Model save

README.md

@@ -7,6 +7,8 @@ datasets:
 - imagefolder
 metrics:
 - accuracy
+- precision
+- recall
 model-index:
 - name: swin-tiny-patch4-window7-224
   results:
@@ -22,7 +24,13 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.7366666666666667
+      value: 0.7466666666666667
+    - name: Precision
+      type: precision
+      value: 0.7167033192888707
+    - name: Recall
+      type: recall
+      value: 0.7466666666666667
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,8 +40,11 @@ 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: 0.5300
-- Accuracy: 0.7367
+- Loss: 0.6199
+- Accuracy: 0.7467
+- Precision: 0.7167
+- Recall: 0.7467
+- F1 Score: 0.7110
 
 ## Model description
 
@@ -65,15 +76,15 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|
-| No log        | 1.0   | 8    | 0.5816          | 0.7333   |
-| 0.6478        | 2.0   | 16   | 0.5634          | 0.7333   |
-| 0.5746        | 3.0   | 24   | 0.5526          | 0.7375   |
-| 0.5414        | 4.0   | 32   | 0.6044          | 0.7333   |
-| 0.5159        | 5.0   | 40   | 0.5310          | 0.7542   |
-| 0.5159        | 6.0   | 48   | 0.5481          | 0.7583   |
-| 0.4901        | 7.0   | 56   | 0.5410          | 0.7583   |
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
+| No log        | 1.0   | 8    | 0.5333          | 0.7583   | 0.7348    | 0.7583 | 0.7122   |
+| 0.4735        | 2.0   | 16   | 0.5572          | 0.7708   | 0.7878    | 0.7708 | 0.7094   |
+| 0.4492        | 3.0   | 24   | 0.5569          | 0.775    | 0.7830    | 0.775  | 0.7210   |
+| 0.3847        | 4.0   | 32   | 0.6170          | 0.7625   | 0.7640    | 0.7625 | 0.6989   |
+| 0.337         | 5.0   | 40   | 0.5983          | 0.7625   | 0.7417    | 0.7625 | 0.7189   |
+| 0.337         | 6.0   | 48   | 0.5763          | 0.7708   | 0.7507    | 0.7708 | 0.7435   |
+| 0.3118        | 7.0   | 56   | 0.6043          | 0.7792   | 0.7661    | 0.7792 | 0.7448   |
 
 
 ### Framework versions