Model save

README.md

@@ -21,7 +21,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.640502354788069
+      value: 0.8741148701809599
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -31,17 +31,17 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the imagefolder dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.6403
-- Accuracy: 0.6405
-- Weighted f1: 0.5795
-- Micro f1: 0.6405
-- Macro f1: 0.5605
-- Weighted recall: 0.6405
-- Micro recall: 0.6405
-- Macro recall: 0.6218
-- Weighted precision: 0.7274
-- Micro precision: 0.6405
-- Macro precision: 0.7335
+- Loss: 0.2285
+- Accuracy: 0.8741
+- Weighted f1: 0.8720
+- Micro f1: 0.8741
+- Macro f1: 0.8640
+- Weighted recall: 0.8741
+- Micro recall: 0.8741
+- Macro recall: 0.8626
+- Weighted precision: 0.8780
+- Micro precision: 0.8741
+- Macro precision: 0.8745
 
 ## Model description
 
@@ -75,9 +75,9 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
-| 1.3111        | 0.97  | 19   | 1.0047          | 0.4615   | 0.3926      | 0.4615   | 0.3846   | 0.4615          | 0.4615       | 0.4584       | 0.3633             | 0.4615          | 0.3514          |
-| 0.923         | 2.0   | 39   | 0.7627          | 0.6005   | 0.5304      | 0.6005   | 0.5094   | 0.6005          | 0.6005       | 0.5864       | 0.7225             | 0.6005          | 0.7237          |
-| 0.7743        | 2.92  | 57   | 0.6403          | 0.6405   | 0.5795      | 0.6405   | 0.5605   | 0.6405          | 0.6405       | 0.6218       | 0.7274             | 0.6405          | 0.7335          |
+| 0.8637        | 0.98  | 38   | 0.5986          | 0.6778   | 0.5949      | 0.6778   | 0.5662   | 0.6778          | 0.6778       | 0.6474       | 0.7506             | 0.6778          | 0.7511          |
+| 0.5018        | 1.99  | 77   | 0.2707          | 0.8517   | 0.8453      | 0.8517   | 0.8377   | 0.8517          | 0.8517       | 0.8345       | 0.8588             | 0.8517          | 0.8623          |
+| 0.3761        | 2.94  | 114  | 0.2285          | 0.8741   | 0.8720      | 0.8741   | 0.8640   | 0.8741          | 0.8741       | 0.8626       | 0.8780             | 0.8741          | 0.8745          |
 
 
 ### Framework versions