This project is still in testing phase as the API may be subject to change. Please report any issues you encounter.
Documentation is available at tanganke.github.io/fusion_bench/.
FusionBench is a benchmark suite designed to evaluate the performance of various deep model fusion techniques. It aims to provide a comprehensive comparison of different methods on a variety of datasets and tasks.
install from PyPI:
pip install fusion-bench
or install the latest version in development from github repository
git clone https://github.com/tanganke/fusion_bench.git
cd fusion_bench
pip install -e . # install the package in editable mode
Deep model fusion is a technique that merges, ensemble, or fuse multiple deep neural networks to obtain a unified model. It can be used to improve the performance and robustness of model or to combine the strengths of different models, such as fuse multiple task-specific models to create a multi-task model. For a more detailed introduction to deep model fusion, you can refer to W. Li, 2023, 'Deep Model Fusion: A Survey'. We also provide a brief overview of deep model fusion in our documentation. In this benchmark, we evaluate the performance of different fusion methods on a variety of datasets and tasks.
The project is structured as follows:
fusion_bench/
: the main package of the benchmark.config/
: configuration files for the benchmark. We use Hydra to manage the configurations.docs/
: documentation for the benchmark. We use mkdocs to generate the documentation. Start the documentation server locally withmkdocs serve
. The required packages can be installed withpip install -r mkdocs-requirements.txt
.examples/
: example scripts for running some of the experiments.tests/
: unit tests for the benchmark.
If you find this benchmark useful, please consider citing our work:
@misc{tangFusionBenchComprehensiveBenchmark2024,
title = {{{FusionBench}}: {{A Comprehensive Benchmark}} of {{Deep Model Fusion}}},
shorttitle = {{{FusionBench}}},
author = {Tang, Anke and Shen, Li and Luo, Yong and Hu, Han and Do, Bo and Tao, Dacheng},
year = {2024},
month = jun,
number = {arXiv:2406.03280},
eprint = {2406.03280},
publisher = {arXiv},
url = {http://arxiv.org/abs/2406.03280},
archiveprefix = {arxiv},
langid = {english},
keywords = {Computer Science - Artificial Intelligence,Computer Science - Computation and Language,Computer Science - Machine Learning}
}