update model card README.md

README.md

@@ -24,16 +24,16 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.9877777777777778
+      value: 0.9844444444444445
     - name: F1
       type: f1
-      value: 0.9877739882871689
+      value: 0.9844678306487884
     - name: Precision
       type: precision
-      value: 0.9878779920239203
+      value: 0.9846508141836958
     - name: Recall
       type: recall
-      value: 0.9877777777777778
+      value: 0.9844444444444445
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -43,11 +43,11 @@ 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: 0.0336
-- Accuracy: 0.9878
-- F1: 0.9878
-- Precision: 0.9879
-- Recall: 0.9878
+- Loss: 0.0393
+- Accuracy: 0.9844
+- F1: 0.9845
+- Precision: 0.9847
+- Recall: 0.9844
 
 ## Model description
 
@@ -66,7 +66,7 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 0.0002
+- learning_rate: 0.0001
 - train_batch_size: 64
 - eval_batch_size: 64
 - seed: 42
@@ -74,18 +74,18 @@ The following hyperparameters were used during training:
 - total_train_batch_size: 256
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- lr_scheduler_warmup_ratio: 0.1
+- lr_scheduler_warmup_ratio: 0.2
 - num_epochs: 5
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
-| 0.3068        | 1.0   | 95   | 0.1464          | 0.9470   | 0.9474 | 0.9503    | 0.9470 |
-| 0.2044        | 2.0   | 190  | 0.0885          | 0.97     | 0.9700 | 0.9711    | 0.97   |
-| 0.1549        | 3.0   | 285  | 0.0612          | 0.98     | 0.9800 | 0.9802    | 0.98   |
-| 0.1279        | 4.0   | 380  | 0.0527          | 0.9793   | 0.9793 | 0.9795    | 0.9793 |
-| 0.104         | 5.0   | 475  | 0.0336          | 0.9878   | 0.9878 | 0.9879    | 0.9878 |
+| 0.3039        | 1.0   | 95   | 0.1300          | 0.9607   | 0.9609 | 0.9619    | 0.9607 |
+| 0.2357        | 2.0   | 190  | 0.0815          | 0.9678   | 0.9678 | 0.9685    | 0.9678 |
+| 0.163         | 3.0   | 285  | 0.0559          | 0.9807   | 0.9807 | 0.9809    | 0.9807 |
+| 0.1267        | 4.0   | 380  | 0.0492          | 0.9837   | 0.9837 | 0.9839    | 0.9837 |
+| 0.1059        | 5.0   | 475  | 0.0393          | 0.9844   | 0.9845 | 0.9847    | 0.9844 |
 
 
 ### Framework versions