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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 ] = 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 ] = 0.000
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 ] = 0.000
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 ] = 0.049
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.083
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.059
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 ] = 0.050
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.117
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.204
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.222
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 ] = 0.025
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.238
```
## Config
- dataset: NIH
- original model: hustvl/yolos-tiny
- lr: 0.0001
- dropout_rate: 0.15
- weight_decay: 0.0005
- max_epochs: 100
- train samples: 885
## Logging
### Training process
```
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{'training_loss': tensor(1.0267, device='cuda:0'), 'train_loss_ce': tensor(0.1566, device='cuda:0'), 'train_loss_bbox': tensor(0.0750, device='cuda:0'), 'train_loss_giou': tensor(0.2475, device='cuda:0'), 'train_cardinality_error': tensor(0.6000, device='cuda:0'), 'validation_loss': tensor(2.0730, device='cuda:0'), 'validation_loss_ce': tensor(0.4747, device='cuda:0'), 'validation_loss_bbox': tensor(0.1282, device='cuda:0'), 'validation_loss_giou': tensor(0.4787, device='cuda:0'), 'validation_cardinality_error': tensor(0.8182, device='cuda:0')}
{'training_loss': tensor(0.7079, device='cuda:0'), 'train_loss_ce': tensor(0.0955, device='cuda:0'), 'train_loss_bbox': tensor(0.0404, device='cuda:0'), 'train_loss_giou': tensor(0.2051, device='cuda:0'), 'train_cardinality_error': tensor(2.8000, device='cuda:0'), 'validation_loss': tensor(2.0235, device='cuda:0'), 'validation_loss_ce': tensor(0.4538, device='cuda:0'), 'validation_loss_bbox': tensor(0.1329, device='cuda:0'), 'validation_loss_giou': tensor(0.4525, device='cuda:0'), 'validation_cardinality_error': tensor(0.8182, device='cuda:0')}
{'training_loss': tensor(0.5221, device='cuda:0'), 'train_loss_ce': tensor(0.1109, device='cuda:0'), 'train_loss_bbox': tensor(0.0314, device='cuda:0'), 'train_loss_giou': tensor(0.1271, device='cuda:0'), 'train_cardinality_error': tensor(0.4000, device='cuda:0'), 'validation_loss': tensor(2.0294, device='cuda:0'), 'validation_loss_ce': tensor(0.4754, device='cuda:0'), 'validation_loss_bbox': tensor(0.1272, device='cuda:0'), 'validation_loss_giou': tensor(0.4590, device='cuda:0'), 'validation_cardinality_error': tensor(0.7980, device='cuda:0')}
{'training_loss': tensor(0.7147, device='cuda:0'), 'train_loss_ce': tensor(0.1476, device='cuda:0'), 'train_loss_bbox': tensor(0.0458, device='cuda:0'), 'train_loss_giou': tensor(0.1690, device='cuda:0'), 'train_cardinality_error': tensor(0.6000, device='cuda:0'), 'validation_loss': tensor(2.0873, device='cuda:0'), 'validation_loss_ce': tensor(0.4671, device='cuda:0'), 'validation_loss_bbox': tensor(0.1382, device='cuda:0'), 'validation_loss_giou': tensor(0.4647, device='cuda:0'), 'validation_cardinality_error': tensor(0.7980, 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])}
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