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Jax Models

Jax Models is an open-source Python repository of JAX and Flax implementations of deep-learning research papers, along with models, layers, activations and other research utilities. It aims to provide code for papers that lacked implementations or whose original code used other frameworks.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Jax Models — 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 a project needs research-oriented JAX/Flax reference code, especially for vision architectures, or when you want to inspect and load an available model through Python.

Who it is for

  • Researchers reproducing or exploring deep-learning architectures in JAX and Flax.
  • Developers building JAX-based experiments or computer-vision prototypes.
  • Practitioners seeking reusable model, layer or activation implementations.

Documented uses

  • Browse the repository's available model architectures.
  • Load a named model through the package's Python interface.
  • Reuse listed layers such as DropPath, squeeze-and-excitation and depthwise convolution.
  • Consult cited research papers and code references while adapting an implementation.
Before you choose

Requirements & limits

The repository documents installation through pip or by cloning the public GitHub repository. It is distributed under the Apache 2.0 license.

  • The project targets Python 3 and the latest JAX/Flax versions according to its README; it recommends a virtual environment to reduce version incompatibilities.
  • The README says pretrained model loading requires attach_head=True and num_classes.
  • Papers and derived or translated code are cited in the README or code docstrings, according to the project.

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 package and inspect available models

Install Jax Models in a Python environment and list the architectures exposed by the package.

Before you start

  • Python 3
  • JAX and Flax versions supported by the project
  • Project prerequisites from the repository's requirements.txt

Steps

  1. Install the listed prerequisites with the command below.

    Documented command
    pip install -r requirements.txt

  2. Install the package with the command below.

    Documented command
    pip install jax-models

  3. Run the documented Python snippet importing list_models from jax_models and print its result.

Expected outcome

A printed list of available model architectures.

Guide limitations

  • The README recommends a virtual environment to reduce version incompatibilities.
  • The documented steps list architectures; they do not describe training or inference.
Documentation 1 ↗
Source-based practical guide

Load a documented pretrained model

Use the documented Python call to request the Swin-Tiny 224 model.

Before you start

  • Python 3
  • JAX and Flax
  • Jax Models installed with its prerequisites

Steps

  1. Import load_model from jax_models.

  2. Call load_model('swin-tiny-224', attach_head=True, num_classes=1000, dropout=0.0, pretrained=True).

Expected outcome

The example requests the named model with a classification head and 1,000 classes.

Guide limitations

  • For pretrained models, the README says attach_head=True and num_classes are necessary.
  • The example does not document a subsequent inference or training procedure.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
18 October 2026
Original website title
DarshanDeshpande/jax-models · Official homepage title · retained in its source language
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