Segformer Pytorch: Generate per-class segmentation maps

Understand the project

Practical overview

Use it when a project needs a PyTorch SegFormer implementation that can be configured and incorporated into an image-segmentation workflow.

Computer-vision developers adding semantic segmentation to image-processing software.
Researchers and practitioners configuring a segmentation model for experiments.

Construct a segmentation model with a chosen number of classes and documented stage settings.
Pass an image tensor to the model and obtain per-class segmentation maps.

Before you choose

The repository is public and specifies the MIT license. The README documents package installation with pip.

The example exposes class count, stage dimensions, attention heads, feedforward expansion, reduction ratios, layer counts, and decoder dimension.
The repository states it is hard-coded for four stages and stage-related keyword sequences should have no more than four entries.
The demonstrated 256 × 256 input produces a 64 × 64 output map for each of four classes.

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

Follow the README's documented installation and example setup for constructing a Segformer model and preparing its sample tensor input.

Install the package with the command below.
Documented command
```
pip install segformer-pytorch
```
Import torch and Segformer from segformer_pytorch.
Construct Segformer with the documented four-stage settings, decoder dimension, and class count.
Prepare a three-channel tensor with shape (1, 3, 256, 256) as in the README example.

The package is installed and the README's model and sample input are prepared for the documented inference call.

The README hard-codes four stages; stage-related keyword sequences should contain no more than four entries.
This guide covers the documented setup and input preparation, not a verified execution.

Evidence and transparency

Source record details

Source availability: Active
Sources checked: 15 October 2026
Original website title: lucidrains/segformer-pytorch · Official homepage title · retained in its source language