---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: vit-base-food101-demo-v5
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: food101
      type: food101
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8558811881188119
  - task:
      type: image-classification
      name: Image Classification
    dataset:
      name: food101
      type: food101
      config: default
      split: validation
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7952079207920792
      verified: true
    - name: Precision Macro
      type: precision
      value: 0.8087208389002668
      verified: true
    - name: Precision Micro
      type: precision
      value: 0.7952079207920792
      verified: true
    - name: Precision Weighted
      type: precision
      value: 0.8087208389002665
      verified: true
    - name: Recall Macro
      type: recall
      value: 0.7952079207920792
      verified: true
    - name: Recall Micro
      type: recall
      value: 0.7952079207920792
      verified: true
    - name: Recall Weighted
      type: recall
      value: 0.7952079207920792
      verified: true
    - name: F1 Macro
      type: f1
      value: 0.7971943899991044
      verified: true
    - name: F1 Micro
      type: f1
      value: 0.7952079207920791
      verified: true
    - name: F1 Weighted
      type: f1
      value: 0.7971943899991044
      verified: true
    - name: loss
      type: loss
      value: 0.7573962807655334
      verified: true
---

<!-- 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-food101-demo-v5

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: 0.5434
- Accuracy: 0.8559

## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.6283        | 1.0   | 4735  | 0.9875          | 0.7409   |
| 0.9874        | 2.0   | 9470  | 0.7967          | 0.7894   |
| 0.7102        | 3.0   | 14205 | 0.6455          | 0.8255   |
| 0.4917        | 4.0   | 18940 | 0.5502          | 0.8524   |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1