Mobilevitv2 1.0 Voc Deeplabv3

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

A MobileViTv2 model pre - trained on PASCAL VOC at 512x512 resolution for semantic segmentation.

🚀 Quick Start

This is a MobileViTv2 model pre - trained on PASCAL VOC at a resolution of 512x512. It was introduced in Separable Self - attention for Mobile Vision Transformers by Sachin Mehta and Mohammad Rastegari, and first released in this repository under the Apple sample code 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.

✨ Features

• Model Structure: MobileViTv2 is constructed by replacing the multi - headed self - attention in MobileViT with separable self - attention.
• Semantic Segmentation: The model in this repo adds a DeepLabV3 head to the MobileViT backbone for semantic segmentation.

💻 Usage Examples

Basic Usage

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.

📚 Documentation

Intended uses & limitations

You can use the raw model for semantic segmentation. See the model hub to look for fine - tuned versions on a task that interests you.

📦 Installation

No installation steps are provided in the original document, so this section is skipped.

🔧 Technical Details

The MobileViT + DeepLabV3 model was pretrained on ImageNet - 1k, a dataset consisting of 1 million images and 1,000 classes, and then fine - tuned on the PASCAL VOC2012 dataset.

📄 License

The license used is Apple sample code license.

BibTeX entry and citation info

@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}
}