update model card README.md

README.md

@@ -15,13 +15,13 @@ model-index:
     dataset:
       name: imagefolder
       type: imagefolder
-      config: Augmented
+      config: Augmented-Final
       split: train
-      args: Augmented
+      args: Augmented-Final
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.9659090909090909
+      value: 0.9722507708119219
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -31,8 +31,8 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft](https://huggingface.co/microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft) on the imagefolder dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1199
-- Accuracy: 0.9659
+- Loss: 0.0966
+- Accuracy: 0.9723
 
 ## Model description
 
@@ -60,18 +60,22 @@ 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: 0.5
-- num_epochs: 6
+- num_epochs: 10
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 0.4562        | 1.0   | 55   | 0.3207          | 0.8898   |
-| 0.372         | 2.0   | 110  | 0.2667          | 0.9068   |
-| 0.2776        | 3.0   | 165  | 0.2862          | 0.9125   |
-| 0.2018        | 4.0   | 220  | 0.1966          | 0.9398   |
-| 0.2751        | 5.0   | 275  | 0.1937          | 0.9375   |
-| 0.2764        | 6.0   | 330  | 0.1199          | 0.9659   |
+| 1.9035        | 1.0   | 61   | 1.8946          | 0.2713   |
+| 1.4731        | 2.0   | 122  | 1.2931          | 0.5560   |
+| 0.9549        | 3.0   | 183  | 0.7530          | 0.6999   |
+| 0.7375        | 4.0   | 244  | 0.4989          | 0.8129   |
+| 0.615         | 5.0   | 305  | 0.3545          | 0.8746   |
+| 0.4751        | 6.0   | 366  | 0.2399          | 0.9168   |
+| 0.3778        | 7.0   | 427  | 0.1628          | 0.9558   |
+| 0.3054        | 8.0   | 488  | 0.1202          | 0.9620   |
+| 0.2787        | 9.0   | 549  | 0.0988          | 0.9733   |
+| 0.253         | 10.0  | 610  | 0.0966          | 0.9723   |
 
 
 ### Framework versions