update model card README.md

README.md

@@ -24,16 +24,16 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.9613120269133726
+      value: 0.9646761984861227
     - name: F1
       type: f1
-      value: 0.9613120269133726
+      value: 0.9646761984861227
     - name: Recall
       type: recall
-      value: 0.9613120269133726
+      value: 0.9646761984861227
     - name: Precision
       type: precision
-      value: 0.9613120269133726
+      value: 0.9646761984861227
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -43,11 +43,11 @@ 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: 0.1093
-- Accuracy: 0.9613
-- F1: 0.9613
-- Recall: 0.9613
-- Precision: 0.9613
+- Loss: 0.1012
+- Accuracy: 0.9647
+- F1: 0.9647
+- Recall: 0.9647
+- Precision: 0.9647
 
 ## Model description
 
@@ -75,16 +75,17 @@ 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: 4
+- num_epochs: 5
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
-| 0.4369        | 0.99  | 83   | 0.2500          | 0.9092   | 0.9092 | 0.9092 | 0.9092    |
-| 0.3777        | 1.99  | 166  | 0.1763          | 0.9302   | 0.9302 | 0.9302 | 0.9302    |
-| 0.2684        | 2.99  | 249  | 0.1215          | 0.9512   | 0.9512 | 0.9512 | 0.9512    |
-| 0.2363        | 3.99  | 332  | 0.1093          | 0.9613   | 0.9613 | 0.9613 | 0.9613    |
+| 0.4856        | 0.99  | 83   | 0.3771          | 0.8444   | 0.8444 | 0.8444 | 0.8444    |
+| 0.3495        | 1.99  | 166  | 0.2608          | 0.8949   | 0.8949 | 0.8949 | 0.8949    |
+| 0.252         | 2.99  | 249  | 0.1445          | 0.9487   | 0.9487 | 0.9487 | 0.9487    |
+| 0.2364        | 3.99  | 332  | 0.1029          | 0.9588   | 0.9588 | 0.9588 | 0.9588    |
+| 0.2178        | 4.99  | 415  | 0.1012          | 0.9647   | 0.9647 | 0.9647 | 0.9647    |
 
 
 ### Framework versions