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yolos_mini_overfitting
MedicalVision/yolos_mini_overfitting
yolos_mini_overfitting is a object detection model from MedicalVision. Use it when you need objects located in an image. It is set up for transformers.
- dataset: NIH - original model: hustvl/yolos-tiny - lr: 0.0001 - dropoutrate: 0.1 - weightdecay: 0.0001 - maxepochs: 300 - train samples: 2
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.safetensors25.9 MB · 97%
At a glance
- Task
- Object Detection
- Library
- transformers
- Model type
- yolos
- Access
- Public
- Created
- Aug 14, 2024
- Updated
- Aug 14, 2024
- SHA
- 8888bb91
Model card
From the Hugging Face model README
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 ] = -1.000
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 ] = -1.000
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 ] = 0.900
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 1.000
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 1.000
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 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.900
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.900
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.900
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.900
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 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.900
Config
- dataset: NIH
- original model: hustvl/yolos-tiny
- lr: 0.0001
- dropout_rate: 0.1
- weight_decay: 0.0001
- max_epochs: 300
- train samples: 2
Logging
Training process
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{'training_loss': tensor(0.5269, device='cuda:0'), 'train_loss_ce': tensor(0.0288, device='cuda:0'), 'train_loss_bbox': tensor(0.0170, device='cuda:0'), 'train_loss_giou': tensor(0.2064, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(0.4526, device='cuda:0'), 'validation_loss_ce': tensor(0.0288, device='cuda:0'), 'validation_loss_bbox': tensor(0.0132, device='cuda:0'), 'validation_loss_giou': tensor(0.1789, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')}
{'training_loss': tensor(0.4526, device='cuda:0'), 'train_loss_ce': tensor(0.0288, device='cuda:0'), 'train_loss_bbox': tensor(0.0132, device='cuda:0'), 'train_loss_giou': tensor(0.1789, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(0.2621, device='cuda:0'), 'validation_loss_ce': tensor(0.0292, device='cuda:0'), 'validation_loss_bbox': tensor(0.0080, device='cuda:0'), 'validation_loss_giou': tensor(0.0964, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')}
The model card is truncated. Read the rest on Hugging Face.
- Task
- Object Detection
- Library
- transformers
- Type
- yolos
- Created
- Aug 14, 2024
- Updated
- Aug 14, 2024