End of training

README.md

@@ -8,7 +8,7 @@ datasets:
 metrics:
 - accuracy
 model-index:
-- name: google/vit-base-patch16-224-in21k-finetuned
+- name: google/vit-base-patch16-224-in21k-v2-finetuned
   results:
   - task:
       name: Image Classification
@@ -19,18 +19,18 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.893
+      value: 0.7968976897689769
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-# google/vit-base-patch16-224-in21k-finetuned
+# google/vit-base-patch16-224-in21k-v2-finetuned
 
 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 food101 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.5688
-- Accuracy: 0.893
+- Loss: 1.0612
+- Accuracy: 0.7969
 
 ## Model description
 
@@ -65,9 +65,9 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 2.6743        | 0.992 | 62   | 2.5147          | 0.83     |
-| 1.8116        | 2.0   | 125  | 1.7366          | 0.863    |
-| 1.5732        | 2.976 | 186  | 1.5688          | 0.893    |
+| 1.9201        | 1.0   | 947  | 1.9632          | 0.7297   |
+| 1.2002        | 2.0   | 1894 | 1.2327          | 0.7805   |
+| 0.9561        | 3.0   | 2841 | 1.0612          | 0.7969   |
 
 
 ### Framework versions