update model card README.md

README.md

@@ -7,6 +7,9 @@ datasets:
 - imagefolder
 metrics:
 - accuracy
+- f1
+- precision
+- recall
 model-index:
 - name: segformer-class-classWeights-augmentation
   results:
@@ -22,7 +25,16 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 1.0
+      value: 0.9655172413793104
+    - name: F1
+      type: f1
+      value: 0.964683592269799
+    - name: Precision
+      type: precision
+      value: 0.9674329501915708
+    - name: Recall
+      type: recall
+      value: 0.9655172413793104
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,8 +44,11 @@ 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: 0.0018
-- Accuracy: 1.0
+- Loss: 0.1453
+- Accuracy: 0.9655
+- F1: 0.9647
+- Precision: 0.9674
+- Recall: 0.9655
 
 ## Model description
 
@@ -61,30 +76,21 @@ 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: 20
+- num_epochs: 10
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|
-| No log        | 0.89  | 6    | 0.9811          | 0.5862   |
-| 1.0899        | 1.93  | 13   | 0.9601          | 0.5862   |
-| 0.7599        | 2.96  | 20   | 0.8930          | 0.6897   |
-| 0.7599        | 4.0   | 27   | 0.5230          | 0.7931   |
-| 0.3436        | 4.89  | 33   | 0.1652          | 0.9310   |
-| 0.1853        | 5.93  | 40   | 0.0544          | 1.0      |
-| 0.1853        | 6.96  | 47   | 0.0937          | 0.9655   |
-| 0.1839        | 8.0   | 54   | 0.0566          | 0.9655   |
-| 0.1772        | 8.89  | 60   | 0.0084          | 1.0      |
-| 0.1772        | 9.93  | 67   | 0.0114          | 1.0      |
-| 0.1016        | 10.96 | 74   | 0.0039          | 1.0      |
-| 0.1534        | 12.0  | 81   | 0.0038          | 1.0      |
-| 0.1534        | 12.89 | 87   | 0.0187          | 0.9655   |
-| 0.1256        | 13.93 | 94   | 0.0023          | 1.0      |
-| 0.1234        | 14.96 | 101  | 0.0016          | 1.0      |
-| 0.1234        | 16.0  | 108  | 0.0019          | 1.0      |
-| 0.1129        | 16.89 | 114  | 0.0018          | 1.0      |
-| 0.17          | 17.78 | 120  | 0.0018          | 1.0      |
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| No log        | 0.89  | 6    | 0.0454          | 1.0      | 1.0    | 1.0       | 1.0    |
+| 0.1558        | 1.93  | 13   | 0.0816          | 0.9655   | 0.9647 | 0.9674    | 0.9655 |
+| 0.1727        | 2.96  | 20   | 0.0775          | 0.9655   | 0.9647 | 0.9674    | 0.9655 |
+| 0.1727        | 4.0   | 27   | 0.0443          | 0.9655   | 0.9647 | 0.9674    | 0.9655 |
+| 0.1299        | 4.89  | 33   | 0.0535          | 0.9655   | 0.9647 | 0.9674    | 0.9655 |
+| 0.1808        | 5.93  | 40   | 0.0298          | 0.9655   | 0.9647 | 0.9674    | 0.9655 |
+| 0.1808        | 6.96  | 47   | 0.0195          | 1.0      | 1.0    | 1.0       | 1.0    |
+| 0.1406        | 8.0   | 54   | 0.0526          | 0.9655   | 0.9647 | 0.9674    | 0.9655 |
+| 0.1193        | 8.89  | 60   | 0.1453          | 0.9655   | 0.9647 | 0.9674    | 0.9655 |
 
 
 ### Framework versions