuda:0'), 'validation_loss_giou': tensor(0.0533, device='cuda:0'), 'validation_cardinality_error': tensor(0., device='cuda:0')}
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{'training_loss': tensor(0.0753, device='cuda:0'), 'train_loss_ce': tensor(0.0020, device='cuda:0'), 'train_loss_bbox': tensor(0.0030, device='cuda:0'), 'train_loss_giou': tensor(0.0290, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.0889, device='cuda:0'), 'validation_loss_ce': tensor(0.0021, device='cuda:0'), 'validation_loss_bbox': tensor(0.0032, device='cuda:0'), 'validation_loss_giou': tensor(0.0353, device='cuda:0'), 'validation_cardinality_error': tensor(0., device='cuda:0')}
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{'training_loss': tensor(0.1461, device='cuda:0'), 'train_loss_ce': tensor(0.0021, device='cuda:0'), 'train_loss_bbox': tensor(0.0058, device='cuda:0'), 'train_loss_giou': tensor(0.0576, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.1026, device='cuda:0'), 'validation_loss_ce': tensor(0.0020, device='cuda:0'), 'validation_loss_bbox': tensor(0.0048, device='cuda:0'), 'validation_loss_giou': tensor(0.0382, device='cuda:0'), 'validation_cardinality_error': tensor(0., device='cuda:0')}
{'training_loss': tensor(0.1026, device='cuda:0'), 'train_loss_ce': tensor(0.0020, device='cuda:0'), 'train_loss_bbox': tensor(0.0048, device='cuda:0'), 'train_loss_giou': tensor(0.0382, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.1504, device='cuda:0'), 'validation_loss_ce': tensor(0.0020, device='cuda:0'), 'validation_loss_bbox': tensor(0.0065, device='cuda:0'), 'validation_loss_giou': tensor(0.0581, device='cuda:0'), 'validation_cardinality_error': tensor(0., device='cuda:0')}
{'training_loss': tensor(0.1504, device='cuda:0'), 'train_loss_ce': tensor(0.0020, device='cuda:0'), 'train_loss_bbox': tensor(0.0065, device='cuda:0'), 'train_loss_giou': tensor(0.0581, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.1370, device='cuda:0'), 'validation_loss_ce': tensor(0.0020, device='cuda:0'), 'validation_loss_bbox': tensor(0.0042, device='cuda:0'), 'validation_loss_giou': tensor(0.0570, device='cuda:0'), 'validation_cardinality_error': tensor(0., device='cuda:0')}
{'training_loss': tensor(0.1370, device='cuda:0'), 'train_loss_ce': tensor(0.0020, device='cuda:0'), 'train_loss_bbox': tensor(0.0042, device='cuda:0'), 'train_loss_giou': tensor(0.0570, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.1498, device='cuda:0'), 'validation_loss_ce': tensor(0.0020, device='cuda:0'), 'validation_loss_bbox': tensor(0.0076, device='cuda:0'), 'validation_loss_giou': tensor(0.0550, device='cuda:0'), 'validation_cardinality_error': tensor(0., device='cuda:0')}
{'training_loss': tensor(0.1498, device='cuda:0'), 'train_loss_ce': tensor(0.0020, device='cuda:0'), 'train_loss_bbox': tensor(0.0076, device='cuda:0'), 'train_loss_giou': tensor(0.0550, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.1961, device='cuda:0'), 'validation_loss_ce': tensor(0.0020, device='cuda:0'), 'validation_loss_bbox': tensor(0.0111, device='cuda:0'), 'validation_loss_giou': tensor(0.0693, device='cuda:0'), 'validation_cardinality_error': tensor(0., device='cuda:0')}
{'training_loss': tensor(0.1961, device='cuda:0'), 'train_loss_ce': tensor(0.0020, device='cuda:0'), 'train_loss_bbox': tensor(0.0111, device='cuda:0'), 'train_loss_giou': tensor(0.0693, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.1626, device='cuda:0'), 'validation_loss_ce': tensor(0.0021, device='cuda:0'), 'validation_loss_bbox': tensor(0.0083, device='cuda:0'), 'validation_loss_giou': tensor(0.0594, device='cuda:0'), 'validation_cardinality_error': tensor(0., device='cuda:0')}
{'training_loss': tensor(0.1626, device='cuda:0'), 'train_loss_ce': tensor(0.0021, device='cuda:0'), 'train_loss_bbox': tensor(0.0083, device='cuda:0'), 'train_loss_giou': tensor(0.0594, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.1654, device='cuda:0'), 'validation_loss_ce': tensor(0.0021, device='cuda:0'), 'validation_loss_bbox': tensor(0.0094, device='cuda:0'), 'validation_loss_giou': tensor(0.0582, 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])}

Example