---
tags:
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: VIT-food101-image-classifier
  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: 0.933
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# VIT-food101-image-classifier

This model was trained from scratch on the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5661
- Accuracy: 0.933

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1716        | 0.99  | 62   | 1.2149          | 0.896    |
| 0.7758        | 1.99  | 124  | 0.8727          | 0.906    |
| 0.6269        | 2.99  | 186  | 0.6833          | 0.928    |
| 0.5495        | 3.99  | 248  | 0.6041          | 0.931    |
| 0.4973        | 4.99  | 310  | 0.5661          | 0.933    |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2