End of training

README.md

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+---
+license: apache-2.0
+base_model: google/vit-base-patch16-224
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: minang_food_classification
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: imagefolder
+      type: imagefolder
+      config: default
+      split: train
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.4388888888888889
+---
+
+<!-- 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. -->
+
+# minang_food_classification
+
+This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.8248
+- Accuracy: 0.4389
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 1e-06
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine_with_restarts
+- num_epochs: 10
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 2.2338        | 1.0   | 45   | 2.1647          | 0.1944   |
+| 2.1439        | 2.0   | 90   | 2.0883          | 0.2389   |
+| 2.0449        | 3.0   | 135  | 2.0265          | 0.2667   |
+| 1.9851        | 4.0   | 180  | 1.9385          | 0.3444   |
+| 1.9242        | 5.0   | 225  | 1.8869          | 0.3944   |
+| 1.8745        | 6.0   | 270  | 1.8432          | 0.4444   |
+| 1.831         | 7.0   | 315  | 1.8211          | 0.4556   |
+| 1.8017        | 8.0   | 360  | 1.8302          | 0.45     |
+| 1.8078        | 9.0   | 405  | 1.8164          | 0.45     |
+| 1.7895        | 10.0  | 450  | 1.7973          | 0.4556   |
+
+
+### Framework versions
+
+- Transformers 4.38.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

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