update model card README.md

README.md

@@ -1,7 +1,6 @@
 ---
 license: apache-2.0
 tags:
-- image-classification
 - generated_from_trainer
 metrics:
 - accuracy
@@ -15,10 +14,10 @@ should probably proofread and complete it, then remove this comment. -->
 
 # vit-base-clothing-leafs-example-full-simple
 
-This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
+This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.9822
-- Accuracy: 0.7145
+- Loss: nan
+- Accuracy: 0.0512
 
 ## Model description
 
@@ -37,7 +36,7 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 1.75e-05
+- learning_rate: 0.002
 - train_batch_size: 32
 - eval_batch_size: 8
 - seed: 42
@@ -48,27 +47,15 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
-|:-------------:|:-----:|:-----:|:---------------:|:--------:|
-| 2.1712        | 0.14  | 1000  | 1.6429          | 0.6206   |
-| 1.4661        | 0.28  | 2000  | 1.3387          | 0.6625   |
-| 1.2803        | 0.41  | 3000  | 1.2176          | 0.6767   |
-| 1.2063        | 0.55  | 4000  | 1.1589          | 0.6827   |
-| 1.1377        | 0.69  | 5000  | 1.0993          | 0.6957   |
-| 1.1039        | 0.83  | 6000  | 1.0647          | 0.7012   |
-| 1.0691        | 0.97  | 7000  | 1.0476          | 0.7037   |
-| 0.9729        | 1.11  | 8000  | 1.0341          | 0.7060   |
-| 0.9621        | 1.24  | 9000  | 1.0242          | 0.7090   |
-| 0.9344        | 1.38  | 10000 | 1.0159          | 0.7104   |
-| 0.9364        | 1.52  | 11000 | 1.0165          | 0.7083   |
-| 0.9235        | 1.66  | 12000 | 1.0139          | 0.7089   |
-| 0.9264        | 1.8   | 13000 | 1.0029          | 0.7109   |
-| 0.9161        | 1.94  | 14000 | 0.9822          | 0.7145   |
-| 0.8473        | 2.07  | 15000 | 1.0041          | 0.7135   |
-| 0.7759        | 2.21  | 16000 | 1.0011          | 0.7137   |
-| 0.7743        | 2.35  | 17000 | 1.0025          | 0.7119   |
-| 0.7699        | 2.49  | 18000 | 0.9976          | 0.7148   |
-| 0.7691        | 2.63  | 19000 | 1.0039          | 0.7146   |
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 2.7222        | 0.14  | 1000 | 2.6766          | 0.2528   |
+| 2.6768        | 0.28  | 2000 | 2.6693          | 0.2481   |
+| 2.6689        | 0.41  | 3000 | nan             | 0.2230   |
+| 0.4613        | 0.55  | 4000 | nan             | 0.0512   |
+| 0.0           | 0.69  | 5000 | nan             | 0.0512   |
+| 0.0           | 0.83  | 6000 | nan             | 0.0512   |
+| 0.0           | 0.97  | 7000 | nan             | 0.0512   |
 
 
 ### Framework versions