Model save

README.md

@@ -22,7 +22,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.7656465622209595
+      value: 0.987904862407663
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,8 +32,8 @@ 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 fair_face dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.6669
-- Accuracy: 0.7656
+- Loss: 0.0743
+- Accuracy: 0.9879
 
 ## Model description
 
@@ -58,15 +58,18 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 2
+- num_epochs: 5
 - mixed_precision_training: Native AMP
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 1.1069        | 1.0   | 385  | 0.9425          | 0.6209   |
-| 0.8465        | 2.0   | 770  | 0.6669          | 0.7656   |
+| 1.2011        | 1.0   | 385  | 1.0297          | 0.5664   |
+| 0.8578        | 2.0   | 770  | 0.7667          | 0.6936   |
+| 0.5961        | 3.0   | 1155 | 0.4088          | 0.8703   |
+| 0.3073        | 4.0   | 1540 | 0.1689          | 0.9581   |
+| 0.1146        | 5.0   | 1925 | 0.0743          | 0.9879   |
 
 
 ### Framework versions