Update README file

README.md

@@ -6,41 +6,41 @@ 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=   all | maxDets=100 ] = 0.044
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.056
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.051
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.157
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.030
  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=   all | maxDets=  1 ] = 0.070
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.202
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.466
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.389
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.557
  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=   all | maxDets=100 ] = 0.001
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.010
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.031
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.009
  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=   all | maxDets=  1 ] = 0.000
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.018
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.048
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.030
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.070
  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
+- lr: 5e-05
 - dropout_rate: 0.1
 - weight_decay: 0.05
 - max_epochs: 20
@@ -49,27 +49,27 @@ IoU metric: bbox
 ## 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')}
+{'validation_loss': tensor(2.5684, device='cuda:0'), 'validation_loss_ce': tensor(0.6809, device='cuda:0'), 'validation_loss_bbox': tensor(0.1171, device='cuda:0'), 'validation_loss_giou': tensor(0.6509, device='cuda:0'), 'validation_cardinality_error': tensor(38.2500, device='cuda:0')}
+{'training_loss': tensor(4.4525, device='cuda:0'), 'train_loss_ce': tensor(0.8844, device='cuda:0'), 'train_loss_bbox': tensor(0.3868, device='cuda:0'), 'train_loss_giou': tensor(0.8171, device='cuda:0'), 'train_cardinality_error': tensor(19., device='cuda:0'), 'validation_loss': tensor(1.2852, device='cuda:0'), 'validation_loss_ce': tensor(0.5324, device='cuda:0'), 'validation_loss_bbox': tensor(0.0360, device='cuda:0'), 'validation_loss_giou': tensor(0.2863, device='cuda:0'), 'validation_cardinality_error': tensor(4., device='cuda:0')}
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+{'training_loss': tensor(3.0507, device='cuda:0'), 'train_loss_ce': tensor(0.7805, device='cuda:0'), 'train_loss_bbox': tensor(0.1745, device='cuda:0'), 'train_loss_giou': tensor(0.6989, device='cuda:0'), 'train_cardinality_error': tensor(16., device='cuda:0'), 'validation_loss': tensor(2.6096, device='cuda:0'), 'validation_loss_ce': tensor(0.4846, device='cuda:0'), 'validation_loss_bbox': tensor(0.1573, device='cuda:0'), 'validation_loss_giou': tensor(0.6693, device='cuda:0'), 'validation_cardinality_error': tensor(5.8462, device='cuda:0')}
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+{'training_loss': tensor(3.2579, device='cuda:0'), 'train_loss_ce': tensor(0.7360, device='cuda:0'), 'train_loss_bbox': tensor(0.2364, device='cuda:0'), 'train_loss_giou': tensor(0.6700, device='cuda:0'), 'train_cardinality_error': tensor(4., device='cuda:0'), 'validation_loss': tensor(2.0824, device='cuda:0'), 'validation_loss_ce': tensor(0.5116, device='cuda:0'), 'validation_loss_bbox': tensor(0.1048, device='cuda:0'), 'validation_loss_giou': tensor(0.5234, device='cuda:0'), 'validation_cardinality_error': tensor(6.9231, device='cuda:0')}
+{'training_loss': tensor(3.7396, device='cuda:0'), 'train_loss_ce': tensor(0.4191, device='cuda:0'), 'train_loss_bbox': tensor(0.3937, device='cuda:0'), 'train_loss_giou': tensor(0.6760, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(3.1265, device='cuda:0'), 'validation_loss_ce': tensor(0.5484, device='cuda:0'), 'validation_loss_bbox': tensor(0.2265, device='cuda:0'), 'validation_loss_giou': tensor(0.7228, device='cuda:0'), 'validation_cardinality_error': tensor(4.3077, device='cuda:0')}
+{'training_loss': tensor(2.6312, device='cuda:0'), 'train_loss_ce': tensor(0.3234, device='cuda:0'), 'train_loss_bbox': tensor(0.2922, device='cuda:0'), 'train_loss_giou': tensor(0.4235, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(3.3476, device='cuda:0'), 'validation_loss_ce': tensor(0.5086, device='cuda:0'), 'validation_loss_bbox': tensor(0.2609, device='cuda:0'), 'validation_loss_giou': tensor(0.7671, device='cuda:0'), 'validation_cardinality_error': tensor(3.8462, device='cuda:0')}
+{'training_loss': tensor(1.7406, device='cuda:0'), 'train_loss_ce': tensor(0.5410, device='cuda:0'), 'train_loss_bbox': tensor(0.0662, device='cuda:0'), 'train_loss_giou': tensor(0.4344, device='cuda:0'), 'train_cardinality_error': tensor(3., device='cuda:0'), 'validation_loss': tensor(3.2995, device='cuda:0'), 'validation_loss_ce': tensor(0.5209, device='cuda:0'), 'validation_loss_bbox': tensor(0.2275, device='cuda:0'), 'validation_loss_giou': tensor(0.8205, device='cuda:0'), 'validation_cardinality_error': tensor(6.3846, device='cuda:0')}
+{'training_loss': tensor(2.6756, device='cuda:0'), 'train_loss_ce': tensor(0.4669, device='cuda:0'), 'train_loss_bbox': tensor(0.2744, device='cuda:0'), 'train_loss_giou': tensor(0.4184, device='cuda:0'), 'train_cardinality_error': tensor(2., device='cuda:0'), 'validation_loss': tensor(3.9060, device='cuda:0'), 'validation_loss_ce': tensor(0.5255, device='cuda:0'), 'validation_loss_bbox': tensor(0.3109, device='cuda:0'), 'validation_loss_giou': tensor(0.9129, device='cuda:0'), 'validation_cardinality_error': tensor(7.3846, device='cuda:0')}
+{'training_loss': tensor(2.6693, device='cuda:0'), 'train_loss_ce': tensor(0.5077, device='cuda:0'), 'train_loss_bbox': tensor(0.1733, device='cuda:0'), 'train_loss_giou': tensor(0.6475, device='cuda:0'), 'train_cardinality_error': tensor(2., device='cuda:0'), 'validation_loss': tensor(3.3320, device='cuda:0'), 'validation_loss_ce': tensor(0.5506, device='cuda:0'), 'validation_loss_bbox': tensor(0.2695, device='cuda:0'), 'validation_loss_giou': tensor(0.7171, device='cuda:0'), 'validation_cardinality_error': tensor(6., device='cuda:0')}
+{'training_loss': tensor(3.8867, device='cuda:0'), 'train_loss_ce': tensor(0.6607, device='cuda:0'), 'train_loss_bbox': tensor(0.3213, device='cuda:0'), 'train_loss_giou': tensor(0.8097, device='cuda:0'), 'train_cardinality_error': tensor(4., device='cuda:0'), 'validation_loss': tensor(3.0807, device='cuda:0'), 'validation_loss_ce': tensor(0.5218, device='cuda:0'), 'validation_loss_bbox': tensor(0.2218, device='cuda:0'), 'validation_loss_giou': tensor(0.7249, device='cuda:0'), 'validation_cardinality_error': tensor(3.8462, device='cuda:0')}
+{'training_loss': tensor(2.8666, device='cuda:0'), 'train_loss_ce': tensor(0.4696, device='cuda:0'), 'train_loss_bbox': tensor(0.2954, device='cuda:0'), 'train_loss_giou': tensor(0.4601, device='cuda:0'), 'train_cardinality_error': tensor(2., device='cuda:0'), 'validation_loss': tensor(3.0620, device='cuda:0'), 'validation_loss_ce': tensor(0.5262, device='cuda:0'), 'validation_loss_bbox': tensor(0.2149, device='cuda:0'), 'validation_loss_giou': tensor(0.7306, device='cuda:0'), 'validation_cardinality_error': tensor(3.8462, device='cuda:0')}
+{'training_loss': tensor(2.1570, device='cuda:0'), 'train_loss_ce': tensor(0.6110, device='cuda:0'), 'train_loss_bbox': tensor(0.0905, device='cuda:0'), 'train_loss_giou': tensor(0.5468, device='cuda:0'), 'train_cardinality_error': tensor(7., device='cuda:0'), 'validation_loss': tensor(2.8941, device='cuda:0'), 'validation_loss_ce': tensor(0.5307, device='cuda:0'), 'validation_loss_bbox': tensor(0.2003, device='cuda:0'), 'validation_loss_giou': tensor(0.6809, device='cuda:0'), 'validation_cardinality_error': tensor(3.8462, device='cuda:0')}
+{'training_loss': tensor(4.1725, device='cuda:0'), 'train_loss_ce': tensor(0.9266, device='cuda:0'), 'train_loss_bbox': tensor(0.2818, device='cuda:0'), 'train_loss_giou': tensor(0.9185, device='cuda:0'), 'train_cardinality_error': tensor(11., device='cuda:0'), 'validation_loss': tensor(3.3124, device='cuda:0'), 'validation_loss_ce': tensor(0.5442, device='cuda:0'), 'validation_loss_bbox': tensor(0.2570, device='cuda:0'), 'validation_loss_giou': tensor(0.7416, device='cuda:0'), 'validation_cardinality_error': tensor(3.8462, device='cuda:0')}
+{'training_loss': tensor(1.4272, device='cuda:0'), 'train_loss_ce': tensor(0.3878, device='cuda:0'), 'train_loss_bbox': tensor(0.0514, device='cuda:0'), 'train_loss_giou': tensor(0.3911, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.9401, device='cuda:0'), 'validation_loss_ce': tensor(0.5416, device='cuda:0'), 'validation_loss_bbox': tensor(0.2045, device='cuda:0'), 'validation_loss_giou': tensor(0.6879, device='cuda:0'), 'validation_cardinality_error': tensor(3.8462, device='cuda:0')}
 ```
 
 ## Examples