---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-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: 0.9068514851485149
---

<!-- 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-in21k-food101-24-12

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: 0.3533
- Accuracy: 0.9069

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7927        | 1.0   | 789  | 2.5629          | 0.7693   |
| 1.256         | 2.0   | 1578 | 0.9637          | 0.8583   |
| 0.94          | 3.0   | 2367 | 0.5866          | 0.8816   |
| 0.6693        | 4.0   | 3157 | 0.4752          | 0.8888   |
| 0.6337        | 5.0   | 3946 | 0.4282          | 0.8941   |
| 0.5811        | 6.0   | 4735 | 0.4110          | 0.8949   |
| 0.4661        | 7.0   | 5524 | 0.3875          | 0.8990   |
| 0.4188        | 8.0   | 6314 | 0.3776          | 0.9010   |
| 0.5045        | 9.0   | 7103 | 0.3633          | 0.9049   |
| 0.3437        | 10.0  | 7892 | 0.3611          | 0.9058   |
| 0.3494        | 11.0  | 8681 | 0.3568          | 0.9060   |
| 0.3381        | 12.0  | 9468 | 0.3533          | 0.9069   |


### Framework versions

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