---
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-food101-24-12
  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: 0.9087524752475248
---

<!-- 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-food101-24-12

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: 0.3328
- Accuracy: 0.9088

## 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: 24
- eval_batch_size: 24
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 12

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1313        | 1.0   | 789  | 0.7486          | 0.8388   |
| 0.735         | 2.0   | 1578 | 0.4546          | 0.8795   |
| 0.7166        | 3.0   | 2367 | 0.3896          | 0.8942   |
| 0.5318        | 4.0   | 3157 | 0.3739          | 0.8961   |
| 0.5326        | 5.0   | 3946 | 0.3576          | 0.9013   |
| 0.4753        | 6.0   | 4735 | 0.3557          | 0.9006   |
| 0.3764        | 7.0   | 5524 | 0.3486          | 0.904    |
| 0.3399        | 8.0   | 6314 | 0.3457          | 0.9046   |
| 0.3987        | 9.0   | 7103 | 0.3378          | 0.9065   |
| 0.2592        | 10.0  | 7892 | 0.3393          | 0.9070   |
| 0.2661        | 11.0  | 8681 | 0.3366          | 0.9080   |
| 0.2632        | 12.0  | 9468 | 0.3328          | 0.9088   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1