Update README file

README.md

@@ -6,34 +6,34 @@ tags: []
 ## Original result
 ```
 IoU metric: bbox
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.014
- Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.023
- Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.016
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.190
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.037
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.018
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.028
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.018
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.061
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.031
  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 ] = 0.006
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.084
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.466
+ 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 ] = 0.136
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.468
  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 ] = 0.552
+ 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 ] = 0.569
- Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.707
- Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.680
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.549
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.775
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.581
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.740
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.661
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.580
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.722
  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 ] = 0.202
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.640
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.664
- Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.548
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.800
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.216
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.686
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.704
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.615
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.809
  Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
 ```
 
@@ -43,53 +43,43 @@ IoU metric: bbox
 - lr: 5e-06
 - dropout_rate: 0.1
 - weight_decay: 0.05
-- max_epochs: 40
+- max_epochs: 30
 - train samples: 61
 
 ## Logging
 ### Training process
 ```
-{'validation_loss': tensor(2.6890, device='cuda:0'), 'validation_loss_ce': tensor(0.7041, device='cuda:0'), 'validation_loss_bbox': tensor(0.1392, device='cuda:0'), 'validation_loss_giou': tensor(0.6446, device='cuda:0'), 'validation_cardinality_error': tensor(68.2500, device='cuda:0')}
-{'training_loss': tensor(0.8197, device='cuda:0'), 'train_loss_ce': tensor(0.6827, device='cuda:0'), 'train_loss_bbox': tensor(0.0069, device='cuda:0'), 'train_loss_giou': tensor(0.0512, device='cuda:0'), 'train_cardinality_error': tensor(28., device='cuda:0'), 'validation_loss': tensor(1.9387, device='cuda:0'), 'validation_loss_ce': tensor(0.6735, device='cuda:0'), 'validation_loss_bbox': tensor(0.0883, device='cuda:0'), 'validation_loss_giou': tensor(0.4118, device='cuda:0'), 'validation_cardinality_error': tensor(40.6154, device='cuda:0')}
-{'training_loss': tensor(1.0182, device='cuda:0'), 'train_loss_ce': tensor(0.6821, device='cuda:0'), 'train_loss_bbox': tensor(0.0331, device='cuda:0'), 'train_loss_giou': tensor(0.0853, device='cuda:0'), 'train_cardinality_error': tensor(19., device='cuda:0'), 'validation_loss': tensor(1.7603, device='cuda:0'), 'validation_loss_ce': tensor(0.6590, device='cuda:0'), 'validation_loss_bbox': tensor(0.0742, device='cuda:0'), 'validation_loss_giou': tensor(0.3652, device='cuda:0'), 'validation_cardinality_error': tensor(37.0769, device='cuda:0')}
-{'training_loss': tensor(2.5174, device='cuda:0'), 'train_loss_ce': tensor(0.6131, device='cuda:0'), 'train_loss_bbox': tensor(0.1633, device='cuda:0'), 'train_loss_giou': tensor(0.5439, device='cuda:0'), 'train_cardinality_error': tensor(7., device='cuda:0'), 'validation_loss': tensor(1.6039, device='cuda:0'), 'validation_loss_ce': tensor(0.6434, device='cuda:0'), 'validation_loss_bbox': tensor(0.0577, device='cuda:0'), 'validation_loss_giou': tensor(0.3360, device='cuda:0'), 'validation_cardinality_error': tensor(25.2308, device='cuda:0')}
-{'training_loss': tensor(0.6921, device='cuda:0'), 'train_loss_ce': tensor(0.5446, device='cuda:0'), 'train_loss_bbox': tensor(0.0102, device='cuda:0'), 'train_loss_giou': tensor(0.0483, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(1.3713, device='cuda:0'), 'validation_loss_ce': tensor(0.6267, device='cuda:0'), 'validation_loss_bbox': tensor(0.0456, device='cuda:0'), 'validation_loss_giou': tensor(0.2582, device='cuda:0'), 'validation_cardinality_error': tensor(18.0769, device='cuda:0')}
-{'training_loss': tensor(4.2869, device='cuda:0'), 'train_loss_ce': tensor(0.6386, device='cuda:0'), 'train_loss_bbox': tensor(0.3740, device='cuda:0'), 'train_loss_giou': tensor(0.8893, device='cuda:0'), 'train_cardinality_error': tensor(2., device='cuda:0'), 'validation_loss': tensor(1.1874, device='cuda:0'), 'validation_loss_ce': tensor(0.6103, device='cuda:0'), 'validation_loss_bbox': tensor(0.0346, device='cuda:0'), 'validation_loss_giou': tensor(0.2020, device='cuda:0'), 'validation_cardinality_error': tensor(13.7692, device='cuda:0')}
-{'training_loss': tensor(0.7044, device='cuda:0'), 'train_loss_ce': tensor(0.4590, device='cuda:0'), 'train_loss_bbox': tensor(0.0204, device='cuda:0'), 'train_loss_giou': tensor(0.0716, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(1.5443, device='cuda:0'), 'validation_loss_ce': tensor(0.5929, device='cuda:0'), 'validation_loss_bbox': tensor(0.0645, device='cuda:0'), 'validation_loss_giou': tensor(0.3144, device='cuda:0'), 'validation_cardinality_error': tensor(10.4615, device='cuda:0')}
-{'training_loss': tensor(0.9410, device='cuda:0'), 'train_loss_ce': tensor(0.6810, device='cuda:0'), 'train_loss_bbox': tensor(0.0120, device='cuda:0'), 'train_loss_giou': tensor(0.0999, device='cuda:0'), 'train_cardinality_error': tensor(11., device='cuda:0'), 'validation_loss': tensor(1.3133, device='cuda:0'), 'validation_loss_ce': tensor(0.5824, device='cuda:0'), 'validation_loss_bbox': tensor(0.0444, device='cuda:0'), 'validation_loss_giou': tensor(0.2544, device='cuda:0'), 'validation_cardinality_error': tensor(8.6923, device='cuda:0')}
-{'training_loss': tensor(0.5475, device='cuda:0'), 'train_loss_ce': tensor(0.4755, device='cuda:0'), 'train_loss_bbox': tensor(0.0040, device='cuda:0'), 'train_loss_giou': tensor(0.0261, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(1.2459, device='cuda:0'), 'validation_loss_ce': tensor(0.5700, device='cuda:0'), 'validation_loss_bbox': tensor(0.0413, device='cuda:0'), 'validation_loss_giou': tensor(0.2347, device='cuda:0'), 'validation_cardinality_error': tensor(8., device='cuda:0')}
-{'training_loss': tensor(1.1077, device='cuda:0'), 'train_loss_ce': tensor(0.6080, device='cuda:0'), 'train_loss_bbox': tensor(0.0200, device='cuda:0'), 'train_loss_giou': tensor(0.1999, device='cuda:0'), 'train_cardinality_error': tensor(3., device='cuda:0'), 'validation_loss': tensor(1.1507, device='cuda:0'), 'validation_loss_ce': tensor(0.5592, device='cuda:0'), 'validation_loss_bbox': tensor(0.0325, device='cuda:0'), 'validation_loss_giou': tensor(0.2146, device='cuda:0'), 'validation_cardinality_error': tensor(6.4615, device='cuda:0')}
-{'training_loss': tensor(0.4938, device='cuda:0'), 'train_loss_ce': tensor(0.4045, device='cuda:0'), 'train_loss_bbox': tensor(0.0035, device='cuda:0'), 'train_loss_giou': tensor(0.0358, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(1.1810, device='cuda:0'), 'validation_loss_ce': tensor(0.5480, device='cuda:0'), 'validation_loss_bbox': tensor(0.0335, device='cuda:0'), 'validation_loss_giou': tensor(0.2327, device='cuda:0'), 'validation_cardinality_error': tensor(5.0769, device='cuda:0')}
-{'training_loss': tensor(0.5889, device='cuda:0'), 'train_loss_ce': tensor(0.4328, device='cuda:0'), 'train_loss_bbox': tensor(0.0169, device='cuda:0'), 'train_loss_giou': tensor(0.0357, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(1.0876, device='cuda:0'), 'validation_loss_ce': tensor(0.5336, device='cuda:0'), 'validation_loss_bbox': tensor(0.0303, device='cuda:0'), 'validation_loss_giou': tensor(0.2014, device='cuda:0'), 'validation_cardinality_error': tensor(4.1538, device='cuda:0')}
-{'training_loss': tensor(0.6849, device='cuda:0'), 'train_loss_ce': tensor(0.5288, device='cuda:0'), 'train_loss_bbox': tensor(0.0162, device='cuda:0'), 'train_loss_giou': tensor(0.0375, device='cuda:0'), 'train_cardinality_error': tensor(2., device='cuda:0'), 'validation_loss': tensor(1.1762, device='cuda:0'), 'validation_loss_ce': tensor(0.5274, device='cuda:0'), 'validation_loss_bbox': tensor(0.0358, device='cuda:0'), 'validation_loss_giou': tensor(0.2349, device='cuda:0'), 'validation_cardinality_error': tensor(3.9231, device='cuda:0')}
-{'training_loss': tensor(0.5448, device='cuda:0'), 'train_loss_ce': tensor(0.4532, device='cuda:0'), 'train_loss_bbox': tensor(0.0034, device='cuda:0'), 'train_loss_giou': tensor(0.0373, device='cuda:0'), 'train_cardinality_error': tensor(2., device='cuda:0'), 'validation_loss': tensor(1.0657, device='cuda:0'), 'validation_loss_ce': tensor(0.5102, device='cuda:0'), 'validation_loss_bbox': tensor(0.0320, device='cuda:0'), 'validation_loss_giou': tensor(0.1978, device='cuda:0'), 'validation_cardinality_error': tensor(3.3846, device='cuda:0')}
-{'training_loss': tensor(0.4614, device='cuda:0'), 'train_loss_ce': tensor(0.3967, device='cuda:0'), 'train_loss_bbox': tensor(0.0038, device='cuda:0'), 'train_loss_giou': tensor(0.0228, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(1.0666, device='cuda:0'), 'validation_loss_ce': tensor(0.5024, device='cuda:0'), 'validation_loss_bbox': tensor(0.0282, device='cuda:0'), 'validation_loss_giou': tensor(0.2115, device='cuda:0'), 'validation_cardinality_error': tensor(3.0769, device='cuda:0')}
-{'training_loss': tensor(0.8316, device='cuda:0'), 'train_loss_ce': tensor(0.6080, device='cuda:0'), 'train_loss_bbox': tensor(0.0217, device='cuda:0'), 'train_loss_giou': tensor(0.0576, device='cuda:0'), 'train_cardinality_error': tensor(6., device='cuda:0'), 'validation_loss': tensor(1.1255, device='cuda:0'), 'validation_loss_ce': tensor(0.4948, device='cuda:0'), 'validation_loss_bbox': tensor(0.0333, device='cuda:0'), 'validation_loss_giou': tensor(0.2321, device='cuda:0'), 'validation_cardinality_error': tensor(3., device='cuda:0')}
-{'training_loss': tensor(0.3968, device='cuda:0'), 'train_loss_ce': tensor(0.3305, device='cuda:0'), 'train_loss_bbox': tensor(0.0058, device='cuda:0'), 'train_loss_giou': tensor(0.0186, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(1.0655, device='cuda:0'), 'validation_loss_ce': tensor(0.4890, device='cuda:0'), 'validation_loss_bbox': tensor(0.0280, device='cuda:0'), 'validation_loss_giou': tensor(0.2183, device='cuda:0'), 'validation_cardinality_error': tensor(2.6923, device='cuda:0')}
-{'training_loss': tensor(0.4433, device='cuda:0'), 'train_loss_ce': tensor(0.3818, device='cuda:0'), 'train_loss_bbox': tensor(0.0059, device='cuda:0'), 'train_loss_giou': tensor(0.0159, device='cuda:0'), 'train_cardinality_error': tensor(2., device='cuda:0'), 'validation_loss': tensor(1.1138, device='cuda:0'), 'validation_loss_ce': tensor(0.4814, device='cuda:0'), 'validation_loss_bbox': tensor(0.0361, device='cuda:0'), 'validation_loss_giou': tensor(0.2258, device='cuda:0'), 'validation_cardinality_error': tensor(2.3846, device='cuda:0')}
-{'training_loss': tensor(0.7553, device='cuda:0'), 'train_loss_ce': tensor(0.5547, device='cuda:0'), 'train_loss_bbox': tensor(0.0123, device='cuda:0'), 'train_loss_giou': tensor(0.0696, device='cuda:0'), 'train_cardinality_error': tensor(5., device='cuda:0'), 'validation_loss': tensor(1.1168, device='cuda:0'), 'validation_loss_ce': tensor(0.4722, device='cuda:0'), 'validation_loss_bbox': tensor(0.0374, device='cuda:0'), 'validation_loss_giou': tensor(0.2289, device='cuda:0'), 'validation_cardinality_error': tensor(2.3846, device='cuda:0')}
-{'training_loss': tensor(0.3290, device='cuda:0'), 'train_loss_ce': tensor(0.3057, device='cuda:0'), 'train_loss_bbox': tensor(0.0025, device='cuda:0'), 'train_loss_giou': tensor(0.0053, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(1.1099, device='cuda:0'), 'validation_loss_ce': tensor(0.4635, device='cuda:0'), 'validation_loss_bbox': tensor(0.0369, device='cuda:0'), 'validation_loss_giou': tensor(0.2309, device='cuda:0'), 'validation_cardinality_error': tensor(2.2308, device='cuda:0')}
-{'training_loss': tensor(0.5072, device='cuda:0'), 'train_loss_ce': tensor(0.4575, device='cuda:0'), 'train_loss_bbox': tensor(0.0043, device='cuda:0'), 'train_loss_giou': tensor(0.0142, device='cuda:0'), 'train_cardinality_error': tensor(2., device='cuda:0'), 'validation_loss': tensor(1.0318, device='cuda:0'), 'validation_loss_ce': tensor(0.4618, device='cuda:0'), 'validation_loss_bbox': tensor(0.0296, device='cuda:0'), 'validation_loss_giou': tensor(0.2111, device='cuda:0'), 'validation_cardinality_error': tensor(2.2308, device='cuda:0')}
-{'training_loss': tensor(0.4039, device='cuda:0'), 'train_loss_ce': tensor(0.2866, device='cuda:0'), 'train_loss_bbox': tensor(0.0154, device='cuda:0'), 'train_loss_giou': tensor(0.0202, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(1.0966, device='cuda:0'), 'validation_loss_ce': tensor(0.4515, device='cuda:0'), 'validation_loss_bbox': tensor(0.0376, device='cuda:0'), 'validation_loss_giou': tensor(0.2285, device='cuda:0'), 'validation_cardinality_error': tensor(2.2308, device='cuda:0')}
-{'training_loss': tensor(0.3389, device='cuda:0'), 'train_loss_ce': tensor(0.2745, device='cuda:0'), 'train_loss_bbox': tensor(0.0094, device='cuda:0'), 'train_loss_giou': tensor(0.0088, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(0.9492, device='cuda:0'), 'validation_loss_ce': tensor(0.4383, device='cuda:0'), 'validation_loss_bbox': tensor(0.0267, device='cuda:0'), 'validation_loss_giou': tensor(0.1888, device='cuda:0'), 'validation_cardinality_error': tensor(2.3077, device='cuda:0')}
-{'training_loss': tensor(0.4502, device='cuda:0'), 'train_loss_ce': tensor(0.4224, device='cuda:0'), 'train_loss_bbox': tensor(0.0011, device='cuda:0'), 'train_loss_giou': tensor(0.0112, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(1.0500, device='cuda:0'), 'validation_loss_ce': tensor(0.4322, device='cuda:0'), 'validation_loss_bbox': tensor(0.0350, device='cuda:0'), 'validation_loss_giou': tensor(0.2213, device='cuda:0'), 'validation_cardinality_error': tensor(2.1538, device='cuda:0')}
-{'training_loss': tensor(0.3770, device='cuda:0'), 'train_loss_ce': tensor(0.2961, device='cuda:0'), 'train_loss_bbox': tensor(0.0093, device='cuda:0'), 'train_loss_giou': tensor(0.0172, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(1.1149, device='cuda:0'), 'validation_loss_ce': tensor(0.4247, device='cuda:0'), 'validation_loss_bbox': tensor(0.0386, device='cuda:0'), 'validation_loss_giou': tensor(0.2485, device='cuda:0'), 'validation_cardinality_error': tensor(2.0769, device='cuda:0')}
-{'training_loss': tensor(0.4584, device='cuda:0'), 'train_loss_ce': tensor(0.3948, device='cuda:0'), 'train_loss_bbox': tensor(0.0064, device='cuda:0'), 'train_loss_giou': tensor(0.0158, device='cuda:0'), 'train_cardinality_error': tensor(2., device='cuda:0'), 'validation_loss': tensor(1.1380, device='cuda:0'), 'validation_loss_ce': tensor(0.4232, device='cuda:0'), 'validation_loss_bbox': tensor(0.0378, device='cuda:0'), 'validation_loss_giou': tensor(0.2628, device='cuda:0'), 'validation_cardinality_error': tensor(2.0769, device='cuda:0')}
-{'training_loss': tensor(0.3421, device='cuda:0'), 'train_loss_ce': tensor(0.2475, device='cuda:0'), 'train_loss_bbox': tensor(0.0112, device='cuda:0'), 'train_loss_giou': tensor(0.0192, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(1.1390, device='cuda:0'), 'validation_loss_ce': tensor(0.4207, device='cuda:0'), 'validation_loss_bbox': tensor(0.0380, device='cuda:0'), 'validation_loss_giou': tensor(0.2642, device='cuda:0'), 'validation_cardinality_error': tensor(2.0769, device='cuda:0')}
-{'training_loss': tensor(0.4257, device='cuda:0'), 'train_loss_ce': tensor(0.3720, device='cuda:0'), 'train_loss_bbox': tensor(0.0057, device='cuda:0'), 'train_loss_giou': tensor(0.0126, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(1.0459, device='cuda:0'), 'validation_loss_ce': tensor(0.4058, device='cuda:0'), 'validation_loss_bbox': tensor(0.0358, device='cuda:0'), 'validation_loss_giou': tensor(0.2307, device='cuda:0'), 'validation_cardinality_error': tensor(1.6923, device='cuda:0')}
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+{'training_loss': tensor(0.4434, device='cuda:0'), 'train_loss_ce': tensor(0.3431, device='cuda:0'), 'train_loss_bbox': tensor(0.0047, device='cuda:0'), 'train_loss_giou': tensor(0.0385, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.9164, device='cuda:0'), 'validation_loss_ce': tensor(0.3956, device='cuda:0'), 'validation_loss_bbox': tensor(0.0263, device='cuda:0'), 'validation_loss_giou': tensor(0.1947, device='cuda:0'), 'validation_cardinality_error': tensor(1.6923, device='cuda:0')}
+{'training_loss': tensor(0.7076, device='cuda:0'), 'train_loss_ce': tensor(0.5633, device='cuda:0'), 'train_loss_bbox': tensor(0.0063, device='cuda:0'), 'train_loss_giou': tensor(0.0564, device='cuda:0'), 'train_cardinality_error': tensor(5., device='cuda:0'), 'validation_loss': tensor(1.0224, device='cuda:0'), 'validation_loss_ce': tensor(0.3994, device='cuda:0'), 'validation_loss_bbox': tensor(0.0360, device='cuda:0'), 'validation_loss_giou': tensor(0.2216, device='cuda:0'), 'validation_cardinality_error': tensor(1.8462, device='cuda:0')}
+{'training_loss': tensor(0.3618, device='cuda:0'), 'train_loss_ce': tensor(0.2966, device='cuda:0'), 'train_loss_bbox': tensor(0.0034, device='cuda:0'), 'train_loss_giou': tensor(0.0242, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.9028, device='cuda:0'), 'validation_loss_ce': tensor(0.3809, device='cuda:0'), 'validation_loss_bbox': tensor(0.0293, device='cuda:0'), 'validation_loss_giou': tensor(0.1877, device='cuda:0'), 'validation_cardinality_error': tensor(1.8462, device='cuda:0')}
+{'training_loss': tensor(0.5109, device='cuda:0'), 'train_loss_ce': tensor(0.3869, device='cuda:0'), 'train_loss_bbox': tensor(0.0057, device='cuda:0'), 'train_loss_giou': tensor(0.0478, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.9129, device='cuda:0'), 'validation_loss_ce': tensor(0.3762, device='cuda:0'), 'validation_loss_bbox': tensor(0.0264, device='cuda:0'), 'validation_loss_giou': tensor(0.2024, device='cuda:0'), 'validation_cardinality_error': tensor(1.6923, device='cuda:0')}
+{'training_loss': tensor(0.3791, device='cuda:0'), 'train_loss_ce': tensor(0.3609, device='cuda:0'), 'train_loss_bbox': tensor(0.0016, device='cuda:0'), 'train_loss_giou': tensor(0.0050, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.9797, device='cuda:0'), 'validation_loss_ce': tensor(0.3611, device='cuda:0'), 'validation_loss_bbox': tensor(0.0331, device='cuda:0'), 'validation_loss_giou': tensor(0.2265, device='cuda:0'), 'validation_cardinality_error': tensor(1.6923, device='cuda:0')}
+{'training_loss': tensor(0.2169, device='cuda:0'), 'train_loss_ce': tensor(0.1839, device='cuda:0'), 'train_loss_bbox': tensor(0.0030, device='cuda:0'), 'train_loss_giou': tensor(0.0091, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(0.8334, device='cuda:0'), 'validation_loss_ce': tensor(0.3444, device='cuda:0'), 'validation_loss_bbox': tensor(0.0261, device='cuda:0'), 'validation_loss_giou': tensor(0.1792, device='cuda:0'), 'validation_cardinality_error': tensor(1.7692, device='cuda:0')}
 ```
 
 ## Examples