---
license: other
library_name: transformers
tags:
- vision
- image-segmentation
---

# MobileViTv2 + DeepLabv3 (shehan97/mobilevitv2-1.0-voc-deeplabv3)

<!-- Provide a quick summary of what the model is/does. -->
MobileViTv2 model pre-trained on PASCAL VOC at resolution 512x512.
It was introduced in [Separable Self-attention for Mobile Vision Transformers](https://arxiv.org/abs/2206.02680) by Sachin Mehta and Mohammad Rastegari, and first released in [this](https://github.com/apple/ml-cvnets) repository. The license used is [Apple sample code license](https://github.com/apple/ml-cvnets/blob/main/LICENSE).

Disclaimer: The team releasing MobileViT did not write a model card for this model so this model card has been written by the Hugging Face team.

### Model Description

<!-- Provide a longer summary of what this model is. -->
MobileViTv2 is constructed by replacing the multi-headed self-attention in MobileViT with separable self-attention.

The model in this repo adds a [DeepLabV3](https://arxiv.org/abs/1706.05587) head to the MobileViT backbone for semantic segmentation.

### Intended uses & limitations

You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?search=mobilevitv2) to look for fine-tuned versions on a task that interests you.

### How to use

Here is how to use this model:

```python
from transformers import MobileViTv2FeatureExtractor, MobileViTv2ForSemanticSegmentation
from PIL import Image
import requests

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

feature_extractor = MobileViTv2FeatureExtractor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")
model = MobileViTv2ForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")

inputs = feature_extractor(images=image, return_tensors="pt")

outputs = model(**inputs)
logits = outputs.logits

predicted_mask = logits.argmax(1).squeeze(0)
```

Currently, both the feature extractor and model support PyTorch.

## Training data

The MobileViT + DeepLabV3 model was pretrained on [ImageNet-1k](https://huggingface.co/datasets/imagenet-1k), a dataset consisting of 1 million images and 1,000 classes, and then fine-tuned on the [PASCAL VOC2012](http://host.robots.ox.ac.uk/pascal/VOC/) dataset.

### BibTeX entry and citation info

```bibtex
@inproceedings{vision-transformer,
title = {Separable Self-attention for Mobile Vision Transformers},
author = {Sachin Mehta and Mohammad Rastegari},
year = {2022},
URL = {https://arxiv.org/abs/2206.02680}
}
```