Model save

README.md

@@ -1,6 +1,6 @@
 ---
 license: apache-2.0
-base_model: microsoft/swin-tiny-patch4-window7-224
+base_model: mansee/swin-tiny-patch4-window7-224-blank_img
 tags:
 - generated_from_trainer
 datasets:
@@ -22,7 +22,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.9777131782945736
+      value: 0.9738372093023255
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -30,10 +30,10 @@ should probably proofread and complete it, then remove this comment. -->
 
 # swin-tiny-patch4-window7-224-blank_img
 
-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.
+This model is a fine-tuned version of [mansee/swin-tiny-patch4-window7-224-blank_img](https://huggingface.co/mansee/swin-tiny-patch4-window7-224-blank_img) on the imagefolder dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0748
-- Accuracy: 0.9777
+- Loss: 0.1016
+- Accuracy: 0.9738
 
 ## Model description
 
@@ -61,22 +61,17 @@ 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: 10
+- num_epochs: 5
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 0.0898        | 0.99  | 72   | 0.1245          | 0.9428   |
-| 0.152         | 1.99  | 145  | 0.0811          | 0.9748   |
-| 0.1235        | 3.0   | 218  | 0.0958          | 0.9700   |
-| 0.1065        | 4.0   | 291  | 0.0748          | 0.9777   |
-| 0.1115        | 4.99  | 363  | 0.0947          | 0.9729   |
-| 0.0804        | 5.99  | 436  | 0.0888          | 0.9758   |
-| 0.0722        | 7.0   | 509  | 0.0827          | 0.9758   |
-| 0.061         | 8.0   | 582  | 0.0899          | 0.9758   |
-| 0.0706        | 8.99  | 654  | 0.0916          | 0.9758   |
-| 0.0633        | 9.9   | 720  | 0.0937          | 0.9758   |
+| 0.0502        | 0.99  | 72   | 0.1300          | 0.9651   |
+| 0.1107        | 1.99  | 145  | 0.1023          | 0.9729   |
+| 0.0917        | 3.0   | 218  | 0.1277          | 0.9651   |
+| 0.1022        | 4.0   | 291  | 0.1258          | 0.9719   |
+| 0.0888        | 4.95  | 360  | 0.1016          | 0.9738   |
 
 
 ### Framework versions