Welcome to the world of JAX Models, where researchers and developers unite to harness the power of JAX and Flax for deep learning! This blog serves as your guide to understanding how to work with JAX Models effectively.
Table of Contents
About The Project
The JAX Models repository aims to provide open source JAX/Flax implementations for research papers that originally lacked code or were developed using different frameworks. It creates a collection of models, layers, activations, and other utilities commonly used in research. Each implementation and its references are well-cited in the README or the code’s docstrings. If you find any missing citations, please raise an issue.
Available model implementations include:
- MetaFormer is Actually What You Need for Vision (Weihao Yu et al., 2021)
- Augmenting Convolutional networks with attention-based aggregation (Hugo Touvron et al., 2021)
- MPViT: Multi-Path Vision Transformer for Dense Prediction (Youngwan Lee et al., 2021)
- MLP-Mixer: An all-MLP Architecture for Vision (Ilya Tolstikhin et al., 2021)
- Patches Are All You Need (Anonymous et al., 2021)
- SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers (Enze Xie et al., 2021)
- A ConvNet for the 2020s (Zhuang Liu et al., 2021)
- Masked Autoencoders Are Scalable Vision Learners (Kaiming He et al., 2021)
- Swin Transformer: Hierarchical Vision Transformer using Shifted Windows (Ze Liu et al., 2021)
- Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions (Wenhai Wang et al., 2021)
- Going deeper with Image Transformers (Hugo Touvron et al., 2021)
- Visual Attention Network (Meng-Hao Guo et al., 2022)
Available layers for seamless integration include:
- DropPath (Stochastic Depth) (Gao Huang et al., 2021)
- Squeeze-and-Excitation Layer (Jie Hu et al., 2019)
- Depthwise Convolution (François Chollet, 2017)
Prerequisites
Before diving into the installation, make sure you have the necessary prerequisites installed. You can do this by running:
sh
pip install -r requirements.txt
Using a virtual environment is highly recommended to avoid version incompatibilities.
Installation
Installing JAX Models is a breeze! It’s built with Python 3 and the latest JAX/Flax versions, which can be directly installed via pip:
sh
pip install jax-models
If you prefer the latest version, you can also clone the repository:
sh
git clone https://github.com/DarshanDeshpande/jax-models.git
Usage
Now, let’s unleash the power of JAX Models! To see all the available model architectures, use the following code:
py
from jax_models import list_models
from pprint import pprint
pprint(list_models())
To load your desired model, you can use this code snippet:
py
from jax_models import load_model
load_model(swin-tiny-224, attach_head=True, num_classes=1000, dropout=0.0, pretrained=True)
Remember, passing attach_head=True and num_classes is essential while loading pretrained models.
Contributing
Your input is highly valuable! Please raise an issue if you encounter incorrect results, crashes during training or inference, or if you believe there’s a missing citation. You’re also welcome to contribute by providing compute resources or donating pretrained weights. If you’re interested in supporting this initiative, feel free to reach out via email!
License
This project is distributed under the Apache 2.0 License. For further information, refer to the LICENSE file.
Contact
If you have issues or requests related to these implementations, feel free to reach out to:
Troubleshooting Ideas
If you encounter issues during installation or usage, consider the following tips:
- Ensure that all prerequisites are correctly installed.
- Verify your Python and pip versions are compatible with JAX Models.
- If you experience module import issues, double-check your installation paths.
- In case of unexpected crashes or incorrect results, consult the documentation for specific models or raise an issue for assistance.
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