README.md

---
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 ] = 0.004
 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 ] = 0.004
```

## After training result
```
IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.016
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.028
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.020
 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 ] = 0.016
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.068
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.123
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.135
 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 ] = 0.136
```

## Config
- dataset: NIH
- original model: hustvl/yolos-tiny
- lr: 5e-06
- dropout_rate: 0.1
- weight_decay: 0.05
- max_epochs: 300
- train samples: 885

## Logging
### Training process
```
{'validation_loss': tensor(6.3551, device='cuda:0'), 'validation_loss_ce': tensor(2.0319, device='cuda:0'), 'validation_loss_bbox': tensor(0.4981, device='cuda:0'), 'validation_loss_giou': tensor(0.9164, device='cuda:0'), 'validation_cardinality_error': tensor(96., device='cuda:0')}
{'training_loss': tensor(3.8686, device='cuda:0'), 'train_loss_ce': tensor(1.4275, device='cuda:0'), 'train_loss_bbox': tensor(0.2086, device='cuda:0'), 'train_loss_giou': tensor(0.6991, device='cuda:0'), 'train_cardinality_error': tensor(8.2000, device='cuda:0'), 'validation_loss': tensor(3.8399, device='cuda:0'), 'validation_loss_ce': tensor(1.3733, device='cuda:0'), 'validation_loss_bbox': tensor(0.2313, device='cuda:0'), 'validation_loss_giou': tensor(0.6552, device='cuda:0'), 'validation_cardinality_error': tensor(4.0505, device='cuda:0')}
{'training_loss': tensor(3.0059, device='cuda:0'), 'train_loss_ce': tensor(0.8301, device='cuda:0'), 'train_loss_bbox': tensor(0.2366, device='cuda:0'), 'train_loss_giou': tensor(0.4965, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(3.0026, device='cuda:0'), 'validation_loss_ce': tensor(0.8374, device='cuda:0'), 'validation_loss_bbox': tensor(0.1967, device='cuda:0'), 'validation_loss_giou': tensor(0.5910, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')}
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{'training_loss': tensor(0.3886, device='cuda:0'), 'train_loss_ce': tensor(0.2701, device='cuda:0'), 'train_loss_bbox': tensor(0.0109, device='cuda:0'), 'train_loss_giou': tensor(0.0320, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.0300, device='cuda:0'), 'validation_loss_ce': tensor(0.3592, device='cuda:0'), 'validation_loss_bbox': tensor(0.1342, device='cuda:0'), 'validation_loss_giou': tensor(0.4998, device='cuda:0'), 'validation_cardinality_error': tensor(0.9293, device='cuda:0')}
{'training_loss': tensor(0.3016, device='cuda:0'), 'train_loss_ce': tensor(0.1853, device='cuda:0'), 'train_loss_bbox': tensor(0.0109, device='cuda:0'), 'train_loss_giou': tensor(0.0309, device='cuda:0'), 'train_cardinality_error': tensor(0.8000, device='cuda:0'), 'validation_loss': tensor(2.0257, device='cuda:0'), 'validation_loss_ce': tensor(0.3619, device='cuda:0'), 'validation_loss_bbox': tensor(0.1343, device='cuda:0'), 'validation_loss_giou': tensor(0.4961, device='cuda:0'), 'validation_cardinality_error': tensor(0.9596, device='cuda:0')}
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{'training_loss': tensor(0.3330, device='cuda:0'), 'train_loss_ce': tensor(0.2249, device='cuda:0'), 'train_loss_bbox': tensor(0.0090, device='cuda:0'), 'train_loss_giou': tensor(0.0317, device='cuda:0'), 'train_cardinality_error': tensor(0.8000, device='cuda:0'), 'validation_loss': tensor(2.0316, device='cuda:0'), 'validation_loss_ce': tensor(0.3598, device='cuda:0'), 'validation_loss_bbox': tensor(0.1350, device='cuda:0'), 'validation_loss_giou': tensor(0.4985, device='cuda:0'), 'validation_cardinality_error': tensor(0.9697, device='cuda:0')}
{'training_loss': tensor(0.4286, device='cuda:0'), 'train_loss_ce': tensor(0.2678, device='cuda:0'), 'train_loss_bbox': tensor(0.0076, device='cuda:0'), 'train_loss_giou': tensor(0.0615, device='cuda:0'), 'train_cardinality_error': tensor(0.8000, device='cuda:0'), 'validation_loss': tensor(2.0223, device='cuda:0'), 'validation_loss_ce': tensor(0.3601, device='cuda:0'), 'validation_loss_bbox': tensor(0.1343, device='cuda:0'), 'validation_loss_giou': tensor(0.4953, device='cuda:0'), 'validation_cardinality_error': tensor(0.9091, device='cuda:0')}
{'training_loss': tensor(0.4372, device='cuda:0'), 'train_loss_ce': tensor(0.2876, device='cuda:0'), 'train_loss_bbox': tensor(0.0120, device='cuda:0'), 'train_loss_giou': tensor(0.0449, device='cuda:0'), 'train_cardinality_error': tensor(1.2000, device='cuda:0'), 'validation_loss': tensor(2.0332, device='cuda:0'), 'validation_loss_ce': tensor(0.3591, device='cuda:0'), 'validation_loss_bbox': tensor(0.1348, device='cuda:0'), 'validation_loss_giou': tensor(0.4999, device='cuda:0'), 'validation_cardinality_error': tensor(0.9394, device='cuda:0')}
{'training_loss': tensor(0.4195, device='cuda:0'), 'train_loss_ce': tensor(0.2297, device='cuda:0'), 'train_loss_bbox': tensor(0.0168, device='cuda:0'), 'train_loss_giou': tensor(0.0530, device='cuda:0'), 'train_cardinality_error': tensor(0.6000, device='cuda:0'), 'validation_loss': tensor(2.0240, device='cuda:0'), 'validation_loss_ce': tensor(0.3607, device='cuda:0'), 'validation_loss_bbox': tensor(0.1351, device='cuda:0'), 'validation_loss_giou': tensor(0.4939, device='cuda:0'), 'validation_cardinality_error': tensor(0.9394, device='cuda:0')}
{'training_loss': tensor(0.4228, device='cuda:0'), 'train_loss_ce': tensor(0.2680, device='cuda:0'), 'train_loss_bbox': tensor(0.0133, device='cuda:0'), 'train_loss_giou': tensor(0.0442, device='cuda:0'), 'train_cardinality_error': tensor(0.8000, device='cuda:0'), 'validation_loss': tensor(2.0364, device='cuda:0'), 'validation_loss_ce': tensor(0.3581, device='cuda:0'), 'validation_loss_bbox': tensor(0.1353, device='cuda:0'), 'validation_loss_giou': tensor(0.5009, device='cuda:0'), 'validation_cardinality_error': tensor(0.9394, device='cuda:0')}
{'training_loss': tensor(0.5344, device='cuda:0'), 'train_loss_ce': tensor(0.3058, device='cuda:0'), 'train_loss_bbox': tensor(0.0090, device='cuda:0'), 'train_loss_giou': tensor(0.0917, device='cuda:0'), 'train_cardinality_error': tensor(0.8000, device='cuda:0'), 'validation_loss': tensor(2.0196, device='cuda:0'), 'validation_loss_ce': tensor(0.3588, device='cuda:0'), 'validation_loss_bbox': tensor(0.1336, device='cuda:0'), 'validation_loss_giou': tensor(0.4964, device='cuda:0'), 'validation_cardinality_error': tensor(0.9394, 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)