Model save

README.md

@@ -25,16 +25,16 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.9259882253994953
+      value: 0.9461732548359967
     - name: F1
       type: f1
-      value: 0.9268755487412734
+      value: 0.9463827697148198
     - name: Precision
       type: precision
-      value: 0.9315188840219485
+      value: 0.9476585951632728
     - name: Recall
       type: recall
-      value: 0.9259882253994953
+      value: 0.9461732548359967
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -44,11 +44,11 @@ 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: 0.3230
-- Accuracy: 0.9260
-- F1: 0.9269
-- Precision: 0.9315
-- Recall: 0.9260
+- Loss: 0.2256
+- Accuracy: 0.9462
+- F1: 0.9464
+- Precision: 0.9477
+- Recall: 0.9462
 
 ## Model description
 
@@ -82,9 +82,9 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
-| 0.6474        | 1.0   | 297  | 0.6716          | 0.8360   | 0.8380 | 0.8527    | 0.8360 |
-| 0.413         | 2.0   | 594  | 0.4295          | 0.8999   | 0.9013 | 0.9098    | 0.8999 |
-| 0.2144        | 3.0   | 891  | 0.3230          | 0.9260   | 0.9269 | 0.9315    | 0.9260 |
+| 0.2716        | 1.0   | 297  | 0.3630          | 0.8957   | 0.8961 | 0.9047    | 0.8957 |
+| 0.098         | 2.0   | 594  | 0.2674          | 0.9344   | 0.9350 | 0.9372    | 0.9344 |
+| 0.0487        | 3.0   | 891  | 0.2256          | 0.9462   | 0.9464 | 0.9477    | 0.9462 |
 
 
 ### Framework versions