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Agileformer

Agileformer is a Python research implementation of a spatially agile Transformer U-Net for medical-image segmentation, with code for 2D segmentation and reported 3D multi-organ segmentation on Synapse.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Agileformer — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Agileformer when you need a research codebase and documented configurations for medical-image segmentation experiments, especially evaluation with pretrained Synapse models.

Who it is for

  • Medical-imaging researchers evaluating segmentation models on documented datasets.
  • Developers adapting a Transformer U-Net implementation for medical-image labeling experiments.

Documented uses

  • Evaluate pretrained segmentation models on Synapse using repository configurations.
  • Explore segmentation configurations for Synapse, ACDC cardiac imaging and Decathlon brain-tumor tasks.
  • Adapt or retrain the implementation to investigate spatially dynamic segmentation components.
Before you choose

Requirements & limits

The public GitHub repository specifies an Apache-2.0 license.

  • The README lists pretrained Synapse checkpoints in tiny and base sizes, with configurations and download links.
  • The documented environment recommends Python 3.8 or later and PyTorch 2.1 or later with CUDA 12.1 or later for package compatibility.
  • NATTEN and deformable-convolution dependencies can involve version-specific installation; the README describes tvdcn, mmcv and PyTorch implementation options.
  • The repository says its deformable-convolution implementation changes require retraining.
  • The README reports results for specified dataset and model setups; these are research results, not a guarantee of performance on other data.

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 an environment and evaluate a pretrained model

Set up the documented prerequisites and start evaluation using a pretrained configuration.

Before you start

  • Python 3.8 or later is recommended.
  • PyTorch 2.1 or later and CUDA 12.1 or later are recommended for package compatibility.
  • Install repository dependencies with the requirements file; the README notes that NATTEN and deformable convolution may need separate, version-matched installation.
  • Prepare the chosen dataset and pretrained weights.

Steps

  1. Install the repository dependencies with the command below.

    Documented command
    pip install -r requirements.txt

  2. Prepare the dataset and place the pretrained weights in the repository directory specified for the evaluation workflow.

  3. Choose a pretrained configuration from the configs directory.

  4. Run the command below, substituting the chosen configuration path.

    Documented command
    python test.py --cfg [pretrained_config_file in configs]

Expected outcome

Starts the repository’s documented pretrained-model evaluation workflow for the selected configuration.

Guide limitations

  • The recommended dependency versions are compatibility guidance, not a guarantee that a particular environment will work.
  • Using a different deformable-convolution implementation may require retraining, according to the README.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
16 October 2026
Original website title
sotiraslab/AgileFormer · Official homepage title · retained in its source language
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