Update README file

README.md

@@ -6,54 +6,54 @@ tags: []
 ## Original result
 ```
 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=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 ] = 0.000
- 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 ] = 0.000
- 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 ] = 0.000
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.005
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.005
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.005
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.203
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.068
+ 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 ] = 0.029
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.029
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.029
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.200
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.067
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.029
 ```
 
 ## After training result
 ```
 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=   all | maxDets=100 ] = 0.009
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.020
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.008
  Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
  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 ] = 0.000
- 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 Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.009
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.043
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.076
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.087
  Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
  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 ] = 0.000
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.089
 ```
 
 ## Config
-- dataset: NIH
+- dataset: VinXray
 - original model: hustvl/yolos-tiny
 - lr: 0.0001
 - dropout_rate: 0.1
 - weight_decay: 0.0001
 - max_epochs: 1
-- train samples: 7
+- train samples: 67234
 
 ## Logging
 ### Training process
 ```
-{'validation_loss': tensor(7.8884), 'validation_loss_ce': tensor(3.2805), 'validation_loss_bbox': tensor(0.4682), 'validation_loss_giou': tensor(1.1333), 'validation_cardinality_error': tensor(99.4286)}
-{'training_loss': tensor(7.8884), 'train_loss_ce': tensor(3.2805), 'train_loss_bbox': tensor(0.4682), 'train_loss_giou': tensor(1.1333), 'train_cardinality_error': tensor(99.4286), 'validation_loss': tensor(6.6633), 'validation_loss_ce': tensor(2.8980), 'validation_loss_bbox': tensor(0.3610), 'validation_loss_giou': tensor(0.9802), 'validation_cardinality_error': tensor(99.4286)}
+{'validation_loss': tensor(8.5927, device='cuda:0'), 'validation_loss_ce': tensor(3.4775, device='cuda:0'), 'validation_loss_bbox': tensor(0.5756, device='cuda:0'), 'validation_loss_giou': tensor(1.1184, device='cuda:0'), 'validation_cardinality_error': tensor(99.5938, device='cuda:0')}
+{'training_loss': tensor(1.3630, device='cuda:0'), 'train_loss_ce': tensor(0.2593, device='cuda:0'), 'train_loss_bbox': tensor(0.0803, device='cuda:0'), 'train_loss_giou': tensor(0.3511, device='cuda:0'), 'train_cardinality_error': tensor(0.5294, device='cuda:0'), 'validation_loss': tensor(1.5262, device='cuda:0'), 'validation_loss_ce': tensor(0.2351, device='cuda:0'), 'validation_loss_bbox': tensor(0.0827, device='cuda:0'), 'validation_loss_giou': tensor(0.4389, device='cuda:0'), 'validation_cardinality_error': tensor(0.4794, device='cuda:0')}
 ```
 
 ## Examples
-{'size': tensor([512, 512]), 'image_id': tensor([28719]), 'class_labels': tensor([], dtype=torch.int64), 'boxes': tensor([], size=(0, 4)), 'area': tensor([]), 'iscrowd': tensor([], dtype=torch.int64), 'orig_size': tensor([3072, 3072])}
+{'size': tensor([560, 512]), 'image_id': tensor([1]), 'class_labels': tensor([], dtype=torch.int64), 'boxes': tensor([], size=(0, 4)), 'area': tensor([]), 'iscrowd': tensor([], dtype=torch.int64), 'orig_size': tensor([2580, 2332])}
 
 ![Example](./example.png)