--- 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 ] = 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 ] = 0.001 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002 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 ] = 0.002 ``` ## After training result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.028 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.075 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.021 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 ] = 0.029 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.089 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.152 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.166 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 ] = 0.168 ``` ## Config - dataset: NIH - original model: hustvl/yolos-tiny - lr: 0.0001 - dropout_rate: 0.1 - weight_decay: 1.0 - max_epochs: 30 - train samples: 885 ## Logging ### Training process ``` {'validation_loss': tensor(7.2510, device='cuda:0'), 'validation_loss_ce': tensor(2.7062, device='cuda:0'), 'validation_loss_bbox': tensor(0.5285, device='cuda:0'), 'validation_loss_giou': tensor(0.9513, device='cuda:0'), 'validation_cardinality_error': tensor(99., device='cuda:0')} {'training_loss': tensor(2.0733, device='cuda:0'), 'train_loss_ce': tensor(0.4772, device='cuda:0'), 'train_loss_bbox': tensor(0.1542, device='cuda:0'), 'train_loss_giou': tensor(0.4126, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.3407, device='cuda:0'), 'validation_loss_ce': tensor(0.4447, device='cuda:0'), 'validation_loss_bbox': tensor(0.1600, device='cuda:0'), 'validation_loss_giou': tensor(0.5479, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.3671, device='cuda:0'), 'train_loss_ce': tensor(0.3991, device='cuda:0'), 'train_loss_bbox': tensor(0.1568, device='cuda:0'), 'train_loss_giou': tensor(0.5919, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.3726, device='cuda:0'), 'validation_loss_ce': tensor(0.4201, device='cuda:0'), 'validation_loss_bbox': tensor(0.1725, device='cuda:0'), 'validation_loss_giou': tensor(0.5451, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.8530, device='cuda:0'), 'train_loss_ce': tensor(0.4628, device='cuda:0'), 'train_loss_bbox': tensor(0.2022, device='cuda:0'), 'train_loss_giou': tensor(0.6896, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.1740, device='cuda:0'), 'validation_loss_ce': tensor(0.4275, device='cuda:0'), 'validation_loss_bbox': tensor(0.1443, device='cuda:0'), 'validation_loss_giou': tensor(0.5126, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.4427, device='cuda:0'), 'train_loss_ce': tensor(0.4072, device='cuda:0'), 'train_loss_bbox': tensor(0.1748, device='cuda:0'), 'train_loss_giou': tensor(0.5806, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.1416, device='cuda:0'), 'validation_loss_ce': tensor(0.4228, device='cuda:0'), 'validation_loss_bbox': tensor(0.1418, device='cuda:0'), 'validation_loss_giou': tensor(0.5049, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.2765, device='cuda:0'), 'train_loss_ce': tensor(0.4684, device='cuda:0'), 'train_loss_bbox': tensor(0.1437, device='cuda:0'), 'train_loss_giou': tensor(0.5449, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.1062, device='cuda:0'), 'validation_loss_ce': tensor(0.4127, device='cuda:0'), 'validation_loss_bbox': 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'train_loss_bbox': tensor(0.1601, device='cuda:0'), 'train_loss_giou': tensor(0.4692, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.0796, device='cuda:0'), 'validation_loss_ce': tensor(0.4127, device='cuda:0'), 'validation_loss_bbox': tensor(0.1413, device='cuda:0'), 'validation_loss_giou': tensor(0.4801, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(1.7375, device='cuda:0'), 'train_loss_ce': tensor(0.3531, device='cuda:0'), 'train_loss_bbox': tensor(0.1156, device='cuda:0'), 'train_loss_giou': tensor(0.4033, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(1.9825, device='cuda:0'), 'validation_loss_ce': tensor(0.3733, device='cuda:0'), 'validation_loss_bbox': tensor(0.1305, device='cuda:0'), 'validation_loss_giou': tensor(0.4784, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.6773, device='cuda:0'), 'train_loss_ce': tensor(0.3795, device='cuda:0'), 'train_loss_bbox': tensor(0.2213, device='cuda:0'), 'train_loss_giou': tensor(0.5956, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.0580, device='cuda:0'), 'validation_loss_ce': tensor(0.3698, device='cuda:0'), 'validation_loss_bbox': tensor(0.1406, device='cuda:0'), 'validation_loss_giou': tensor(0.4927, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.0451, device='cuda:0'), 'train_loss_ce': tensor(0.3619, device='cuda:0'), 'train_loss_bbox': tensor(0.1562, device='cuda:0'), 'train_loss_giou': tensor(0.4512, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.0350, device='cuda:0'), 'validation_loss_ce': tensor(0.3855, device='cuda:0'), 'validation_loss_bbox': tensor(0.1382, device='cuda:0'), 'validation_loss_giou': tensor(0.4791, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.1357, device='cuda:0'), 'train_loss_ce': tensor(0.3901, device='cuda:0'), 'train_loss_bbox': tensor(0.1723, device='cuda:0'), 'train_loss_giou': tensor(0.4422, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(1.9736, device='cuda:0'), 'validation_loss_ce': tensor(0.3603, device='cuda:0'), 'validation_loss_bbox': tensor(0.1357, device='cuda:0'), 'validation_loss_giou': tensor(0.4673, device='cuda:0'), 'validation_cardinality_error': tensor(0.8687, device='cuda:0')} {'training_loss': tensor(1.5814, device='cuda:0'), 'train_loss_ce': tensor(0.3938, device='cuda:0'), 'train_loss_bbox': tensor(0.0935, device='cuda:0'), 'train_loss_giou': tensor(0.3600, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.0607, device='cuda:0'), 'validation_loss_ce': tensor(0.3561, device='cuda:0'), 'validation_loss_bbox': tensor(0.1488, device='cuda:0'), 'validation_loss_giou': tensor(0.4804, device='cuda:0'), 'validation_cardinality_error': tensor(0.9394, device='cuda:0')} {'training_loss': tensor(1.7642, device='cuda:0'), 'train_loss_ce': tensor(0.3312, device='cuda:0'), 'train_loss_bbox': tensor(0.0892, device='cuda:0'), 'train_loss_giou': tensor(0.4934, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(1.9519, device='cuda:0'), 'validation_loss_ce': tensor(0.3478, device='cuda:0'), 'validation_loss_bbox': tensor(0.1330, device='cuda:0'), 'validation_loss_giou': tensor(0.4697, device='cuda:0'), 'validation_cardinality_error': tensor(0.9697, device='cuda:0')} {'training_loss': tensor(1.3615, device='cuda:0'), 'train_loss_ce': tensor(0.3474, device='cuda:0'), 'train_loss_bbox': tensor(0.0976, device='cuda:0'), 'train_loss_giou': tensor(0.2630, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(1.8283, device='cuda:0'), 'validation_loss_ce': tensor(0.3620, device='cuda:0'), 'validation_loss_bbox': tensor(0.1182, device='cuda:0'), 'validation_loss_giou': tensor(0.4376, device='cuda:0'), 'validation_cardinality_error': tensor(0.9596, device='cuda:0')} {'training_loss': tensor(1.4507, device='cuda:0'), 'train_loss_ce': tensor(0.2499, device='cuda:0'), 'train_loss_bbox': tensor(0.0839, device='cuda:0'), 'train_loss_giou': tensor(0.3905, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.0212, device='cuda:0'), 'validation_loss_ce': tensor(0.3600, device='cuda:0'), 'validation_loss_bbox': tensor(0.1371, device='cuda:0'), 'validation_loss_giou': tensor(0.4878, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} ``` ## 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](./example.png)