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

Shufflenet

Shufflenet is a Python and PyTorch implementation of the ShuffleNet convolutional neural network, using residual ShuffleUnits and pointwise group convolutions for image-classification models.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Shufflenet — 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 it when you want a code-level starting point for a PyTorch classification model based on ShuffleNet, especially when the example's configurable class and input-channel counts suit your task.

Who it is for

  • Computer-vision developers seeking a PyTorch CNN implementation to adapt for classification.
  • Learners and researchers examining residual blocks and pointwise group convolutions.

Documented uses

  • Instantiate a classification model with a chosen number of classes and input channels.
  • Inspect or adapt a ShuffleNet implementation for a computer-vision project.
  • Review the repository's reported ImageNet training results and pretrained state-dictionary reference.
Before you choose

Requirements & limits

The repository is public on GitHub and carries an MIT license.

  • The README includes a concrete MNIST construction example with 10 classes and one input channel.
  • Reported ImageNet scores are for a specific groups=3, no-channel-multiplier setup and are explicitly not directly comparable with the paper's settings.
  • The README points to a pretrained state dictionary, but does not document a complete training or deployment process.

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

Clone the repository and instantiate the MNIST example

Set up the documented repository and construct its example model.

Before you start

  • Git
  • Python
  • PyTorch

Steps

  1. Clone the repository with the command below.

    Documented command
    git clone https://github.com/jaxony/ShuffleNet.git

  2. Import ShuffleNet from model.py.

  3. Construct ShuffleNet(num_classes=10, in_channels=1) as shown for MNIST.

Expected outcome

A ShuffleNet model instance configured for 10 classes and one input channel.

Guide limitations

  • The README's example documents model construction, not a complete training, inference, or deployment workflow.
  • The repository's reported ImageNet results use training settings different from those described in the paper.
Documentation 1 ↗
Evidence and transparency

Sources & review

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