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Transfer Learning Library

Transfer Learning Library (TLlib) is an open-source Python library built on PyTorch that provides reusable transfer-learning methods, API documentation and example code for domain adaptation, task adaptation, domain generalization and related learning setups.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Transfer Learning Library — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when a Python/PyTorch project needs documented transfer-learning methods or example workflows for adapting models across domains or tasks.

Who it is for

  • Machine-learning developers building transfer-learning workflows in Python and PyTorch.
  • Researchers comparing or extending methods for domain adaptation, fine-tuning, semi-supervised learning or out-of-distribution generalization.

Documented uses

  • Apply an existing domain-adaptation method to a classification task.
  • Develop or extend transfer-learning algorithms using the library's method APIs.
  • Explore example workflows for object detection, segmentation, regression and other supported tasks.
  • Use model-ranking, self-training or data-reweighting methods in a transfer-learning experiment.
Before you choose

Requirements & limits

The repository identifies the project as open source under the MIT license. Source installation instructions are provided; pip installation is described as experimental.

  • The library is based on PyTorch and describes an API design consistent with torchvision.
  • Examples are organized by learning setup, including domain adaptation, task adaptation, out-of-distribution generalization, semi-supervised learning and model selection.
  • The README's DANN example uses Office-31 with Amazon as the source domain and Webcam as the target, and names ResNet-50 as the model.
  • The project does not host or grant licenses for datasets used by its utility code; users are responsible for checking dataset permissions.

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 TLlib from source

Install the library and its listed dependencies from the repository.

Before you start

  • Python environment
  • Git access to the repository

Steps

  1. Clone the Transfer Learning Library repository.

  2. Run the command below.

    Documented command
    python setup.py install

  3. Run the command below.

    Documented command
    pip install -r requirements.txt

Expected outcome

The repository's installation instructions install tllib and the dependencies listed in its requirements file.

Guide limitations

  • The repository does not specify a Python version in the supplied installation instructions.
  • The README labels pip installation experimental; this recipe uses the source-install path.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
15 October 2026
Original website title
thuml/Transfer-Learning-Library · Official homepage title · retained in its source language
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