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Use HF papers (#38184)

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2025-06-13 13:07:09 +02:00
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docs/source/en/model_doc/mobilevit.md
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## Overview
The MobileViT model was proposed in [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) by Sachin Mehta and Mohammad Rastegari. MobileViT introduces a new layer that replaces local processing in convolutions with global processing using transformers.
The MobileViT model was proposed in [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://huggingface.co/papers/2110.02178) by Sachin Mehta and Mohammad Rastegari. MobileViT introduces a new layer that replaces local processing in convolutions with global processing using transformers.
The abstract from the paper is the following:
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- MobileViT is more like a CNN than a Transformer model. It does not work on sequence data but on batches of images. Unlike ViT, there are no embeddings. The backbone model outputs a feature map. You can follow [this tutorial](https://keras.io/examples/vision/mobilevit) for a lightweight introduction.
- One can use [`MobileViTImageProcessor`] to prepare images for the model. Note that if you do your own preprocessing, the pretrained checkpoints expect images to be in BGR pixel order (not RGB).
- The available image classification checkpoints are pre-trained on [ImageNet-1k](https://huggingface.co/datasets/imagenet-1k) (also referred to as ILSVRC 2012, a collection of 1.3 million images and 1,000 classes).
- The segmentation model uses a [DeepLabV3](https://arxiv.org/abs/1706.05587) head. The available semantic segmentation checkpoints are pre-trained on [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/).
- The segmentation model uses a [DeepLabV3](https://huggingface.co/papers/1706.05587) head. The available semantic segmentation checkpoints are pre-trained on [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/).
- As the name suggests MobileViT was designed to be performant and efficient on mobile phones. The TensorFlow versions of the MobileViT models are fully compatible with [TensorFlow Lite](https://www.tensorflow.org/lite).
You can use the following code to convert a MobileViT checkpoint (be it image classification or semantic segmentation) to generate a