MedicalVision
/

detr_balloon_example

@@ -3,197 +3,76 @@ library_name: transformers
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 tags: []
 ---
+## Original result
+```
+IoU metric: bbox
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.042
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.058
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.041
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.128
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.093
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.062
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.250
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.470
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.393
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.561
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
+```
+## After training result
+```
+IoU metric: bbox
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.575
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.744
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.661
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.534
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.767
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.200
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.648
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.694
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.574
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.835
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
+```
+## Config
+- dataset: NIH
+- original model: facebook/detr-resnet-50
+- lr: 1e-05
+- dropout_rate: 0.1
+- weight_decay: 0.05
+- max_epochs: 20
+- train samples: 61
+## Logging
+### Training process
+```
+{'validation_loss': tensor(2.8403, device='cuda:0'), 'validation_loss_ce': tensor(0.7536, device='cuda:0'), 'validation_loss_bbox': tensor(0.1414, device='cuda:0'), 'validation_loss_giou': tensor(0.6899, device='cuda:0'), 'validation_cardinality_error': tensor(88.5000, device='cuda:0')}
+{'training_loss': tensor(0.9331, device='cuda:0'), 'train_loss_ce': tensor(0.7954, device='cuda:0'), 'train_loss_bbox': tensor(0.0169, device='cuda:0'), 'train_loss_giou': tensor(0.0266, device='cuda:0'), 'train_cardinality_error': tensor(73., device='cuda:0'), 'validation_loss': tensor(1.9357, device='cuda:0'), 'validation_loss_ce': tensor(0.7015, device='cuda:0'), 'validation_loss_bbox': tensor(0.0786, device='cuda:0'), 'validation_loss_giou': tensor(0.4205, device='cuda:0'), 'validation_cardinality_error': tensor(63., device='cuda:0')}
+{'training_loss': tensor(0.7740, device='cuda:0'), 'train_loss_ce': tensor(0.6548, device='cuda:0'), 'train_loss_bbox': tensor(0.0032, device='cuda:0'), 'train_loss_giou': tensor(0.0516, device='cuda:0'), 'train_cardinality_error': tensor(15., device='cuda:0'), 'validation_loss': tensor(1.6569, device='cuda:0'), 'validation_loss_ce': tensor(0.6407, device='cuda:0'), 'validation_loss_bbox': tensor(0.0773, device='cuda:0'), 'validation_loss_giou': tensor(0.3149, device='cuda:0'), 'validation_cardinality_error': tensor(38.3846, device='cuda:0')}
+{'training_loss': tensor(0.8202, device='cuda:0'), 'train_loss_ce': tensor(0.5803, device='cuda:0'), 'train_loss_bbox': tensor(0.0250, device='cuda:0'), 'train_loss_giou': tensor(0.0574, device='cuda:0'), 'train_cardinality_error': tensor(19., device='cuda:0'), 'validation_loss': tensor(1.5251, device='cuda:0'), 'validation_loss_ce': tensor(0.6084, device='cuda:0'), 'validation_loss_bbox': tensor(0.0518, device='cuda:0'), 'validation_loss_giou': tensor(0.3288, device='cuda:0'), 'validation_cardinality_error': tensor(23.6154, device='cuda:0')}
+{'training_loss': tensor(0.6044, device='cuda:0'), 'train_loss_ce': tensor(0.4874, device='cuda:0'), 'train_loss_bbox': tensor(0.0041, device='cuda:0'), 'train_loss_giou': tensor(0.0483, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(1.6047, device='cuda:0'), 'validation_loss_ce': tensor(0.5633, device='cuda:0'), 'validation_loss_bbox': tensor(0.0684, device='cuda:0'), 'validation_loss_giou': tensor(0.3497, device='cuda:0'), 'validation_cardinality_error': tensor(13.2308, device='cuda:0')}
+{'training_loss': tensor(0.6582, device='cuda:0'), 'train_loss_ce': tensor(0.5104, device='cuda:0'), 'train_loss_bbox': tensor(0.0069, device='cuda:0'), 'train_loss_giou': tensor(0.0567, device='cuda:0'), 'train_cardinality_error': tensor(2., device='cuda:0'), 'validation_loss': tensor(1.3342, device='cuda:0'), 'validation_loss_ce': tensor(0.5352, device='cuda:0'), 'validation_loss_bbox': tensor(0.0504, device='cuda:0'), 'validation_loss_giou': tensor(0.2735, device='cuda:0'), 'validation_cardinality_error': tensor(8.1538, device='cuda:0')}
+{'training_loss': tensor(1.0112, device='cuda:0'), 'train_loss_ce': tensor(0.5257, device='cuda:0'), 'train_loss_bbox': tensor(0.0471, device='cuda:0'), 'train_loss_giou': tensor(0.1252, device='cuda:0'), 'train_cardinality_error': tensor(3., device='cuda:0'), 'validation_loss': tensor(1.2920, device='cuda:0'), 'validation_loss_ce': tensor(0.5065, device='cuda:0'), 'validation_loss_bbox': tensor(0.0475, device='cuda:0'), 'validation_loss_giou': tensor(0.2741, device='cuda:0'), 'validation_cardinality_error': tensor(5.1538, device='cuda:0')}
+{'training_loss': tensor(0.4205, device='cuda:0'), 'train_loss_ce': tensor(0.3367, device='cuda:0'), 'train_loss_bbox': tensor(0.0080, device='cuda:0'), 'train_loss_giou': tensor(0.0220, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(1.6009, device='cuda:0'), 'validation_loss_ce': tensor(0.4899, device='cuda:0'), 'validation_loss_bbox': tensor(0.0742, device='cuda:0'), 'validation_loss_giou': tensor(0.3700, device='cuda:0'), 'validation_cardinality_error': tensor(3.4615, device='cuda:0')}
+{'training_loss': tensor(0.4747, device='cuda:0'), 'train_loss_ce': tensor(0.3562, device='cuda:0'), 'train_loss_bbox': tensor(0.0168, device='cuda:0'), 'train_loss_giou': tensor(0.0172, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(1.3055, device='cuda:0'), 'validation_loss_ce': tensor(0.4662, device='cuda:0'), 'validation_loss_bbox': tensor(0.0488, device='cuda:0'), 'validation_loss_giou': tensor(0.2977, device='cuda:0'), 'validation_cardinality_error': tensor(2.4615, device='cuda:0')}
+{'training_loss': tensor(0.6444, device='cuda:0'), 'train_loss_ce': tensor(0.4712, device='cuda:0'), 'train_loss_bbox': tensor(0.0081, device='cuda:0'), 'train_loss_giou': tensor(0.0665, device='cuda:0'), 'train_cardinality_error': tensor(2., device='cuda:0'), 'validation_loss': tensor(1.4009, device='cuda:0'), 'validation_loss_ce': tensor(0.4472, device='cuda:0'), 'validation_loss_bbox': tensor(0.0580, device='cuda:0'), 'validation_loss_giou': tensor(0.3319, device='cuda:0'), 'validation_cardinality_error': tensor(1.6923, device='cuda:0')}
+{'training_loss': tensor(0.3142, device='cuda:0'), 'train_loss_ce': tensor(0.2558, device='cuda:0'), 'train_loss_bbox': tensor(0.0038, device='cuda:0'), 'train_loss_giou': tensor(0.0198, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(1.2037, device='cuda:0'), 'validation_loss_ce': tensor(0.4325, device='cuda:0'), 'validation_loss_bbox': tensor(0.0478, device='cuda:0'), 'validation_loss_giou': tensor(0.2662, device='cuda:0'), 'validation_cardinality_error': tensor(1.7692, device='cuda:0')}
+{'training_loss': tensor(1.2118, device='cuda:0'), 'train_loss_ce': tensor(0.5910, device='cuda:0'), 'train_loss_bbox': tensor(0.0650, device='cuda:0'), 'train_loss_giou': tensor(0.1480, device='cuda:0'), 'train_cardinality_error': tensor(6., device='cuda:0'), 'validation_loss': tensor(1.3762, device='cuda:0'), 'validation_loss_ce': tensor(0.4274, device='cuda:0'), 'validation_loss_bbox': tensor(0.0517, device='cuda:0'), 'validation_loss_giou': tensor(0.3451, device='cuda:0'), 'validation_cardinality_error': tensor(1.5385, device='cuda:0')}
+{'training_loss': tensor(0.3037, device='cuda:0'), 'train_loss_ce': tensor(0.2012, device='cuda:0'), 'train_loss_bbox': tensor(0.0025, device='cuda:0'), 'train_loss_giou': tensor(0.0449, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(1.2914, device='cuda:0'), 'validation_loss_ce': tensor(0.4120, device='cuda:0'), 'validation_loss_bbox': tensor(0.0510, device='cuda:0'), 'validation_loss_giou': tensor(0.3121, device='cuda:0'), 'validation_cardinality_error': tensor(1.4615, device='cuda:0')}
+{'training_loss': tensor(0.3875, device='cuda:0'), 'train_loss_ce': tensor(0.2326, device='cuda:0'), 'train_loss_bbox': tensor(0.0093, device='cuda:0'), 'train_loss_giou': tensor(0.0543, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(1.5544, device='cuda:0'), 'validation_loss_ce': tensor(0.3982, device='cuda:0'), 'validation_loss_bbox': tensor(0.0771, device='cuda:0'), 'validation_loss_giou': tensor(0.3854, device='cuda:0'), 'validation_cardinality_error': tensor(1.3846, device='cuda:0')}
+{'training_loss': tensor(2.0364, device='cuda:0'), 'train_loss_ce': tensor(0.3892, device='cuda:0'), 'train_loss_bbox': tensor(0.2506, device='cuda:0'), 'train_loss_giou': tensor(0.1970, device='cuda:0'), 'train_cardinality_error': tensor(2., device='cuda:0'), 'validation_loss': tensor(1.4121, device='cuda:0'), 'validation_loss_ce': tensor(0.3892, device='cuda:0'), 'validation_loss_bbox': tensor(0.0629, device='cuda:0'), 'validation_loss_giou': tensor(0.3542, device='cuda:0'), 'validation_cardinality_error': tensor(1.2308, device='cuda:0')}
+{'training_loss': tensor(0.3154, device='cuda:0'), 'train_loss_ce': tensor(0.2601, device='cuda:0'), 'train_loss_bbox': tensor(0.0058, device='cuda:0'), 'train_loss_giou': tensor(0.0131, device='cuda:0'), 'train_cardinality_error': tensor(2., device='cuda:0'), 'validation_loss': tensor(1.1014, device='cuda:0'), 'validation_loss_ce': tensor(0.3505, device='cuda:0'), 'validation_loss_bbox': tensor(0.0466, device='cuda:0'), 'validation_loss_giou': tensor(0.2590, device='cuda:0'), 'validation_cardinality_error': tensor(1.0769, device='cuda:0')}
+{'training_loss': tensor(0.3392, device='cuda:0'), 'train_loss_ce': tensor(0.1534, device='cuda:0'), 'train_loss_bbox': tensor(0.0219, device='cuda:0'), 'train_loss_giou': tensor(0.0381, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(1.1544, device='cuda:0'), 'validation_loss_ce': tensor(0.3387, device='cuda:0'), 'validation_loss_bbox': tensor(0.0510, device='cuda:0'), 'validation_loss_giou': tensor(0.2803, device='cuda:0'), 'validation_cardinality_error': tensor(0.9231, device='cuda:0')}
+{'training_loss': tensor(0.3263, device='cuda:0'), 'train_loss_ce': tensor(0.2588, device='cuda:0'), 'train_loss_bbox': tensor(0.0077, device='cuda:0'), 'train_loss_giou': tensor(0.0145, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(1.1032, device='cuda:0'), 'validation_loss_ce': tensor(0.3281, device='cuda:0'), 'validation_loss_bbox': tensor(0.0441, device='cuda:0'), 'validation_loss_giou': tensor(0.2773, device='cuda:0'), 'validation_cardinality_error': tensor(0.7692, device='cuda:0')}
+{'training_loss': tensor(0.1587, device='cuda:0'), 'train_loss_ce': tensor(0.1014, device='cuda:0'), 'train_loss_bbox': tensor(0.0073, device='cuda:0'), 'train_loss_giou': tensor(0.0105, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(1.1960, device='cuda:0'), 'validation_loss_ce': tensor(0.3185, device='cuda:0'), 'validation_loss_bbox': tensor(0.0570, device='cuda:0'), 'validation_loss_giou': tensor(0.2962, device='cuda:0'), 'validation_cardinality_error': tensor(0.9231, device='cuda:0')}
+{'training_loss': tensor(0.2787, device='cuda:0'), 'train_loss_ce': tensor(0.1191, device='cuda:0'), 'train_loss_bbox': tensor(0.0105, device='cuda:0'), 'train_loss_giou': tensor(0.0536, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.9316, device='cuda:0'), 'validation_loss_ce': tensor(0.2925, device='cuda:0'), 'validation_loss_bbox': tensor(0.0291, device='cuda:0'), 'validation_loss_giou': tensor(0.2469, device='cuda:0'), 'validation_cardinality_error': tensor(1.0769, device='cuda:0')}
+{'training_loss': tensor(0.1896, device='cuda:0'), 'train_loss_ce': tensor(0.0810, device='cuda:0'), 'train_loss_bbox': tensor(0.0107, device='cuda:0'), 'train_loss_giou': tensor(0.0276, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.8570, device='cuda:0'), 'validation_loss_ce': tensor(0.2889, device='cuda:0'), 'validation_loss_bbox': tensor(0.0264, device='cuda:0'), 'validation_loss_giou': tensor(0.2180, device='cuda:0'), 'validation_cardinality_error': tensor(1.1538, device='cuda:0')}
+```
+## Examples
+{'size': tensor([ 800, 1066]), 'image_id': tensor([0]), 'class_labels': tensor([0]), 'boxes': tensor([[0.5955, 0.5811, 0.2202, 0.3561]]), 'area': tensor([3681.5083]), 'iscrowd': tensor([0]), 'orig_size': tensor([1536, 2048])}
+![Example](./example.png)