---
license: apache-2.0
datasets:
- food101
language:
- en
metrics:
- accuracy
pipeline_tag: image-classification
---
# Model Card for Model ID
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the food101 dataset. It achieves the following results on the evaluation set:
<!-- Provide a quick summary of what the model is/does. -->
<ul>Evaluation loss: 0.7166455984115601</ul>
<ul>Accuracy: 0.8753663366336634</ul>
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).

## Model Details

### Model Description

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### Model Sources [optional]

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## Uses

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### Direct Use

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

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#### Speeds, Sizes, Times [optional]
TrainOutput(global_step=4735, training_loss=1.7299224627936907, metrics={'train_runtime': 3538.2995, 'train_samples_per_second': 21.409, 'train_steps_per_second': 1.338, 'total_flos': 5.8752267138432e+18, 'train_loss': 1.7299224627936907, 'epoch': 1.0})
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## Evaluation

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eval_loss: 0.7166455984115601
eval_accuracy: 0.8753663366336634, 'eval_runtime': 446.9362, 'eval_samples_per_second': 56.496, 'eval_steps_per_second': 3.533, 'epoch': 1.0

### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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## Technical Specifications [optional]

### Model Architecture and Objective

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## Glossary [optional]

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