Model save

README.md

@@ -22,7 +22,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.5308641975308642
+      value: 0.808641975308642
 ---
 
 <!-- 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 [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.3839
-- Accuracy: 0.5309
+- Loss: 0.5712
+- Accuracy: 0.8086
 
 ## Model description
 
@@ -61,15 +61,30 @@ The following hyperparameters were used during training:
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_ratio: 0.1
-- num_epochs: 3
+- num_epochs: 20
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| No log        | 0.87  | 5    | 1.4617          | 0.5370   |
-| 1.6107        | 1.91  | 11   | 1.3572          | 0.5370   |
-| 1.6107        | 2.61  | 15   | 1.3839          | 0.5309   |
+| No log        | 0.87  | 5    | 1.3767          | 0.5370   |
+| 1.289         | 1.91  | 11   | 1.3503          | 0.5494   |
+| 1.289         | 2.96  | 17   | 1.3712          | 0.5556   |
+| 1.0376        | 4.0   | 23   | 1.3064          | 0.5556   |
+| 1.0376        | 4.87  | 28   | 1.1062          | 0.5802   |
+| 0.8346        | 5.91  | 34   | 0.9249          | 0.6481   |
+| 0.7096        | 6.96  | 40   | 0.8947          | 0.6235   |
+| 0.7096        | 8.0   | 46   | 0.8626          | 0.6543   |
+| 0.6356        | 8.87  | 51   | 0.6820          | 0.7222   |
+| 0.6356        | 9.91  | 57   | 0.7249          | 0.7346   |
+| 0.5956        | 10.96 | 63   | 0.6818          | 0.7407   |
+| 0.5956        | 12.0  | 69   | 0.6111          | 0.7840   |
+| 0.5534        | 12.87 | 74   | 0.6026          | 0.7778   |
+| 0.519         | 13.91 | 80   | 0.6070          | 0.7901   |
+| 0.519         | 14.96 | 86   | 0.5758          | 0.7963   |
+| 0.5117        | 16.0  | 92   | 0.5791          | 0.7840   |
+| 0.5117        | 16.87 | 97   | 0.5711          | 0.8025   |
+| 0.4913        | 17.39 | 100  | 0.5712          | 0.8086   |
 
 
 ### Framework versions