Update README file

README.md

@@ -6,35 +6,35 @@ tags: []
 ## Original result
 ```
 IoU metric: bbox
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.028
- Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.126
- Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.012
+ 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 ] = -1.000
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.028
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.050
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.200
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.350
+ 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 ] = 0.000
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
  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 ] = -1.000
- Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.350
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 ```
 
 ## After training result
 ```
 IoU metric: bbox
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.264
- Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.670
- Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.182
+ 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 ] = -1.000
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.264
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.150
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.650
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.650
+ 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 ] = 0.000
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000
  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 ] = -1.000
- Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.650
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
 ```
 
 ## Config
@@ -49,8 +49,8 @@ IoU metric: bbox
 ## Logging
 ### Training process
 ```
-{'validation_loss': tensor(4.5317, device='cuda:0'), 'validation_loss_ce': tensor(2.2122, device='cuda:0'), 'validation_loss_bbox': tensor(0.2691, device='cuda:0'), 'validation_loss_giou': tensor(0.4871, device='cuda:0'), 'validation_cardinality_error': tensor(98.5000, device='cuda:0')}
-{'training_loss': tensor(4.5317, device='cuda:0'), 'train_loss_ce': tensor(2.2122, device='cuda:0'), 'train_loss_bbox': tensor(0.2691, device='cuda:0'), 'train_loss_giou': tensor(0.4871, device='cuda:0'), 'train_cardinality_error': tensor(98.5000, device='cuda:0'), 'validation_loss': tensor(2.9984, device='cuda:0'), 'validation_loss_ce': tensor(2.0644, device='cuda:0'), 'validation_loss_bbox': tensor(0.1045, device='cuda:0'), 'validation_loss_giou': tensor(0.2057, device='cuda:0'), 'validation_cardinality_error': tensor(90.5000, device='cuda:0')}
+{'validation_loss': tensor(5.6946, device='cuda:0'), 'validation_loss_ce': tensor(2.1729, device='cuda:0'), 'validation_loss_bbox': tensor(0.4112, device='cuda:0'), 'validation_loss_giou': tensor(0.7330, device='cuda:0'), 'validation_cardinality_error': tensor(94.5000, device='cuda:0')}
+{'training_loss': tensor(5.6946, device='cuda:0'), 'train_loss_ce': tensor(2.1729, device='cuda:0'), 'train_loss_bbox': tensor(0.4112, device='cuda:0'), 'train_loss_giou': tensor(0.7330, device='cuda:0'), 'train_cardinality_error': tensor(94.5000, device='cuda:0'), 'validation_loss': tensor(3.1776, device='cuda:0'), 'validation_loss_ce': tensor(1.9970, device='cuda:0'), 'validation_loss_bbox': tensor(0.1255, device='cuda:0'), 'validation_loss_giou': tensor(0.2765, device='cuda:0'), 'validation_cardinality_error': tensor(79., device='cuda:0')}
 ```
 
 ## Examples