---
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 ] = 0.001
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 ] = 0.001
```

## After training result
```
IoU metric: bbox
Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.826
Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.867
Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.864
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 ] = 0.716
Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.844
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.841
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.844
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.844
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 ] = 0.750
Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.854
```

## Config
- dataset: NIH
- original model: hustvl/yolos-tiny
- lr: 1e-05
- dropout_rate: 0.1
- weight_decay: 0.001
- max_epochs: 600
- train samples: 354

## Logging
### Training process
```
{'validation_loss': tensor(7.1177, device='cuda:0'), 'validation_loss_ce': tensor(2.3438, device='cuda:0'), 'validation_loss_bbox': tensor(0.5517, device='cuda:0'), 'validation_loss_giou': tensor(1.0076, device='cuda:0'), 'validation_cardinality_error': tensor(98.9375, device='cuda:0')}
{'training_loss': tensor(4.8380, device='cuda:0'), 'train_loss_ce': tensor(1.9076, device='cuda:0'), 'train_loss_bbox': tensor(0.2720, device='cuda:0'), 'train_loss_giou': tensor(0.7852, device='cuda:0'), 'train_cardinality_error': tensor(40.5000, device='cuda:0'), 'validation_loss': tensor(4.6572, device='cuda:0'), 'validation_loss_ce': tensor(1.9323, device='cuda:0'), 'validation_loss_bbox': tensor(0.2644, device='cuda:0'), 'validation_loss_giou': tensor(0.7016, device='cuda:0'), 'validation_cardinality_error': tensor(64.3898, device='cuda:0')}
{'training_loss': tensor(4.6511, device='cuda:0'), 'train_loss_ce': tensor(1.7157, device='cuda:0'), 'train_loss_bbox': tensor(0.2469, device='cuda:0'), 'train_loss_giou': tensor(0.8504, device='cuda:0'), 'train_cardinality_error': tensor(18.5000, device='cuda:0'), 'validation_loss': tensor(3.6326, device='cuda:0'), 'validation_loss_ce': tensor(1.5263, device='cuda:0'), 'validation_loss_bbox': tensor(0.1872, device='cuda:0'), 'validation_loss_giou': tensor(0.5851, device='cuda:0'), 'validation_cardinality_error': tensor(5.3277, device='cuda:0')}
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{'training_loss': tensor(0.0699, device='cuda:0'), 'train_loss_ce': tensor(0.0079, device='cuda:0'), 'train_loss_bbox': tensor(0.0044, device='cuda:0'), 'train_loss_giou': tensor(0.0199, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.1210, device='cuda:0'), 'validation_loss_ce': tensor(0.0077, device='cuda:0'), 'validation_loss_bbox': tensor(0.0070, device='cuda:0'), 'validation_loss_giou': tensor(0.0393, device='cuda:0'), 'validation_cardinality_error': tensor(0.2203, device='cuda:0')}
{'training_loss': tensor(0.0682, device='cuda:0'), 'train_loss_ce': tensor(0.0051, device='cuda:0'), 'train_loss_bbox': tensor(0.0059, device='cuda:0'), 'train_loss_giou': tensor(0.0168, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.1295, device='cuda:0'), 'validation_loss_ce': tensor(0.0075, device='cuda:0'), 'validation_loss_bbox': tensor(0.0073, device='cuda:0'), 'validation_loss_giou': tensor(0.0427, device='cuda:0'), 'validation_cardinality_error': tensor(0.1695, device='cuda:0')}
{'training_loss': tensor(0.0636, device='cuda:0'), 'train_loss_ce': tensor(0.0103, device='cuda:0'), 'train_loss_bbox': tensor(0.0037, device='cuda:0'), 'train_loss_giou': tensor(0.0173, device='cuda:0'), 'train_cardinality_error': tensor(0.5000, device='cuda:0'), 'validation_loss': tensor(0.1114, device='cuda:0'), 'validation_loss_ce': tensor(0.0071, device='cuda:0'), 'validation_loss_bbox': tensor(0.0064, device='cuda:0'), 'validation_loss_giou': tensor(0.0360, device='cuda:0'), 'validation_cardinality_error': tensor(0.2006, 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)