metadata
library_name: transformers
tags: []
Original result
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.005
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.005
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.203
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.068
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.029
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.029
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.029
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.200
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.067
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.029
After training result
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.009
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.020
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.008
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
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 ] = 0.009
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.043
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.076
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.087
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
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 ] = 0.089
Config
- dataset: VinXray
- original model: hustvl/yolos-tiny
- lr: 0.0001
- dropout_rate: 0.1
- weight_decay: 0.0001
- max_epochs: 1
- train samples: 67234
Logging
Training process
{'validation_loss': tensor(8.5927, device='cuda:0'), 'validation_loss_ce': tensor(3.4775, device='cuda:0'), 'validation_loss_bbox': tensor(0.5756, device='cuda:0'), 'validation_loss_giou': tensor(1.1184, device='cuda:0'), 'validation_cardinality_error': tensor(99.5938, device='cuda:0')}
{'training_loss': tensor(1.3630, device='cuda:0'), 'train_loss_ce': tensor(0.2593, device='cuda:0'), 'train_loss_bbox': tensor(0.0803, device='cuda:0'), 'train_loss_giou': tensor(0.3511, device='cuda:0'), 'train_cardinality_error': tensor(0.5294, device='cuda:0'), 'validation_loss': tensor(1.5262, device='cuda:0'), 'validation_loss_ce': tensor(0.2351, device='cuda:0'), 'validation_loss_bbox': tensor(0.0827, device='cuda:0'), 'validation_loss_giou': tensor(0.4389, device='cuda:0'), 'validation_cardinality_error': tensor(0.4794, device='cuda:0')}
Examples
{'size': tensor([560, 512]), 'image_id': tensor([1]), 'class_labels': tensor([], dtype=torch.int64), 'boxes': tensor([], size=(0, 4)), 'area': tensor([]), 'iscrowd': tensor([], dtype=torch.int64), 'orig_size': tensor([2580, 2332])}
