update model card README.md

README.md

@@ -24,16 +24,16 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.9987851440472059
+      value: 0.9979173897952099
     - name: Precision
       type: precision
-      value: 0.9987851440472059
+      value: 0.9981546674586633
     - name: Recall
       type: recall
-      value: 0.9987851440472059
+      value: 0.9976078463525905
     - name: F1
       type: f1
-      value: 0.9987851440472059
+      value: 0.9978801278905362
 ---
 
 <!-- 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: 0.0038
-- Accuracy: 0.9988
-- Precision: 0.9988
-- Recall: 0.9988
-- F1: 0.9988
+- Loss: 0.0080
+- Accuracy: 0.9979
+- Precision: 0.9982
+- Recall: 0.9976
+- F1: 0.9979
 
 ## Model description
 
@@ -81,11 +81,11 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
-| 0.0118        | 1.0   | 101  | 0.0083          | 0.9977   | 0.9977    | 0.9977 | 0.9977 |
-| 0.0269        | 2.0   | 203  | 0.0111          | 0.9969   | 0.9969    | 0.9969 | 0.9969 |
-| 0.0076        | 3.0   | 304  | 0.0093          | 0.9965   | 0.9965    | 0.9965 | 0.9965 |
-| 0.0072        | 4.0   | 406  | 0.0051          | 0.9986   | 0.9986    | 0.9986 | 0.9986 |
-| 0.01          | 4.98  | 505  | 0.0038          | 0.9988   | 0.9988    | 0.9988 | 0.9988 |
+| 0.015         | 1.0   | 101  | 0.0229          | 0.9938   | 0.9928    | 0.9929 | 0.9928 |
+| 0.0177        | 2.0   | 203  | 0.0316          | 0.9913   | 0.9928    | 0.9880 | 0.9903 |
+| 0.0177        | 3.0   | 304  | 0.0127          | 0.9958   | 0.9954    | 0.9955 | 0.9954 |
+| 0.0145        | 4.0   | 406  | 0.0129          | 0.9967   | 0.9970    | 0.9960 | 0.9965 |
+| 0.0101        | 4.98  | 505  | 0.0080          | 0.9979   | 0.9982    | 0.9976 | 0.9979 |
 
 
 ### Framework versions