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tinyml

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- - eval.sh
- - default.yaml
  - netaug.yaml
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  - fig2.png
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  - fig4.png
  - fig5.png
  - fig6.png
- - __init__.py
- - __init__.py
  - criterion.py
  - distributed.py
  - init.py
  - lr_scheduler.py
  - metric.py
  - misc.py
  - profile.py
- .gitignore
- eval.py
- README.md
- setup.py
- train.py
- - make_aircraft.py
  - make_all_datasets.sh
  - make_cub200.py
  - make_flowers102.py
  - make_food.py
  - make_pets.py
  - make_stanford_cars.py
- - __init__.py
- README.md
- tinytl_fgvc_train.py
.gitignore
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mcunet
once-for-all
README.md

# Installation Guide

1. Get the code

git clone https://github.com/mit-han-lab/tinyml

Downloads the entire project code from GitHub to your computer.

cd tinyml

Moves into the project folder you just downloaded.

2. Python

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip install .

Installs the package published on PyPI directly — no need to clone the source.

python <실행할 파일명>.py # README에서 정확한 실행 파일명을 확인하세요

Runs the Python script (or module).

✅ If it runs without errors and prints output in the terminal, it worked.

// repository documentation

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# Tiny Machine Learning [[website]](https://tinyml.mit.edu)

**[News]** We refactored MCUNet into a standalone repo: https://github.com/mit-han-lab/mcunet. Please follow the new repo for updates on TinyEngine release!

**[News]** We actively collaborate with industrial partners for real-world TinyML applications. Our technolgy has successfully influenced many products and deployed on over 100K IoT devices. Feel free to contact Prof. Song Han for more info.

**[News]** Our projects are covered by: 
[MIT News](https://news.mit.edu/2020/iot-deep-learning-1113), 
[WIRED](https://www.wired.com/story/ai-algorithms-slimming-fit-fridge/), 
[Morning Brew](https://www.morningbrew.com/emerging-tech/stories/2020/12/07/researchers-figured-fit-ai-ever-onto-internet-things-microchips), 
[Stacey on IoT](https://staceyoniot.com/researchers-take-a-3-pronged-approach-to-edge-ai/), 
[Analytics Insight](https://www.analyticsinsight.net/amalgamating-ml-and-iot-in-smart-home-devices/), 
[Techable](https://techable.jp/archives/142462).

## TinyML Projects
| Projects                | Keywords | 
|----------------------|:----------:|
| [MCUNet](https://github.com/mit-han-lab/mcunet) | Memory-efficient inference, System-algorithm co-design |
| [TinyTL](https://github.com/mit-han-lab/tinyml/tree/master/tinytl) | On-device learning, Memory-efficient transfer learning |
| [NetAug](https://github.com/mit-han-lab/tinyml/tree/master/netaug) | Training technique for tiny neural networks |

## About TinyML
Intelligent edge devices with rich sensors (e.g., billions of mobile phones and IoT devices) have been ubiquitous 
in our daily lives. Combining artificial intelligence (AI) and these edge devices, 
there are vast real-world applications such as smart home, smart retail, autonomous driving, 
and so on. However, the state-of-the-art deep learning AI systems typically require tremendous 
resources (e.g., large labeled dataset, many computational resources, many AI experts), 
both for training and inference. This hinders the application of these powerful deep learning 
AI systems on edge devices. The [TinyML project](https://tinyml.mit.edu) aims to improve the efficiency of deep learning 
AI systems by requiring less computation, fewer engineers, and less data, 
to facilitate the giant market of edge AI and AIoT.

<p align="center">
    <img src="https://hanlab.mit.edu/projects/tinyml/figures/background1.png" width="100%" />
</p>
<p align="center">
    <img src="https://hanlab.mit.edu/projects/tinyml/figures/background2.png" width="100%" />
</p>

## Demo
[![Watch the video](https://hanlab.mit.edu/projects/tinyml/figures/mcunet_demo.png)](https://youtu.be/YvioBgtec4U)

## Related Projects

[MCUNet: Tiny Deep Learning on IoT Devices](https://arxiv.org/abs/2007.10319) (NeurIPS'20, spotlight)

[TinyTL: Reduce Memory, Not Parameters for Efficient On-Device Learning](https://arxiv.org/abs/2007.11622) (NeurIPS'20)

[Once for All: Train One Network and Specialize it for Efficient Deployment](https://arxiv.org/abs/1908.09791) (ICLR'20)

[ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware](https://arxiv.org/pdf/1812.00332.pdf) (ICLR'19)

[AutoML for Architecting Efficient and Specialized Neural Networks](https://ieeexplore.ieee.org/abstract/document/8897011) (IEEE Micro)

[AMC: AutoML for Model Compression and Acceleration on Mobile Devices](https://arxiv.org/pdf/1802.03494.pdf) (ECCV'18)

[HAQ: Hardware-Aware Automated Quantization](https://arxiv.org/pdf/1811.08886.pdf)  (CVPR'19, oral)