update model card README.md

README.md

@@ -21,7 +21,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.46823040380047504
+      value: 0.5026722090261283
 ---
 
 <!-- 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/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.6973
-- Accuracy: 0.4682
+- Loss: 1.5983
+- Accuracy: 0.5027
 
 ## Model description
 
@@ -51,7 +51,7 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 5e-05
+- learning_rate: 0.0001
 - train_batch_size: 32
 - eval_batch_size: 32
 - seed: 42
@@ -66,16 +66,16 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| No log        | 0.89  | 6    | 1.7731          | 0.2550   |
-| 1.9105        | 1.89  | 12   | 1.7591          | 0.3409   |
-| 1.9105        | 2.89  | 18   | 1.7453          | 0.3910   |
-| 2.0207        | 3.89  | 24   | 1.7334          | 0.4394   |
-| 1.8655        | 4.89  | 30   | 1.7232          | 0.4388   |
-| 1.8655        | 5.89  | 36   | 1.7149          | 0.4569   |
-| 1.9825        | 6.89  | 42   | 1.7101          | 0.4840   |
-| 1.9825        | 7.89  | 48   | 1.7018          | 0.4736   |
-| 1.9672        | 8.89  | 54   | 1.6976          | 0.4828   |
-| 1.8329        | 9.89  | 60   | 1.6973          | 0.4682   |
+| No log        | 0.89  | 6    | 1.7745          | 0.2838   |
+| 1.9066        | 1.89  | 12   | 1.7447          | 0.3895   |
+| 1.9066        | 2.89  | 18   | 1.7216          | 0.4154   |
+| 1.9875        | 3.89  | 24   | 1.6914          | 0.4311   |
+| 1.8094        | 4.89  | 30   | 1.6659          | 0.4629   |
+| 1.8094        | 5.89  | 36   | 1.6485          | 0.4852   |
+| 1.8906        | 6.89  | 42   | 1.6278          | 0.4869   |
+| 1.8906        | 7.89  | 48   | 1.6098          | 0.5021   |
+| 1.8516        | 8.89  | 54   | 1.6131          | 0.5175   |
+| 1.718         | 9.89  | 60   | 1.5983          | 0.5027   |
 
 
 ### Framework versions