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yolos_tiny_30ep

Object Detection
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yolos
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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 ] = 0.003
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.008
 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 ] = 0.008

After training result 

IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.012
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.023
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.011
 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 ] = 0.012
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.056
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.118
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.145
 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 ] = 0.146

Config

  • dataset: NIH
  • original model: hustvl/yolos-tiny
  • lr: 1e-05
  • dropout_rate: 0.1
  • weight_decay: 0.05
  • max_epochs: 30
  • train samples: 885

Logging

Training process 

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{'training_loss': tensor(3.7287, device='cuda:0'), 'train_loss_ce': tensor(1.3098, device='cuda:0'), 'train_loss_bbox': tensor(0.2119, device='cuda:0'), 'train_loss_giou': tensor(0.6797, device='cuda:0'), 'train_cardinality_error': tensor(0.8000, device='cuda:0'), 'validation_loss': tensor(3.4332, device='cuda:0'), 'validation_loss_ce': tensor(1.2399, device='cuda:0'), 'validation_loss_bbox': tensor(0.2065, device='cuda:0'), 'validation_loss_giou': tensor(0.5804, device='cuda:0'), 'validation_cardinality_error': tensor(1.0909, device='cuda:0')}
{'training_loss': tensor(2.8569, device='cuda:0'), 'train_loss_ce': tensor(0.5845, device='cuda:0'), 'train_loss_bbox': tensor(0.2106, device='cuda:0'), 'train_loss_giou': tensor(0.6097, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.4866, device='cuda:0'), 'validation_loss_ce': tensor(0.5491, device='cuda:0'), 'validation_loss_bbox': tensor(0.1759, device='cuda:0'), 'validation_loss_giou': tensor(0.5290, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')}
{'training_loss': tensor(2.2491, device='cuda:0'), 'train_loss_ce': tensor(0.5182, device='cuda:0'), 'train_loss_bbox': tensor(0.1662, device='cuda:0'), 'train_loss_giou': tensor(0.4500, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.3079, device='cuda:0'), 'validation_loss_ce': tensor(0.4791, device='cuda:0'), 'validation_loss_bbox': tensor(0.1619, device='cuda:0'), 'validation_loss_giou': tensor(0.5096, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')}
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Examples

{'size': tensor([512, 512]), 'image_id': tensor([1]), 'class_labels': tensor([4]), 'boxes': tensor([[0.2622, 0.5729, 0.0847, 0.0773]]), 'area': tensor([1717.9431]), 'iscrowd': tensor([0]), 'orig_size': tensor([1024, 1024])}

Example

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