update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- image-classification
+- generated_from_trainer
+datasets:
+- food101
+metrics:
+- accuracy
+model-index:
+- name: vit-base-food101-demo-v5
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: food101
+      type: food101
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.8558811881188119
+---
+
+<!-- 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. -->
+
+# vit-base-food101-demo-v5
+
+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: 0.5434
+- Accuracy: 0.8559
+
+## 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: 0.0002
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 4
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|
+| 1.6283        | 1.0   | 4735  | 0.9875          | 0.7409   |
+| 0.9874        | 2.0   | 9470  | 0.7967          | 0.7894   |
+| 0.7102        | 3.0   | 14205 | 0.6455          | 0.8255   |
+| 0.4917        | 4.0   | 18940 | 0.5502          | 0.8524   |
+
+
+### Framework versions
+
+- Transformers 4.19.2
+- Pytorch 1.11.0+cu113
+- Datasets 2.2.1
+- Tokenizers 0.12.1