update model card README.md

README.md

@@ -6,6 +6,9 @@ datasets:
 - imagefolder
 metrics:
 - accuracy
+- f1
+- recall
+- precision
 model-index:
 - name: swin-tiny-patch4-window7-224-finetuned-brainTumorData
   results:
@@ -21,7 +24,16 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.9735294117647059
+      value: 0.9911764705882353
+    - name: F1
+      type: f1
+      value: 0.9923273657289001
+    - name: Recall
+      type: recall
+      value: 0.9948717948717949
+    - name: Precision
+      type: precision
+      value: 0.9897959183673469
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -31,8 +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: 0.0764
-- Accuracy: 0.9735
+- Loss: 0.0484
+- Accuracy: 0.9912
+- F1: 0.9923
+- Recall: 0.9949
+- Precision: 0.9898
 
 ## Model description
 
@@ -64,12 +79,12 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 0.4994        | 1.0   | 24   | 0.2253          | 0.9059   |
-| 0.2449        | 2.0   | 48   | 0.0806          | 0.9735   |
-| 0.1921        | 3.0   | 72   | 0.0640          | 0.9765   |
-| 0.1755        | 4.0   | 96   | 0.0764          | 0.9735   |
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
+| 0.3405        | 1.0   | 24   | 0.1137          | 0.9647   | 0.9688 | 0.9538 | 0.9841    |
+| 0.2449        | 2.0   | 48   | 0.0811          | 0.9735   | 0.9766 | 0.9641 | 0.9895    |
+| 0.1713        | 3.0   | 72   | 0.0613          | 0.9794   | 0.9818 | 0.9692 | 0.9947    |
+| 0.1615        | 4.0   | 96   | 0.0484          | 0.9912   | 0.9923 | 0.9949 | 0.9898    |
 
 
 ### Framework versions