---
library_name: keras-hub
---
### Model Overview
Data-efficient Image Transformer (DeiT).

**Reference**

- [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877)

ViT models required training on expensive infrastructure for multiple weeks, using external data. DeiT (data-efficient image transformers) are more efficiently trained transformers for image classification, requiring far less data and far less computing resources compared to the original ViT models.

## Links

* [DeiT Quickstart Notebook] - coming soon
* [DeiT API Documentation] - coming soon
* [DeiT Beginner Guide] - coming soon
* [KerasHub Model Publishing Guide](https://keras.io/guides/keras_hub/upload/)


## Installation

Keras and KerasHub can be installed with:

```
pip install -U -q keras-hub
pip install -U -q keras
```

Jax, TensorFlow, and Torch come preinstalled in Kaggle Notebooks. For instructions on installing them in another environment see the [Keras Getting Started](https://keras.io/getting_started/) page.

## Presets

The following model checkpoints are provided by the Keras team. Weights have been ported from: https://huggingface.co. Full code examples for each are available below.

| Preset name                        | Parameters | Description                                                                                                                                                                                                                                                                                               |
|------------------------------------|------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| deit_tiny_distilled_patch16_224_imagenet | 5.52M      | DeiT-T16 model pre-trained on the ImageNet 1k dataset with image resolution of 224x224 |
| deit_small_distilled_patch16_224_imagenet | 21.66M      | DeiT-S16 model pre-trained on the ImageNet 1k dataset with image resolution of 224x224 |
| deit_base_distilled_patch16_224_imagenet | 85.80M      | DeiT-B16 model pre-trained on the ImageNet 1k dataset with image resolution of 224x224 . |
| deit_base_distilled_patch16_384_imagenet | 86.09M     | DeiT-B16 model pre-trained on the ImageNet 1k dataset with image resolution of 384x384 |