Transfer learning is an essential technique in machine learning, allowing us to utilize pre-trained models on new tasks. TLlib is an open-source library designed to facilitate transfer learning using PyTorch. This article provides a step-by-step guide to get you started with TLlib, ensuring a smooth integration into your machine learning projects.
Table of Contents
Introduction
TLlib is a powerful library offering a user-friendly API based on pure PyTorch, making it easier to develop and apply transfer learning algorithms. Its design is consistent with torchvision, so that developers can quickly adapt existing algorithms or create new ones.
Updates
On 2024-03-15, an offline version of the documentation was uploaded. You can download it here and unzip it for local access.
Please note that on 2023-08-09, a notice was issued about broken dataset links. You can find details in DATASETS.md.
Supported Methods
TLlib includes several transfer learning methods organized under key categories:
- Domain Alignment: tllib.alignment
- Domain Translation: tllib.translation
- Self-Training: tllib.self_training
- Regularization: tllib.regularization
- Data Reweighting: tllib.reweight
- Model Ranking: tllib.ranking
- Normalization: tllib.normalization
Installation
To install TLlib, choose one of the following methods:
1. Install from Source Code
Clone the library and run the following commands:
git clone https://github.com/thuml/Transfer-Learning-Library.git
cd Transfer-Learning-Library
python setup.py install
pip install -r requirements.txt2. Install via Pip
This method is currently experimental:
pip install -i https://test.pypi.org/simple tllib==0.4Documentation
The complete API documentation can be accessed on the official website: Documentation.
How to Use TLlib
Here is a general analogy to understand how to use TLlib in your projects: Imagine TLlib as a toolbox for building furniture. Just like you can use many tools in different combinations to create a beautiful piece of furniture, TLlib allows you to apply different learning algorithms to effectively construct your model.
To train an algorithm (like a table with a specific design), you simply run:
python dann.py data/office31 -d Office31 -s A -t W -a resnet50 --epochs 20Troubleshooting
If you encounter any issues with TLlib, here are a few troubleshooting tips:
- Ensure you’ve installed all required packages listed in requirements.txt.
- Check your Python version; TLlib may have compatibility requirements.
- If datasets fail to download, verify your internet connection or consult the DATASETS.md for alternative links.
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Contact
If you have further questions or suggestions, feel free to reach out to:
- Baixu Chen (cbx_99_hasta@outlook.com)
- Junguang Jiang (JiangJunguang1123@outlook.com)
- Mingsheng Long (longmingsheng@gmail.com)
Citation
If you use this toolbox in your research, please cite the project as indicated in the README.