update model card README.md

README.md

@@ -24,16 +24,16 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.9888888888888889
+      value: 0.9825925925925926
     - name: F1
       type: f1
-      value: 0.9888960568775346
+      value: 0.9825868474705166
     - name: Precision
       type: precision
-      value: 0.9889615535194125
+      value: 0.9828193476192771
     - name: Recall
       type: recall
-      value: 0.9888888888888889
+      value: 0.9825925925925926
 ---
 
 <!-- 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/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the imagefolder dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0383
-- Accuracy: 0.9889
-- F1: 0.9889
-- Precision: 0.9890
-- Recall: 0.9889
+- Loss: 0.0510
+- Accuracy: 0.9826
+- F1: 0.9826
+- Precision: 0.9828
+- Recall: 0.9826
 
 ## Model description
 
@@ -81,11 +81,11 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
-| 0.3514        | 1.0   | 95   | 0.1291          | 0.9563   | 0.9566 | 0.9584    | 0.9563 |
-| 0.2514        | 2.0   | 190  | 0.0652          | 0.9778   | 0.9778 | 0.9780    | 0.9778 |
-| 0.1703        | 3.0   | 285  | 0.0464          | 0.9841   | 0.9841 | 0.9842    | 0.9841 |
-| 0.1449        | 4.0   | 380  | 0.0422          | 0.9863   | 0.9863 | 0.9864    | 0.9863 |
-| 0.1303        | 5.0   | 475  | 0.0383          | 0.9889   | 0.9889 | 0.9890    | 0.9889 |
+| 0.4479        | 1.0   | 95   | 0.1592          | 0.9478   | 0.9478 | 0.9500    | 0.9478 |
+| 0.3078        | 2.0   | 190  | 0.0914          | 0.9685   | 0.9686 | 0.9695    | 0.9685 |
+| 0.2307        | 3.0   | 285  | 0.0603          | 0.9785   | 0.9785 | 0.9790    | 0.9785 |
+| 0.227         | 4.0   | 380  | 0.0531          | 0.9811   | 0.9811 | 0.9814    | 0.9811 |
+| 0.1674        | 5.0   | 475  | 0.0510          | 0.9826   | 0.9826 | 0.9828    | 0.9826 |
 
 
 ### Framework versions