metadata
library_name: transformers
tags: []
Original result
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.014
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.023
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.016
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.190
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.037
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 ] = 0.006
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.084
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.466
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.393
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.552
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
After training result
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.569
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.707
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.680
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.549
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.775
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 ] = 0.202
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.640
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.664
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.548
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.800
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Config
- dataset: NIH
- original model: facebook/detr-resnet-50
- lr: 5e-06
- dropout_rate: 0.1
- weight_decay: 0.05
- max_epochs: 40
- train samples: 61
Logging
Training process
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{'training_loss': tensor(0.2031, device='cuda:0'), 'train_loss_ce': tensor(0.1681, device='cuda:0'), 'train_loss_bbox': tensor(0.0015, device='cuda:0'), 'train_loss_giou': tensor(0.0138, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.9900, device='cuda:0'), 'validation_loss_ce': tensor(0.3618, device='cuda:0'), 'validation_loss_bbox': tensor(0.0332, device='cuda:0'), 'validation_loss_giou': tensor(0.2310, device='cuda:0'), 'validation_cardinality_error': tensor(1.4615, device='cuda:0')}
{'training_loss': tensor(0.6779, device='cuda:0'), 'train_loss_ce': tensor(0.4923, device='cuda:0'), 'train_loss_bbox': tensor(0.0074, device='cuda:0'), 'train_loss_giou': tensor(0.0742, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(0.9209, device='cuda:0'), 'validation_loss_ce': tensor(0.3567, device='cuda:0'), 'validation_loss_bbox': tensor(0.0315, device='cuda:0'), 'validation_loss_giou': tensor(0.2033, device='cuda:0'), 'validation_cardinality_error': tensor(1.4615, device='cuda:0')}
{'training_loss': tensor(2.5319, device='cuda:0'), 'train_loss_ce': tensor(0.4393, device='cuda:0'), 'train_loss_bbox': tensor(0.1542, device='cuda:0'), 'train_loss_giou': tensor(0.6607, device='cuda:0'), 'train_cardinality_error': tensor(2., device='cuda:0'), 'validation_loss': tensor(1.0136, device='cuda:0'), 'validation_loss_ce': tensor(0.3559, device='cuda:0'), 'validation_loss_bbox': tensor(0.0335, device='cuda:0'), 'validation_loss_giou': tensor(0.2451, device='cuda:0'), 'validation_cardinality_error': tensor(1.4615, device='cuda:0')}
{'training_loss': tensor(0.2293, device='cuda:0'), 'train_loss_ce': tensor(0.1921, device='cuda:0'), 'train_loss_bbox': tensor(0.0033, device='cuda:0'), 'train_loss_giou': tensor(0.0104, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.8533, device='cuda:0'), 'validation_loss_ce': tensor(0.3347, device='cuda:0'), 'validation_loss_bbox': tensor(0.0281, device='cuda:0'), 'validation_loss_giou': tensor(0.1891, device='cuda:0'), 'validation_cardinality_error': tensor(1.3846, device='cuda:0')}
{'training_loss': tensor(0.1696, device='cuda:0'), 'train_loss_ce': tensor(0.1435, device='cuda:0'), 'train_loss_bbox': tensor(0.0009, device='cuda:0'), 'train_loss_giou': tensor(0.0108, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.9884, device='cuda:0'), 'validation_loss_ce': tensor(0.3382, device='cuda:0'), 'validation_loss_bbox': tensor(0.0360, device='cuda:0'), 'validation_loss_giou': tensor(0.2352, device='cuda:0'), 'validation_cardinality_error': tensor(1.3846, device='cuda:0')}
{'training_loss': tensor(0.5900, device='cuda:0'), 'train_loss_ce': tensor(0.3056, device='cuda:0'), 'train_loss_bbox': tensor(0.0299, device='cuda:0'), 'train_loss_giou': tensor(0.0675, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.8545, device='cuda:0'), 'validation_loss_ce': tensor(0.3202, device='cuda:0'), 'validation_loss_bbox': tensor(0.0306, device='cuda:0'), 'validation_loss_giou': tensor(0.1908, device='cuda:0'), 'validation_cardinality_error': tensor(1.3077, device='cuda:0')}
{'training_loss': tensor(0.1700, device='cuda:0'), 'train_loss_ce': tensor(0.1408, device='cuda:0'), 'train_loss_bbox': tensor(0.0015, device='cuda:0'), 'train_loss_giou': tensor(0.0109, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(0.8922, device='cuda:0'), 'validation_loss_ce': tensor(0.3169, device='cuda:0'), 'validation_loss_bbox': tensor(0.0319, device='cuda:0'), 'validation_loss_giou': tensor(0.2080, device='cuda:0'), 'validation_cardinality_error': tensor(1.3846, device='cuda:0')}
{'training_loss': tensor(0.4513, device='cuda:0'), 'train_loss_ce': tensor(0.3160, device='cuda:0'), 'train_loss_bbox': tensor(0.0127, device='cuda:0'), 'train_loss_giou': tensor(0.0360, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(0.8982, device='cuda:0'), 'validation_loss_ce': tensor(0.3177, device='cuda:0'), 'validation_loss_bbox': tensor(0.0301, device='cuda:0'), 'validation_loss_giou': tensor(0.2150, device='cuda:0'), 'validation_cardinality_error': tensor(1.3077, device='cuda:0')}
Examples
{'size': tensor([ 800, 1066]), 'image_id': tensor([0]), 'class_labels': tensor([0]), 'boxes': tensor([[0.5955, 0.5811, 0.2202, 0.3561]]), 'area': tensor([3681.5083]), 'iscrowd': tensor([0]), 'orig_size': tensor([1536, 2048])}
