Upload README.md

README.md

@@ -1,55 +1,55 @@
 ---
-    library_name: transformers
-    tags: []
-    ---
+library_name: transformers
+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 ] = -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 ] = 0.000
- 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 ] = 0.001
- 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 ] = 0.001
+## 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 ] = -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 ] = 0.000
+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 ] = 0.001
+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 ] = 0.001
 ```
 
-    ## After training result
-    ```
-    IoU metric: bbox
- Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.826
- Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.867
- Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.864
- 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 ] = 0.716
- Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.844
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.841
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.844
- Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.844
- 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 ] = 0.750
- Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.854
+## After training result
+```
+IoU metric: bbox
+Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.826
+Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.867
+Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.864
+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 ] = 0.716
+Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.844
+Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.841
+Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.844
+Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.844
+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 ] = 0.750
+Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.854
 ```
 
-    ## Config
-    - dataset: NIH
-    - original model: hustvl/yolos-tiny
-    - lr: 1e-05
-    - dropout_rate: 0.1
-    - weight_decay: 0.001
-    - max_epochs: 600
-    - train samples: 354
+## Config
+- dataset: NIH
+- original model: hustvl/yolos-tiny
+- lr: 1e-05
+- dropout_rate: 0.1
+- weight_decay: 0.001
+- max_epochs: 600
+- train samples: 354
 
-    ## Logging
-    ### Training process
-    ```
-    {'validation_loss': tensor(7.1177, device='cuda:0'), 'validation_loss_ce': tensor(2.3438, device='cuda:0'), 'validation_loss_bbox': tensor(0.5517, device='cuda:0'), 'validation_loss_giou': tensor(1.0076, device='cuda:0'), 'validation_cardinality_error': tensor(98.9375, device='cuda:0')}
+## Logging
+### Training process
+```
+{'validation_loss': tensor(7.1177, device='cuda:0'), 'validation_loss_ce': tensor(2.3438, device='cuda:0'), 'validation_loss_bbox': tensor(0.5517, device='cuda:0'), 'validation_loss_giou': tensor(1.0076, device='cuda:0'), 'validation_cardinality_error': tensor(98.9375, device='cuda:0')}
 {'training_loss': tensor(4.8380, device='cuda:0'), 'train_loss_ce': tensor(1.9076, device='cuda:0'), 'train_loss_bbox': tensor(0.2720, device='cuda:0'), 'train_loss_giou': tensor(0.7852, device='cuda:0'), 'train_cardinality_error': tensor(40.5000, device='cuda:0'), 'validation_loss': tensor(4.6572, device='cuda:0'), 'validation_loss_ce': tensor(1.9323, device='cuda:0'), 'validation_loss_bbox': tensor(0.2644, device='cuda:0'), 'validation_loss_giou': tensor(0.7016, device='cuda:0'), 'validation_cardinality_error': tensor(64.3898, device='cuda:0')}
 {'training_loss': tensor(4.6511, device='cuda:0'), 'train_loss_ce': tensor(1.7157, device='cuda:0'), 'train_loss_bbox': tensor(0.2469, device='cuda:0'), 'train_loss_giou': tensor(0.8504, device='cuda:0'), 'train_cardinality_error': tensor(18.5000, device='cuda:0'), 'validation_loss': tensor(3.6326, device='cuda:0'), 'validation_loss_ce': tensor(1.5263, device='cuda:0'), 'validation_loss_bbox': tensor(0.1872, device='cuda:0'), 'validation_loss_giou': tensor(0.5851, device='cuda:0'), 'validation_cardinality_error': tensor(5.3277, device='cuda:0')}
 {'training_loss': tensor(4.4295, device='cuda:0'), 'train_loss_ce': tensor(1.1966, device='cuda:0'), 'train_loss_bbox': tensor(0.2759, device='cuda:0'), 'train_loss_giou': tensor(0.9268, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.9645, device='cuda:0'), 'validation_loss_ce': tensor(1.0482, device='cuda:0'), 'validation_loss_bbox': tensor(0.1677, device='cuda:0'), 'validation_loss_giou': tensor(0.5388, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')}
@@ -650,10 +650,10 @@
 {'training_loss': tensor(0.0699, device='cuda:0'), 'train_loss_ce': tensor(0.0079, device='cuda:0'), 'train_loss_bbox': tensor(0.0044, device='cuda:0'), 'train_loss_giou': tensor(0.0199, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.1210, device='cuda:0'), 'validation_loss_ce': tensor(0.0077, device='cuda:0'), 'validation_loss_bbox': tensor(0.0070, device='cuda:0'), 'validation_loss_giou': tensor(0.0393, device='cuda:0'), 'validation_cardinality_error': tensor(0.2203, device='cuda:0')}
 {'training_loss': tensor(0.0682, device='cuda:0'), 'train_loss_ce': tensor(0.0051, device='cuda:0'), 'train_loss_bbox': tensor(0.0059, device='cuda:0'), 'train_loss_giou': tensor(0.0168, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.1295, device='cuda:0'), 'validation_loss_ce': tensor(0.0075, device='cuda:0'), 'validation_loss_bbox': tensor(0.0073, device='cuda:0'), 'validation_loss_giou': tensor(0.0427, device='cuda:0'), 'validation_cardinality_error': tensor(0.1695, device='cuda:0')}
 {'training_loss': tensor(0.0636, device='cuda:0'), 'train_loss_ce': tensor(0.0103, device='cuda:0'), 'train_loss_bbox': tensor(0.0037, device='cuda:0'), 'train_loss_giou': tensor(0.0173, device='cuda:0'), 'train_cardinality_error': tensor(0.5000, device='cuda:0'), 'validation_loss': tensor(0.1114, device='cuda:0'), 'validation_loss_ce': tensor(0.0071, device='cuda:0'), 'validation_loss_bbox': tensor(0.0064, device='cuda:0'), 'validation_loss_giou': tensor(0.0360, device='cuda:0'), 'validation_cardinality_error': tensor(0.2006, device='cuda:0')}
-    ```
+```
 
-    ## Examples
-    {'size': tensor([512, 512]), 'image_id': tensor([1]), 'class_labels': tensor([4]), 'boxes': tensor([[0.2622, 0.5729, 0.0847, 0.0773]]), 'area': tensor([1717.9431]), 'iscrowd': tensor([0]), 'orig_size': tensor([1024, 1024])}
+## Examples
+{'size': tensor([512, 512]), 'image_id': tensor([1]), 'class_labels': tensor([4]), 'boxes': tensor([[0.2622, 0.5729, 0.0847, 0.0773]]), 'area': tensor([1717.9431]), 'iscrowd': tensor([0]), 'orig_size': tensor([1024, 1024])}
 
-    ![Example](./example.png)
-    
+![Example](./example.png)
+