MedicalVision
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yolos_tiny_30ep

Object Detection

Transformers

Safetensors

yolos

Inference Endpoints

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npvinHnivqn commited on Aug 13

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@@ -3,197 +3,86 @@ library_name: transformers
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-# Model Card for Model ID
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 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 ] = 0.011
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.011
+ 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 ] = 0.011
+```
+## After training result
+```
+IoU metric: bbox
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.008
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.026
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.001
+ 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 ] = 0.008
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.040
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.049
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.056
+ 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: 0.0001
+- dropout_rate: 0.1
+- weight_decay: 10.0
+- max_epochs: 30
+- train samples: 885
+## Logging
+### Training process
+```
+{'validation_loss': tensor(6.4845, device='cuda:0'), 'validation_loss_ce': tensor(2.0703, device='cuda:0'), 'validation_loss_bbox': tensor(0.5406, device='cuda:0'), 'validation_loss_giou': tensor(0.8556, device='cuda:0'), 'validation_cardinality_error': tensor(79.9062, device='cuda:0')}
+{'training_loss': tensor(2.7016, device='cuda:0'), 'train_loss_ce': tensor(0.4314, device='cuda:0'), 'train_loss_bbox': tensor(0.2228, device='cuda:0'), 'train_loss_giou': tensor(0.5781, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.3099, device='cuda:0'), 'validation_loss_ce': tensor(0.4543, device='cuda:0'), 'validation_loss_bbox': tensor(0.1610, device='cuda:0'), 'validation_loss_giou': tensor(0.5252, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')}
+{'training_loss': tensor(2.2841, device='cuda:0'), 'train_loss_ce': tensor(0.4562, device='cuda:0'), 'train_loss_bbox': tensor(0.1660, device='cuda:0'), 'train_loss_giou': tensor(0.4990, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.3262, device='cuda:0'), 'validation_loss_ce': tensor(0.4318, device='cuda:0'), 'validation_loss_bbox': tensor(0.1678, device='cuda:0'), 'validation_loss_giou': tensor(0.5276, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')}
+{'training_loss': tensor(2.0995, device='cuda:0'), 'train_loss_ce': tensor(0.4676, device='cuda:0'), 'train_loss_bbox': tensor(0.1685, device='cuda:0'), 'train_loss_giou': tensor(0.3948, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.2856, device='cuda:0'), 'validation_loss_ce': tensor(0.4396, device='cuda:0'), 'validation_loss_bbox': tensor(0.1619, device='cuda:0'), 'validation_loss_giou': tensor(0.5182, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')}
+{'training_loss': tensor(1.8772, device='cuda:0'), 'train_loss_ce': tensor(0.4828, device='cuda:0'), 'train_loss_bbox': tensor(0.1217, device='cuda:0'), 'train_loss_giou': tensor(0.3929, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.2444, device='cuda:0'), 'validation_loss_ce': tensor(0.4270, device='cuda:0'), 'validation_loss_bbox': tensor(0.1613, device='cuda:0'), 'validation_loss_giou': tensor(0.5056, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')}
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+{'training_loss': tensor(2.4045, device='cuda:0'), 'train_loss_ce': tensor(0.4238, device='cuda:0'), 'train_loss_bbox': tensor(0.1777, device='cuda:0'), 'train_loss_giou': tensor(0.5461, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.7113, device='cuda:0'), 'validation_loss_ce': tensor(0.4276, device='cuda:0'), 'validation_loss_bbox': tensor(0.2102, device='cuda:0'), 'validation_loss_giou': tensor(0.6164, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')}
+{'training_loss': tensor(3.2236, device='cuda:0'), 'train_loss_ce': tensor(0.3068, device='cuda:0'), 'train_loss_bbox': tensor(0.2612, device='cuda:0'), 'train_loss_giou': tensor(0.8055, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.7548, device='cuda:0'), 'validation_loss_ce': tensor(0.4379, device='cuda:0'), 'validation_loss_bbox': tensor(0.2177, device='cuda:0'), 'validation_loss_giou': tensor(0.6142, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')}
+{'training_loss': tensor(3.0097, device='cuda:0'), 'train_loss_ce': tensor(0.4255, device='cuda:0'), 'train_loss_bbox': tensor(0.2340, device='cuda:0'), 'train_loss_giou': tensor(0.7071, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.7268, device='cuda:0'), 'validation_loss_ce': tensor(0.4118, device='cuda:0'), 'validation_loss_bbox': tensor(0.2167, device='cuda:0'), 'validation_loss_giou': tensor(0.6157, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')}
+{'training_loss': tensor(2.2934, device='cuda:0'), 'train_loss_ce': tensor(0.4336, device='cuda:0'), 'train_loss_bbox': tensor(0.1805, device='cuda:0'), 'train_loss_giou': tensor(0.4787, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.7026, device='cuda:0'), 'validation_loss_ce': tensor(0.4199, device='cuda:0'), 'validation_loss_bbox': tensor(0.2155, device='cuda:0'), 'validation_loss_giou': tensor(0.6026, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')}
+{'training_loss': tensor(2.2257, device='cuda:0'), 'train_loss_ce': tensor(0.4414, device='cuda:0'), 'train_loss_bbox': tensor(0.1801, device='cuda:0'), 'train_loss_giou': tensor(0.4420, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.8028, device='cuda:0'), 'validation_loss_ce': tensor(0.4215, device='cuda:0'), 'validation_loss_bbox': tensor(0.2218, device='cuda:0'), 'validation_loss_giou': tensor(0.6361, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')}
+{'training_loss': tensor(3.2935, device='cuda:0'), 'train_loss_ce': tensor(0.4851, device='cuda:0'), 'train_loss_bbox': tensor(0.2494, device='cuda:0'), 'train_loss_giou': tensor(0.7807, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.7375, device='cuda:0'), 'validation_loss_ce': tensor(0.4192, device='cuda:0'), 'validation_loss_bbox': tensor(0.2163, device='cuda:0'), 'validation_loss_giou': tensor(0.6184, device='cuda:0'), 'validation_cardinality_error': tensor(1., 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)