Update README file

README.md

@@ -9,62 +9,62 @@ 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 ] = 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 ] = 0.004
+ 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 ] = 0.006
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.007
- Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.025
+ 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 ] = 0.007
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.025
 ```
 
 ## After training result
 ```
 IoU metric: bbox
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.003
- Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.008
- Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.001
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.005
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.015
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.003
+ 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 ] = 0.003
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.045
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.082
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.100
- Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.037
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.105
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.110
+ 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 ] = 0.102
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.111
 ```
 
 ## Config
 - dataset: NIH
 - original model: facebook/detr-resnet-50
-- lr: 5e-05
+- lr: 0.0001
 - max_epochs: 20
 
 ## Logging
 ### Training process
 ```
-{'validation_loss': tensor(6.2787, device='cuda:0'), 'validation_loss_ce': tensor(2.6265, device='cuda:0'), 'validation_loss_bbox': tensor(0.4374, device='cuda:0'), 'validation_loss_giou': tensor(0.7325, device='cuda:0'), 'validation_cardinality_error': tensor(98.8438, device='cuda:0')}
-{'training_loss': tensor(2.4728, device='cuda:0'), 'train_loss_ce': tensor(0.8662, device='cuda:0'), 'train_loss_bbox': tensor(0.1563, device='cuda:0'), 'train_loss_giou': tensor(0.4126, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.8297, device='cuda:0'), 'validation_loss_ce': tensor(0.8479, device='cuda:0'), 'validation_loss_bbox': tensor(0.1815, device='cuda:0'), 'validation_loss_giou': tensor(0.5372, device='cuda:0'), 'validation_cardinality_error': tensor(1.1023, device='cuda:0')}
-{'training_loss': tensor(2.5428, device='cuda:0'), 'train_loss_ce': tensor(0.5452, device='cuda:0'), 'train_loss_bbox': tensor(0.1659, device='cuda:0'), 'train_loss_giou': tensor(0.5841, device='cuda:0'), 'train_cardinality_error': tensor(1.0625, device='cuda:0'), 'validation_loss': tensor(2.4400, device='cuda:0'), 'validation_loss_ce': tensor(0.5658, device='cuda:0'), 'validation_loss_bbox': tensor(0.1644, device='cuda:0'), 'validation_loss_giou': tensor(0.5261, device='cuda:0'), 'validation_cardinality_error': tensor(1.1023, device='cuda:0')}
-{'training_loss': tensor(2.7825, device='cuda:0'), 'train_loss_ce': tensor(0.5663, device='cuda:0'), 'train_loss_bbox': tensor(0.1977, device='cuda:0'), 'train_loss_giou': tensor(0.6140, device='cuda:0'), 'train_cardinality_error': tensor(1.4375, device='cuda:0'), 'validation_loss': tensor(2.3192, device='cuda:0'), 'validation_loss_ce': tensor(0.5071, device='cuda:0'), 'validation_loss_bbox': tensor(0.1524, device='cuda:0'), 'validation_loss_giou': tensor(0.5251, device='cuda:0'), 'validation_cardinality_error': tensor(1.1023, device='cuda:0')}
-{'training_loss': tensor(2.0775, device='cuda:0'), 'train_loss_ce': tensor(0.5095, device='cuda:0'), 'train_loss_bbox': tensor(0.1396, device='cuda:0'), 'train_loss_giou': tensor(0.4350, device='cuda:0'), 'train_cardinality_error': tensor(1.1250, device='cuda:0'), 'validation_loss': tensor(2.1858, device='cuda:0'), 'validation_loss_ce': tensor(0.4854, device='cuda:0'), 'validation_loss_bbox': tensor(0.1473, device='cuda:0'), 'validation_loss_giou': tensor(0.4819, device='cuda:0'), 'validation_cardinality_error': tensor(1.1023, device='cuda:0')}
-{'training_loss': tensor(2.1950, device='cuda:0'), 'train_loss_ce': tensor(0.4945, device='cuda:0'), 'train_loss_bbox': tensor(0.1309, device='cuda:0'), 'train_loss_giou': tensor(0.5231, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.1162, device='cuda:0'), 'validation_loss_ce': tensor(0.4827, device='cuda:0'), 'validation_loss_bbox': tensor(0.1368, device='cuda:0'), 'validation_loss_giou': tensor(0.4747, device='cuda:0'), 'validation_cardinality_error': tensor(1.1023, device='cuda:0')}
-{'training_loss': tensor(1.8894, device='cuda:0'), 'train_loss_ce': tensor(0.4502, device='cuda:0'), 'train_loss_bbox': tensor(0.1240, device='cuda:0'), 'train_loss_giou': tensor(0.4097, device='cuda:0'), 'train_cardinality_error': tensor(1.0625, device='cuda:0'), 'validation_loss': tensor(2.0748, device='cuda:0'), 'validation_loss_ce': tensor(0.4766, device='cuda:0'), 'validation_loss_bbox': tensor(0.1295, device='cuda:0'), 'validation_loss_giou': tensor(0.4753, device='cuda:0'), 'validation_cardinality_error': tensor(1.1023, device='cuda:0')}
-{'training_loss': tensor(2.0579, device='cuda:0'), 'train_loss_ce': tensor(0.5390, device='cuda:0'), 'train_loss_bbox': tensor(0.1282, device='cuda:0'), 'train_loss_giou': tensor(0.4390, device='cuda:0'), 'train_cardinality_error': tensor(1.2500, device='cuda:0'), 'validation_loss': tensor(1.9857, device='cuda:0'), 'validation_loss_ce': tensor(0.4704, device='cuda:0'), 'validation_loss_bbox': tensor(0.1222, device='cuda:0'), 'validation_loss_giou': tensor(0.4522, device='cuda:0'), 'validation_cardinality_error': tensor(1.1023, device='cuda:0')}
-{'training_loss': tensor(2.0756, device='cuda:0'), 'train_loss_ce': tensor(0.4579, device='cuda:0'), 'train_loss_bbox': tensor(0.1233, device='cuda:0'), 'train_loss_giou': tensor(0.5005, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(1.9634, device='cuda:0'), 'validation_loss_ce': tensor(0.4642, device='cuda:0'), 'validation_loss_bbox': tensor(0.1178, device='cuda:0'), 'validation_loss_giou': tensor(0.4551, device='cuda:0'), 'validation_cardinality_error': tensor(1.1023, device='cuda:0')}
-{'training_loss': tensor(2.5117, device='cuda:0'), 'train_loss_ce': tensor(0.5013, device='cuda:0'), 'train_loss_bbox': tensor(0.1788, device='cuda:0'), 'train_loss_giou': tensor(0.5582, device='cuda:0'), 'train_cardinality_error': tensor(1.1250, device='cuda:0'), 'validation_loss': tensor(2.5719, device='cuda:0'), 'validation_loss_ce': tensor(0.4732, device='cuda:0'), 'validation_loss_bbox': tensor(0.1865, device='cuda:0'), 'validation_loss_giou': tensor(0.5831, device='cuda:0'), 'validation_cardinality_error': tensor(1.1023, device='cuda:0')}
-{'training_loss': tensor(2.8214, device='cuda:0'), 'train_loss_ce': tensor(0.4699, device='cuda:0'), 'train_loss_bbox': tensor(0.2064, device='cuda:0'), 'train_loss_giou': tensor(0.6598, device='cuda:0'), 'train_cardinality_error': tensor(1.0625, device='cuda:0'), 'validation_loss': tensor(2.8114, device='cuda:0'), 'validation_loss_ce': tensor(0.4901, device='cuda:0'), 'validation_loss_bbox': tensor(0.2061, device='cuda:0'), 'validation_loss_giou': tensor(0.6454, device='cuda:0'), 'validation_cardinality_error': tensor(1.1023, device='cuda:0')}
-{'training_loss': tensor(2.5244, device='cuda:0'), 'train_loss_ce': tensor(0.4981, device='cuda:0'), 'train_loss_bbox': tensor(0.1825, device='cuda:0'), 'train_loss_giou': tensor(0.5569, device='cuda:0'), 'train_cardinality_error': tensor(1.0625, device='cuda:0'), 'validation_loss': tensor(2.4898, device='cuda:0'), 'validation_loss_ce': tensor(0.4951, device='cuda:0'), 'validation_loss_bbox': tensor(0.1762, device='cuda:0'), 'validation_loss_giou': tensor(0.5568, device='cuda:0'), 'validation_cardinality_error': tensor(1.1023, device='cuda:0')}
-{'training_loss': tensor(2.5200, device='cuda:0'), 'train_loss_ce': tensor(0.4599, device='cuda:0'), 'train_loss_bbox': tensor(0.1521, device='cuda:0'), 'train_loss_giou': tensor(0.6498, device='cuda:0'), 'train_cardinality_error': tensor(1.1875, device='cuda:0'), 'validation_loss': tensor(2.3670, device='cuda:0'), 'validation_loss_ce': tensor(0.4635, device='cuda:0'), 'validation_loss_bbox': tensor(0.1615, device='cuda:0'), 'validation_loss_giou': tensor(0.5480, device='cuda:0'), 'validation_cardinality_error': tensor(1.1023, device='cuda:0')}
-{'training_loss': tensor(2.1473, device='cuda:0'), 'train_loss_ce': tensor(0.3752, device='cuda:0'), 'train_loss_bbox': tensor(0.1617, device='cuda:0'), 'train_loss_giou': tensor(0.4818, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.2321, device='cuda:0'), 'validation_loss_ce': tensor(0.4669, device='cuda:0'), 'validation_loss_bbox': tensor(0.1535, device='cuda:0'), 'validation_loss_giou': tensor(0.4989, device='cuda:0'), 'validation_cardinality_error': tensor(1.1023, device='cuda:0')}
-{'training_loss': tensor(2.1107, device='cuda:0'), 'train_loss_ce': tensor(0.4600, device='cuda:0'), 'train_loss_bbox': tensor(0.1382, device='cuda:0'), 'train_loss_giou': tensor(0.4798, device='cuda:0'), 'train_cardinality_error': tensor(1.0625, device='cuda:0'), 'validation_loss': tensor(2.1397, device='cuda:0'), 'validation_loss_ce': tensor(0.4597, device='cuda:0'), 'validation_loss_bbox': tensor(0.1427, device='cuda:0'), 'validation_loss_giou': tensor(0.4832, device='cuda:0'), 'validation_cardinality_error': tensor(1.1023, device='cuda:0')}
-{'training_loss': tensor(2.1844, device='cuda:0'), 'train_loss_ce': tensor(0.5193, device='cuda:0'), 'train_loss_bbox': tensor(0.1445, device='cuda:0'), 'train_loss_giou': tensor(0.4714, device='cuda:0'), 'train_cardinality_error': tensor(1.1875, device='cuda:0'), 'validation_loss': tensor(2.2037, device='cuda:0'), 'validation_loss_ce': tensor(0.4556, device='cuda:0'), 'validation_loss_bbox': tensor(0.1470, device='cuda:0'), 'validation_loss_giou': tensor(0.5064, device='cuda:0'), 'validation_cardinality_error': tensor(1.1023, device='cuda:0')}
-{'training_loss': tensor(2.2464, device='cuda:0'), 'train_loss_ce': tensor(0.4751, device='cuda:0'), 'train_loss_bbox': tensor(0.1265, device='cuda:0'), 'train_loss_giou': tensor(0.5695, device='cuda:0'), 'train_cardinality_error': tensor(1.0625, device='cuda:0'), 'validation_loss': tensor(2.1071, device='cuda:0'), 'validation_loss_ce': tensor(0.4545, device='cuda:0'), 'validation_loss_bbox': tensor(0.1350, device='cuda:0'), 'validation_loss_giou': tensor(0.4888, device='cuda:0'), 'validation_cardinality_error': tensor(1.1023, device='cuda:0')}
-{'training_loss': tensor(1.6003, device='cuda:0'), 'train_loss_ce': tensor(0.4440, device='cuda:0'), 'train_loss_bbox': tensor(0.0950, device='cuda:0'), 'train_loss_giou': tensor(0.3407, device='cuda:0'), 'train_cardinality_error': tensor(1.0625, device='cuda:0'), 'validation_loss': tensor(1.9561, device='cuda:0'), 'validation_loss_ce': tensor(0.4494, device='cuda:0'), 'validation_loss_bbox': tensor(0.1214, device='cuda:0'), 'validation_loss_giou': tensor(0.4499, device='cuda:0'), 'validation_cardinality_error': tensor(1.1023, device='cuda:0')}
-{'training_loss': tensor(2.1248, device='cuda:0'), 'train_loss_ce': tensor(0.3898, device='cuda:0'), 'train_loss_bbox': tensor(0.1418, device='cuda:0'), 'train_loss_giou': tensor(0.5129, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.1797, device='cuda:0'), 'validation_loss_ce': tensor(0.4527, device='cuda:0'), 'validation_loss_bbox': tensor(0.1483, device='cuda:0'), 'validation_loss_giou': tensor(0.4929, device='cuda:0'), 'validation_cardinality_error': tensor(1.1023, device='cuda:0')}
-{'training_loss': tensor(2.8601, device='cuda:0'), 'train_loss_ce': tensor(0.5175, device='cuda:0'), 'train_loss_bbox': tensor(0.2310, device='cuda:0'), 'train_loss_giou': tensor(0.5938, device='cuda:0'), 'train_cardinality_error': tensor(1.1875, device='cuda:0'), 'validation_loss': tensor(2.3561, device='cuda:0'), 'validation_loss_ce': tensor(0.4562, device='cuda:0'), 'validation_loss_bbox': tensor(0.1600, device='cuda:0'), 'validation_loss_giou': tensor(0.5500, device='cuda:0'), 'validation_cardinality_error': tensor(1.1023, device='cuda:0')}
-{'training_loss': tensor(1.9230, device='cuda:0'), 'train_loss_ce': tensor(0.4642, device='cuda:0'), 'train_loss_bbox': tensor(0.1238, device='cuda:0'), 'train_loss_giou': tensor(0.4198, device='cuda:0'), 'train_cardinality_error': tensor(1.1250, device='cuda:0'), 'validation_loss': tensor(2.2561, device='cuda:0'), 'validation_loss_ce': tensor(0.4476, device='cuda:0'), 'validation_loss_bbox': tensor(0.1525, device='cuda:0'), 'validation_loss_giou': tensor(0.5229, device='cuda:0'), 'validation_cardinality_error': tensor(1.1023, device='cuda:0')}
+{'validation_loss': tensor(6.3146, device='cuda:0'), 'validation_loss_ce': tensor(2.1177, device='cuda:0'), 'validation_loss_bbox': tensor(0.4698, device='cuda:0'), 'validation_loss_giou': tensor(0.9240, device='cuda:0'), 'validation_cardinality_error': tensor(92.7500, device='cuda:0')}
+{'training_loss': tensor(2.3670, device='cuda:0'), 'train_loss_ce': tensor(0.4749, device='cuda:0'), 'train_loss_bbox': tensor(0.1771, device='cuda:0'), 'train_loss_giou': tensor(0.5034, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.5731, device='cuda:0'), 'validation_loss_ce': tensor(0.4578, device='cuda:0'), 'validation_loss_bbox': tensor(0.1954, device='cuda:0'), 'validation_loss_giou': tensor(0.5691, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')}
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 ```