https://github.com/tinyvision/imagenet-training-pipeline
https://github.com/tinyvision/imagenet-training-pipeline
Last synced: 10 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/tinyvision/imagenet-training-pipeline
- Owner: tinyvision
- Created: 2022-07-08T08:31:51.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2022-09-27T12:31:30.000Z (over 3 years ago)
- Last Synced: 2025-03-21T18:51:45.644Z (11 months ago)
- Language: Python
- Size: 16.6 KB
- Stars: 4
- Watchers: 0
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## Introduction
This repository is the training pipeline specific for Light-NAS Quantization models, which is easy to use. User needs to move the `nas/models` folder of Light-NAS to `model_zoo` to complete this pipeline.
Light-NAS is a utral fast training-free neural architecture search toolbox. It supports recognition, detection and mix-precision quantization search tasks without GPU requirments. You can find more information about Light-NAS at https://github.com/alibaba/lightweight-neural-architecture-search
## Installation
### Prerequisites
* Linux
* Python 3.6+
* PyTorch 1.4+
* CUDA 10.0+
1. Create a conda virtual environment and activate it.
```shell
conda create -n light-nas python=3.6 -y
conda activate light-nas
```
2. Install torch and torchvision with the following command or [offcial instruction](https://pytorch.org/get-started/locally/).
```shell
pip install torch==1.4.0+cu100 torchvision==0.5.0+cu100 -f https://download.pytorch.org/whl/torch_stable.html
```
if meet `"Not be found for jpeg"`, please install the libjpeg for the system.
```shell
sudo yum install libjpeg # for centos
sudo apt install libjpeg-dev # for ubuntu
```
3. Install other packages with the following command.
```shell
pip install -r requirements.txt
```
***
## Easy to use
* **Train low-precision models**
```shell
cd scripts
sh run_train_base_best_low_aug.sh
```
***
## Results and Models
|Backbone|Param (MB)|BitOps (G)|ImageNet TOP1|Structure|Download|
|:----|:----|:----|:----|:----|:----|
|MBV2-8bit|3.4|19.2|71.90%| -| -|
|MBV2-4bit|2.3|7|68.90%| -|- |
|Mixed19d2G|3.2|18.8|74.80%|[txt](scripts/quant/mixed19d2G.txt)|[model](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/LightNAS/quant/mixed-7d0G/quant_238_70.7660.pth.tar) |
|Mixed7d0G|2.2|6.9|70.80%|[txt](scripts/quant/mixed7d0G.txt) |[model](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/LightNAS/quant/mixed-19d2G/quant_237_74.8180.pth.tar) |
***
## Citation
If you use this toolbox in your research, please cite the paper.
```
@article{qescore,
title = {Entropy-Driven Mixed-Precision Quantization for Deep Network Design on IoT Devices},
author = {Zhenhong Sun and Ce Ge and Junyan Wang and Ming Lin and Hesen Chen and Hao Li and Xiuyu Sun},
booktitle = {Advances in Neural Information Processing Systems},
year = {2022},
}
```
***
## Main Contributors
Hesen Chen, [Zhenhong Sun](https://sites.google.com/view/sunzhenhong).