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detr_nih_300ep
MedicalVision/detr_nih_300ep
detr_nih_300ep 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: facebook/detr-resnet-50 - lr: 0.0001 - dropoutrate: 0.1 - weightdecay: 0.0001 - maxepochs: 300 - train samples: 885
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.safetensors167 MB · 99%
At a glance
- Task
- Object Detection
- Library
- transformers
- Model type
- detr
- Access
- Public
- Created
- Aug 9, 2024
- Updated
- Aug 12, 2024
- SHA
- 297d386a
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 ] = 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 ] = 0.002
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.008
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.012
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 ] = 0.012
After training result
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.026
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.055
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.019
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 ] = 0.050
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.026
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.063
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.147
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.169
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 ] = 0.050
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.170
Config
- dataset: NIH
- original model: facebook/detr-resnet-50
- lr: 0.0001
- dropout_rate: 0.1
- weight_decay: 0.0001
- max_epochs: 300
- train samples: 885
Logging
Training process
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{'training_loss': tensor(0.5640, device='cuda:0'), 'train_loss_ce': tensor(0.0942, device='cuda:0'), 'train_loss_bbox': tensor(0.0210, device='cuda:0'), 'train_loss_giou': tensor(0.1825, device='cuda:0'), 'train_cardinality_error': tensor(0.4000, device='cuda:0'), 'validation_loss': tensor(2.1276, device='cuda:0'), 'validation_loss_ce': tensor(0.5339, device='cuda:0'), 'validation_loss_bbox': tensor(0.1291, device='cuda:0'), 'validation_loss_giou': tensor(0.4742, device='cuda:0'), 'validation_cardinality_error': tensor(0.8586, device='cuda:0')}
{'training_loss': tensor(0.5279, device='cuda:0'), 'train_loss_ce': tensor(0.1142, device='cuda:0'), 'train_loss_bbox': tensor(0.0261, device='cuda:0'), 'train_loss_giou': tensor(0.1415, device='cuda:0'), 'train_cardinality_error': tensor(0.6000, device='cuda:0'), 'validation_loss': tensor(2.1176, device='cuda:0'), 'validation_loss_ce': tensor(0.5059, device='cuda:0'), 'validation_loss_bbox': tensor(0.1309, device='cuda:0'), 'validation_loss_giou': tensor(0.4786, device='cuda:0'), 'validation_cardinality_error': tensor(0.9697, device='cuda:0')}
The model card is truncated. Read the rest on Hugging Face.
- Task
- Object Detection
- Library
- transformers
- Type
- detr
- Created
- Aug 9, 2024
- Updated
- Aug 12, 2024