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📘Documentation | 🛠️Installation | 🚀Research | 🤔Reporting Issues

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Introduction

YSAutoML is an open-source library for the automated construction and optimization of AI systems, developed by Yonsei CVLab.
It integrates key technologies across data, network architectures, and optimization to build task-specific, efficient deep learning solutions with minimal manual intervention.

The library is designed as part of a multi-year project that aims to:

• Develop a next-generation automated AI platform applicable to image recognition, segmentation, and object detection.
• Provide a unified solution that integrates dataset handling, model search, and efficient training.
• Support users in rapidly generating customized AI models according to their requirements (task, dataset, resource budget, etc.).

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✨ Major Features

• Integrated Automation
  Combines dataset utilities, network architecture search (NAS), and optimization into a single pipeline.

• Data Utilities
  Tools for dataset condensation, augmentation-aware BN (DSBN), and memory-efficient training.

• Network Utilities (NAS)
  Support for few-shot, zero-shot, and one-shot NAS frameworks to automatically discover architectures.

• Optimization Utilities
  Fixed-point quantization, loss function search, and multi-task loss integration for efficient deployment.

• User-Centered Interface
  Accepts user requirements such as task type, dataset, computational budget, and memory constraints, and generates optimized models automatically.

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What's New

💎 Initial release of YSAutoML: a unified library integrating dataset, network, and optimization utilities.

Highlight

• Automated condensation of large datasets into compact synthetic sets.
• DSBN (Dual BatchNorm) support for domain-adaptive training.
• Unified NAS framework supporting multiple search paradigms.
• Built-in support for quantization and multi-task loss training.

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Installation

YSAutoML requires Python 3.8+ and PyTorch 1.8+.

git clone https://github.com/cvlab-yonsei/YonseiAutoML.git
cd YonseiAutoML
pip install -v -e .

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Getting Started

Dataset Condensation with DSA

from ysautoml.data.fyi import run_dsa

run_dsa(
    dataset="CIFAR10",
    model="ConvNet",
    ipc=10,
    dsa_strategy="color_crop_cutout_flip_scale_rotate",
    init="real", lr_img=1.0, num_exp=5, num_eval=5,
    run_name="DSAFYI", run_tags="CIFAR10_10IPC", device="0", eval_mode="M",
)

Domain-Specific BatchNorm (DSBN)

from ysautoml.data.dsbn import convert_and_wrap, train_with_dsbn

model = convert_and_wrap("resnet18_cifar", dataset="CIFAR10", num_classes=10)
result = train_with_dsbn(model, source_loader, target_loader, epochs=5, lr=0.01)
print(result["final_acc"])

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Module Overview

ysautoml
│
├── data
│   ├── fyi      # Dataset condensation (Flip Your Images)
│   ├── dsbn     # Domain-Specific BatchNorm
│
├── network
│   ├── fewshot  # Few-shot NAS
│   ├── zeroshot # Zero-shot NAS
│   ├── oneshot  # One-shot NAS
│
└── optimization
    ├── fxp        # Fixed-point quantization
    ├── losssearch # Loss search
    └── mtlloss    # Multi-task learning loss

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Roadmap

This project is part of a 4-year research initiative.
In the final phase, we focus on integrating and optimizing exploration spaces for data, network, and loss jointly.

• Unified Search Space Optimization: jointly optimize network structures, data augmentation, and objectives.
• Automated Library Development: provide a unified library that can generate AI models from user inputs.
• Efficiency Enhancements: integrate dataset compression and fast optimization techniques to reduce memory and training cost.

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FAQ

Please refer to the documentation for common usage questions.

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Acknowledgement

YSAutoML is developed by researchers at Yonsei CVLab.
We thank all contributors and collaborators who helped design, implement, and test its components.

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Citation

If you use this toolbox or benchmark in your research, please cite:

@misc{ysautoml2025,
  title   = {YSAutoML: Automated AI System Construction Library},
  author  = {Yonsei CVLab},
  year    = {2025},
  note    = {https://github.com/cvlab-yonsei/YonseiAutoML}
}