Model save

README.md

@@ -14,14 +14,13 @@ model-index:
 <!-- 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. -->
 
-[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/jalitv77-ml/lora_transformers/runs/q00oyrrm)
-[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/jalitv77-ml/lora_transformers/runs/q00oyrrm)
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/jalitv77-ml/lora_transformers/runs/62dlprwk)
 # vit-base-patch16-224-in21k-finetuned-lora-food101
 
 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.5198
-- Accuracy: 0.8565
+- Loss: 0.5267
+- Accuracy: 0.8573
 
 ## Model description
 
@@ -55,11 +54,11 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch  | Step | Validation Loss | Accuracy |
 |:-------------:|:------:|:----:|:---------------:|:--------:|
-| 0.8397        | 0.9981 | 133  | 0.6682          | 0.8154   |
-| 0.8471        | 1.9962 | 266  | 0.5984          | 0.8366   |
-| 0.6114        | 2.9944 | 399  | 0.5590          | 0.8438   |
-| 0.6202        | 4.0    | 533  | 0.5335          | 0.8532   |
-| 0.4775        | 4.9906 | 665  | 0.5198          | 0.8565   |
+| 1.0269        | 0.9958 | 118  | 0.6697          | 0.8207   |
+| 0.9299        | 2.0    | 237  | 0.5993          | 0.8387   |
+| 0.6699        | 2.9958 | 355  | 0.5610          | 0.8473   |
+| 0.6616        | 4.0    | 474  | 0.5421          | 0.8543   |
+| 0.5378        | 4.9789 | 590  | 0.5267          | 0.8573   |
 
 
 ### Framework versions