fusion-bench 0.1.3

A Comprehensive Benchmark of Deep Model Fusion

FusionBench: A Comprehensive Benchmark of Deep Model Fusion

[!WARNING]
This project is still in testing phase as the API may be subject to change. Please report any issues you encounter.

[!TIP]
Documentation is available at tanganke.github.io/fusion_bench/.

Overview

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.

Installation

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

Introduction to Deep Model Fusion

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.

Project Structure

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 with mkdocs serve. The required packages can be installed with pip install -r mkdocs-requirements.txt.
• examples/: example scripts for running some of the experiments.
• tests/: unit tests for the benchmark.

Citation

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 Du, 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}
}