---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: image_classification
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: food101
      type: food101
      config: default
      split: train[:5000]
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.911
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# image_classification

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: 1.5938
- Accuracy: 0.911

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7307        | 0.99  | 62   | 2.5306          | 0.833    |
| 1.8698        | 2.0   | 125  | 1.7637          | 0.903    |
| 1.5629        | 2.98  | 186  | 1.5856          | 0.915    |


### Framework versions

- Transformers 4.33.1
- Pytorch 1.13.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3