---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-food101-16-7
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: food101
      type: food101
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9080792079207921
---

<!-- 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-patch16-224-food101-16-7

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3293
- Accuracy: 0.9081

## 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: 7

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9326        | 1.0   | 1183 | 0.5737          | 0.8566   |
| 0.6632        | 2.0   | 2367 | 0.4265          | 0.884    |
| 0.4608        | 3.0   | 3551 | 0.3747          | 0.8958   |
| 0.5356        | 4.0   | 4735 | 0.3557          | 0.8992   |
| 0.483         | 5.0   | 5918 | 0.3431          | 0.9044   |
| 0.3975        | 6.0   | 7102 | 0.3343          | 0.9071   |
| 0.3716        | 7.0   | 8281 | 0.3293          | 0.9081   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1