yolos_tiny_100ep / README.md
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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 ] = 0.002
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.009
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 ] = 0.003
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.004
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.033
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.054
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 ] = 0.056
```
## Config
- dataset: NIH
- original model: hustvl/yolos-tiny
- lr: 1e-06
- dropout_rate: 0.1
- weight_decay: 0.05
- max_epochs: 100
- train samples: 885
## Logging
### Training process
```
{'validation_loss': tensor(7.4421, device='cuda:0'), 'validation_loss_ce': tensor(2.5115, device='cuda:0'), 'validation_loss_bbox': tensor(0.5845, device='cuda:0'), 'validation_loss_giou': tensor(1.0041, device='cuda:0'), 'validation_cardinality_error': tensor(98.9688, device='cuda:0')}
{'training_loss': tensor(5.3640, device='cuda:0'), 'train_loss_ce': tensor(2.2904, device='cuda:0'), 'train_loss_bbox': tensor(0.3264, device='cuda:0'), 'train_loss_giou': tensor(0.7207, device='cuda:0'), 'train_cardinality_error': tensor(99., device='cuda:0'), 'validation_loss': tensor(6.1285, device='cuda:0'), 'validation_loss_ce': tensor(2.2476, device='cuda:0'), 'validation_loss_bbox': tensor(0.4395, device='cuda:0'), 'validation_loss_giou': tensor(0.8417, device='cuda:0'), 'validation_cardinality_error': tensor(98.7273, device='cuda:0')}
{'training_loss': tensor(4.6269, device='cuda:0'), 'train_loss_ce': tensor(2.0755, device='cuda:0'), 'train_loss_bbox': tensor(0.2549, device='cuda:0'), 'train_loss_giou': tensor(0.6384, device='cuda:0'), 'train_cardinality_error': tensor(97.6000, device='cuda:0'), 'validation_loss': tensor(5.2690, device='cuda:0'), 'validation_loss_ce': tensor(2.0870, device='cuda:0'), 'validation_loss_bbox': tensor(0.3288, device='cuda:0'), 'validation_loss_giou': tensor(0.7689, device='cuda:0'), 'validation_cardinality_error': tensor(96.6364, device='cuda:0')}
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```
## 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)