OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
AI Models

Resnest

ResNeSt is an open-source deep-learning model project centered on split-attention networks, a ResNet variant intended for computer-vision systems. It provides pretrained model-loading examples for PyTorch and MXNet Gluon, plus resources for training and using ResNeSt backbones in detection, instance-segmentation and semantic-segmentation workflows.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Resnest — 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 ResNeSt when you need a split-attention backbone to evaluate or integrate into a computer-vision research or development workflow, especially where the documented PyTorch, Gluon, Detectron2 or segmentation resources apply.

Who it is for

  • Computer-vision researchers comparing neural-network backbones.
  • Developers building image-recognition systems who want to explore split-attention models.
  • Teams adapting backbones for detection or segmentation with the supported framework resources.

Documented uses

  • Load a pretrained ResNeSt model through PyTorch Hub or the Python package.
  • Load a pretrained Gluon model using the documented Gluon interface.
  • Explore ResNeSt as a backbone for image recognition, object detection, instance segmentation or semantic segmentation.
  • Find training and verification resources for ImageNet and downstream vision tasks.
Before you choose

Requirements & limits

The repository is public and identifies the project as open source under the Apache-2.0 license. The README documents installation through GitHub or the PyPI prerelease channel.

  • The README provides pretrained-model loading examples for PyTorch and Gluon, but its training paths vary by task and framework.
  • Detectron2 training configurations and pretrained models are referenced in the repository's d2 resources; semantic-segmentation training is directed to separate toolkits.
  • The README lists pretrained model variants and crop sizes, but those listed results should not be treated as a guarantee for a particular application.

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

Install the ResNeSt package

Install ResNeSt from its GitHub repository or the PyPI prerelease channel.

Before you start

  • Python environment with pip.

Steps

  1. Choose one installation route: run the command below or the command below.

    Documented command
    pip install git+https://github.com/zhanghang1989/ResNeSt
    Documented command
    pip install resnest --pre

  2. Continue to the repository's model-loading examples to select a framework and model.

Expected outcome

The package installation command is documented; model loading is the next documented step.

Guide limitations

  • The README does not specify Python or framework version requirements in the cited installation instructions.
  • Installation alone does not load a model or run inference.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
14 October 2026
Original website title
zhanghang1989/ResNeSt · Official homepage title · retained in its source language
How we review sourcesSuggest a correction