120 lines
5.5 KiB
Markdown
120 lines
5.5 KiB
Markdown
# Image Classification based on NAS-Searched Models
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This directory contains 10 image classification models.
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Nine of them are automatically searched models using different Neural Architecture Search (NAS) algorithms, and the other is the residual network.
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We provide codes and scripts to train these models on both CIFAR-10 and CIFAR-100.
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We use the standard data augmentation, i.e., random crop, random flip, and normalization.
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---
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## Table of Contents
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- [Installation](#installation)
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- [Data Preparation](#data-preparation)
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- [Training Models](#training-models)
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- [Project Structure](#project-structure)
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- [Citation](#citation)
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### Installation
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This project has the following requirements:
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- Python = 3.6
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- PadddlePaddle Fluid >= v0.15.0
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- numpy, tarfile, cPickle, PIL
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### Data Preparation
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Please download [CIFAR-10](https://dataset.bj.bcebos.com/cifar/cifar-10-python.tar.gz) and [CIFAR-100](https://dataset.bj.bcebos.com/cifar/cifar-100-python.tar.gz) before running the codes.
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Note that the MD5 of CIFAR-10-Python compressed file is `c58f30108f718f92721af3b95e74349a` and the MD5 of CIFAR-100-Python compressed file is `eb9058c3a382ffc7106e4002c42a8d85`.
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Please save the file into `${TORCH_HOME}/cifar.python`.
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After data preparation, there should be two files `${TORCH_HOME}/cifar.python/cifar-10-python.tar.gz` and `${TORCH_HOME}/cifar.python/cifar-100-python.tar.gz`.
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### Training Models
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After setting up the environment and preparing the data, you can train the model. The main function entrance is `train_cifar.py`. We also provide some scripts for easy usage.
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```
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bash ./scripts/base-train.sh 0 cifar-10 ResNet110
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bash ./scripts/train-nas.sh 0 cifar-10 GDAS_V1
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bash ./scripts/train-nas.sh 0 cifar-10 GDAS_V2
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bash ./scripts/train-nas.sh 0 cifar-10 SETN
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bash ./scripts/train-nas.sh 0 cifar-10 NASNet
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bash ./scripts/train-nas.sh 0 cifar-10 ENASNet
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bash ./scripts/train-nas.sh 0 cifar-10 AmoebaNet
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bash ./scripts/train-nas.sh 0 cifar-10 PNASNet
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bash ./scripts/train-nas.sh 0 cifar-100 SETN
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```
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The first argument is the GPU-ID to train your program, the second argument is the dataset name (`cifar-10` or `cifar-100`), and the last one is the model name.
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Please use `./scripts/base-train.sh` for ResNet and use `./scripts/train-nas.sh` for NAS-searched models.
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### Project Structure
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```
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.
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├──train_cifar.py [Training CNN models]
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├──lib [Library for dataset, models, and others]
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│ └──models
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│ ├──__init__.py [Import useful Classes and Functions in models]
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│ ├──resnet.py [Define the ResNet models]
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│ ├──operations.py [Define the atomic operation in NAS search space]
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│ ├──genotypes.py [Define the topological structure of different NAS-searched models]
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│ └──nas_net.py [Define the macro structure of NAS models]
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│ └──utils
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│ ├──__init__.py [Import useful Classes and Functions in utils]
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│ ├──meter.py [Define the AverageMeter class to count the accuracy and loss]
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│ ├──time_utils.py [Define some functions to print date or convert seconds into hours]
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│ └──data_utils.py [Define data augmentation functions and dataset reader for CIFAR]
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└──scripts [Scripts for running]
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```
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### Citation
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If you find that this project helps your research, please consider citing these papers:
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```
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@inproceedings{dong2019one,
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title = {One-Shot Neural Architecture Search via Self-Evaluated Template Network},
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author = {Dong, Xuanyi and Yang, Yi},
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booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
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year = {2019}
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}
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@inproceedings{dong2019search,
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title = {Searching for A Robust Neural Architecture in Four GPU Hours},
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author = {Dong, Xuanyi and Yang, Yi},
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booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
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pages = {1761--1770},
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year = {2019}
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}
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@inproceedings{liu2018darts,
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title = {Darts: Differentiable architecture search},
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author = {Liu, Hanxiao and Simonyan, Karen and Yang, Yiming},
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booktitle = {International Conference on Learning Representations (ICLR)},
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year = {2018}
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}
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@inproceedings{pham2018efficient,
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title = {Efficient Neural Architecture Search via Parameter Sharing},
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author = {Pham, Hieu and Guan, Melody and Zoph, Barret and Le, Quoc and Dean, Jeff},
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booktitle = {International Conference on Machine Learning (ICML)},
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pages = {4092--4101},
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year = {2018}
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}
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@inproceedings{liu2018progressive,
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title = {Progressive neural architecture search},
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author = {Liu, Chenxi and Zoph, Barret and Neumann, Maxim and Shlens, Jonathon and Hua, Wei and Li, Li-Jia and Fei-Fei, Li and Yuille, Alan and Huang, Jonathan and Murphy, Kevin},
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booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
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pages = {19--34},
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year = {2018}
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}
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@inproceedings{zoph2018learning,
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title = {Learning transferable architectures for scalable image recognition},
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author = {Zoph, Barret and Vasudevan, Vijay and Shlens, Jonathon and Le, Quoc V},
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booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
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pages = {8697--8710},
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year = {2018}
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}
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@inproceedings{real2019regularized,
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title = {Regularized evolution for image classifier architecture search},
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author = {Real, Esteban and Aggarwal, Alok and Huang, Yanping and Le, Quoc V},
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booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)},
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pages = {4780--4789},
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year = {2019}
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}
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```
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