update model card README.md

README.md

@@ -21,7 +21,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.9259259259259259
+      value: 0.9726247987117552
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -31,8 +31,8 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.2031
-- Accuracy: 0.9259
+- Loss: 0.0668
+- Accuracy: 0.9726
 
 ## Model description
 
@@ -51,7 +51,7 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 1e-05
+- learning_rate: 5e-05
 - train_batch_size: 8
 - eval_batch_size: 8
 - seed: 42
@@ -59,18 +59,23 @@ The following hyperparameters were used during training:
 - total_train_batch_size: 32
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- lr_scheduler_warmup_ratio: 0.1
-- num_epochs: 5
+- lr_scheduler_warmup_ratio: 0.01
+- num_epochs: 10
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 0.6591        | 1.0   | 174  | 0.5232          | 0.8068   |
-| 0.4104        | 2.0   | 349  | 0.3161          | 0.8889   |
-| 0.3559        | 3.0   | 523  | 0.2237          | 0.9163   |
-| 0.3487        | 4.0   | 698  | 0.1999          | 0.9195   |
-| 0.3422        | 4.99  | 870  | 0.2031          | 0.9259   |
+| 0.4444        | 1.0   | 174  | 0.2271          | 0.9163   |
+| 0.3518        | 2.0   | 349  | 0.2449          | 0.9034   |
+| 0.225         | 3.0   | 523  | 0.1325          | 0.9501   |
+| 0.2195        | 4.0   | 698  | 0.1024          | 0.9549   |
+| 0.2627        | 5.0   | 872  | 0.1046          | 0.9630   |
+| 0.142         | 6.0   | 1047 | 0.0839          | 0.9726   |
+| 0.1516        | 7.0   | 1221 | 0.0918          | 0.9630   |
+| 0.1498        | 8.0   | 1396 | 0.0780          | 0.9726   |
+| 0.1189        | 9.0   | 1570 | 0.0721          | 0.9662   |
+| 0.1594        | 9.97  | 1740 | 0.0668          | 0.9726   |
 
 
 ### Framework versions