update model card README.md

README.md

@@ -24,16 +24,16 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.9985872380503885
+      value: 0.9977631269131152
     - name: Precision
       type: precision
-      value: 0.9989954885489135
+      value: 0.998134723737648
     - name: Recall
       type: recall
-      value: 0.998161142953993
+      value: 0.9974298183920257
     - name: F1
       type: f1
-      value: 0.9985770990024514
+      value: 0.9977816548360952
 ---
 
 <!-- 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 [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0217
-- Accuracy: 0.9986
-- Precision: 0.9990
-- Recall: 0.9982
-- F1: 0.9986
+- Loss: 0.0228
+- Accuracy: 0.9978
+- Precision: 0.9981
+- Recall: 0.9974
+- F1: 0.9978
 
 ## Model description
 
@@ -81,11 +81,11 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
-| 0.1235        | 1.0   | 149  | 0.0936          | 0.9858   | 0.9845    | 0.9814 | 0.9830 |
-| 0.067         | 2.0   | 299  | 0.0622          | 0.9878   | 0.9909    | 0.9813 | 0.9859 |
-| 0.049         | 3.0   | 448  | 0.0322          | 0.9968   | 0.9969    | 0.9959 | 0.9964 |
-| 0.0477        | 4.0   | 598  | 0.0249          | 0.9978   | 0.9985    | 0.9965 | 0.9975 |
-| 0.0336        | 4.98  | 745  | 0.0217          | 0.9986   | 0.9990    | 0.9982 | 0.9986 |
+| 0.1415        | 1.0   | 149  | 0.1286          | 0.9712   | 0.9788    | 0.9623 | 0.9700 |
+| 0.0671        | 2.0   | 299  | 0.0463          | 0.9948   | 0.9917    | 0.9946 | 0.9932 |
+| 0.0423        | 3.0   | 448  | 0.0356          | 0.9952   | 0.9970    | 0.9908 | 0.9939 |
+| 0.0383        | 4.0   | 598  | 0.0242          | 0.9976   | 0.9980    | 0.9972 | 0.9976 |
+| 0.033         | 4.98  | 745  | 0.0228          | 0.9978   | 0.9981    | 0.9974 | 0.9978 |
 
 
 ### Framework versions