---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: my_awesome_food_model
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: food101
      type: food101
      config: default
      split: train[:20200]
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8853960396039604
---

<!-- 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. -->

# my_awesome_food_model

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: 0.4703
- Accuracy: 0.8854

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.4019        | 1.0   | 1010  | 1.3796          | 0.8156   |
| 0.6238        | 2.0   | 2020  | 0.6604          | 0.8448   |
| 0.3691        | 3.0   | 3030  | 0.5661          | 0.8522   |
| 0.3947        | 4.0   | 4040  | 0.5226          | 0.8614   |
| 0.3511        | 5.0   | 5050  | 0.5125          | 0.8644   |
| 0.2504        | 6.0   | 6060  | 0.5180          | 0.8656   |
| 0.1285        | 7.0   | 7070  | 0.5312          | 0.8668   |
| 0.2301        | 8.0   | 8080  | 0.4779          | 0.875    |
| 0.0844        | 9.0   | 9090  | 0.4823          | 0.8839   |
| 0.1189        | 10.0  | 10100 | 0.4703          | 0.8854   |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3