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توضیحات

A Library for Deep Domain Adaptation

ویژگی	مقدار
سیستم عامل	-
نام فایل	dalib-0.2
نام	dalib
نسخه کتابخانه	0.2
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	THUML
ایمیل نویسنده	JiangJunguang1123@outlook.com
آدرس صفحه اصلی	https://github.com/thuml/Domain-Adaptation-Lib
آدرس اینترنتی	https://pypi.org/project/dalib/
مجوز	-

## Introduction *Trans-Learn* is a Transfer Learning library 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 easily apply existing algorithms.. On July 24th, 2020, we released the v0.1 (preview version), the first sub-library is for Domain Adaptation (DALIB). The supported algorithms currently include: - [Domain-Adversarial Training of Neural Networks (DANN)](https://arxiv.org/abs/1505.07818) - [Deep Adaptation Networks (DAN)](https://arxiv.org/abs/1502.02791) - [Joint Adaptation Networks (JAN)](https://arxiv.org/abs/1605.06636) - [Conditional Adversarial Domain Adaptation (CDAN)](https://arxiv.org/abs/1705.10667) - [Maximum Classifier Discrepancy (MCD)](https://arxiv.org/abs/1712.02560) - [Margin Disparity Discrepancy (MDD)](https://arxiv.org/abs/1904.05801) ## Installation DALIB is currently hosted on [PyPI](https://pypi.org/project/dalib/). It requires Python >= 3.6. You can simply install dalib with the following command: ```bash pip install dalib ``` You can also install with the newest version through GitHub: ```bash pip install git+https://github.com/thuml/Transfer-Learning-Library.git@master ``` After installation, open your python console and type the following. If no error occurs, you have successfully installed DALIB. ```python import dalib print(dalib.__version__) ``` For flexible use and modification, git clone the library is also a good choice. ## Documentation You can find the tutorial and API documentation on the website: [DALIB API](https://dalib.readthedocs.io/en/latest/index.html) Also, we have examples in the directory `examples`. A typical usage is ```shell script # 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 examples/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 - Junguang Jiang (JiangJunguang1123@outlook.com) - Bo Fu (fb1121@vip.qq.com) - Mingsheng Long (longmingsheng@gmail.com) or describe it in Issues. ## Citation If you use this toolbox or benchmark in your research, please cite this project. ```latex @misc{dalib, author = {Junguang Jiang, Bo Fu, Mingsheng Long}, title = {Transfer-Learning-library}, year = {2020}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/thuml/Transfer-Learning-Library}}, } ```

نیازمندی

مقدار	نام
>=1.4.0	torch
>=0.5.0	torchvision
-	numpy
>=1.4.0	qpsolvers
-	Sphinx
-	sphinx-rtd-theme

زبان مورد نیاز

مقدار	نام
>=3.6	Python

نحوه نصب

نصب پکیج whl dalib-0.2:

pip install dalib-0.2.whl

نصب پکیج tar.gz dalib-0.2:

pip install dalib-0.2.tar.gz