Swin Transformer Pytorch: configure a vision model in PyTorch

Understand the project

Practical overview

Use it when you want a Python/PyTorch implementation to adapt or study the shifted-window Swin architecture as part of a computer-vision workflow.

PyTorch developers building image-classification models.
Computer-vision researchers and engineers adapting a transformer backbone for detection or segmentation pipelines.
Practitioners experimenting with attention heads, stage depths, window sizes and embedding settings.

Instantiate a configurable Swin Transformer and produce class logits from an image tensor.
Use the architecture as a starting point for computer-vision backbone work.
Compare model configurations such as window size, stage depth and attention-head counts.

Before you choose

The public GitHub repository is MIT-licensed. The README documents installation via pip or the cloned repository requirements file.

The model exposes four-stage settings for layer counts and attention heads, along with input channels, class count, window size and downscaling factors.
The README requires each configured layer count to be divisible by two.
Image dimensions need to remain divisible by the window size after each downscaling step and large enough for the configured downscaling factors.
The README marks adjustment and validation on ImageNet-1K and COCO 2017 as TODO items.
The repository states that it is not the official Swin Transformer repository.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Prepare the documented Python package or cloned repository dependencies before importing the model.

For package installation, run the command below.
Documented command
```
pip install swin-transformer-pytorch
```
For a cloned repository, run the command below.
Documented command
```
pip install -r requirements.txt
```

The README documents the installation route needed before following its Python import example.

The README does not specify Python or PyTorch version requirements.
Installation guidance alone does not establish that a particular environment is compatible.

Evidence and transparency

Source record details

Source availability: Active
Sources checked: 16 October 2026
Original website title: berniwal/swin-transformer-pytorch · Official homepage title · retained in its source language