Skip to content
master
Switch branches/tags
Code

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
Oct 16, 2021
Oct 27, 2021
Oct 26, 2021
Oct 27, 2021
Oct 27, 2021
Oct 26, 2021
Oct 27, 2021
Oct 19, 2021

Introduction

Transfer-Learn is an open-source and well-documented library for Transfer Learning. It is based on pure PyTorch with high performance and friendly API. Our code is pythonic, and the design is consistent with torchvision. You can easily develop new algorithms, or readily apply existing algorithms.

The currently supported algorithms include:

Domain Adaptation for Classification
Partial Domain Adaptation
Open-set Domain Adaptation
Domain Adaptation for Semantic Segmentation
Domain Adaptation for Keypoint Detection
Domain Adaptation for Person Re-identification
Task Adaptation for Image Classification
Domain Generalization for Classification
Domain Generalization for Person Re-identification

We are planning to add

  • DA for Object Detection
  • TA for text classification

Installation

To use dalib, talib, dglib, and common in other places, you need to install Transfer-Learn,

python setup.py install

Note that we do not support pip install currently.

For flexible use and modification of Transfer-Learn, please git clone the library.

Documentation

You can find the tutorial and API documentation on the website: Documentation (please open in Firefox or Safari). Note that this link is only for temporary use. You can also build the doc by yourself following the instructions in http://170.106.108.162/get_started/faq.html.

Also, we have examples in the directory examples. A typical usage is

# Train a DANN on Office-31 Amazon -> Webcam task using ResNet 50.
# Assume you have put the datasets under the path `data/office-31`, 
# or you are glad to download the datasets automatically from the Internet to this path
python dann.py data/office31 -d Office31 -s A -t W -a resnet50  --epochs 20

In the directory examples, you can find all the necessary running scripts to reproduce the benchmarks with specified hyper-parameters.

Contributing

We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us.

Disclaimer on Datasets

This is a utility library that downloads and prepares public datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have licenses to use the dataset. It is your responsibility to determine whether you have permission to use the dataset under the dataset's license.

If you're a dataset owner and wish to update any part of it (description, citation, etc.), or do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thanks for your contribution to the ML community!

Contact

If you have any problem with our code or have some suggestions, including the future feature, feel free to contact

or describe it in Issues.

For Q&A in Chinese, you can choose to ask questions here before sending an email. 迁移学习算法库答疑专区

Citation

If you use this toolbox or benchmark in your research, please cite this project.

@misc{dalib,
  author = {Junguang Jiang, Baixu Chen, Bo Fu, Mingsheng Long},
  title = {Transfer-Learning-library},
  year = {2020},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/thuml/Transfer-Learning-Library}},
}

Acknowledgment

We would like to thank School of Software, Tsinghua University and The National Engineering Laboratory for Big Data Software for providing such an excellent ML research platform.