Add more algorithms

2019-09-28 18:24:47 +10:00
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+# Image Classification based on NAS-Searched Models
+
+This directory contains 10 image classification models.
+Nine of them are automatically searched models from different Neural Architecture Search (NAS) algorithms. The other is the residual network.
+We provide codes and scripts to train these models on both CIFAR-10 and CIFAR-100.
+We use the standard data augmentation, i.e., random crop, random flip, and normalization.
+
+---
+## Table of Contents
+- [Installation](#installation)
+- [Data Preparation](#data-preparation)
+- [Training Models](#training-models)
+- [Project Structure](#project-structure)
+- [Citation](#citation)
+
+
+### Installation
+This project has the following requirements:
+- Python = 3.6
+- PadddlePaddle Fluid >= v0.15.0
+
+
+### Data Preparation
+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.
+Note that the MD5 of CIFAR-10-Python compressed file is `c58f30108f718f92721af3b95e74349a` and the MD5 of CIFAR-100-Python compressed file is `eb9058c3a382ffc7106e4002c42a8d85`.
+Please save the file into `${TORCH_HOME}/cifar.python`.
+After data preparation, there should be two files `${TORCH_HOME}/cifar.python/cifar-10-python.tar.gz` and `${TORCH_HOME}/cifar.python/cifar-100-python.tar.gz`.
+
+
+### Training Models
+
+After setting up the environment and preparing the data, one can train the model. The main function entrance is `train_cifar.py`. We also provide some scripts for easy usage.
+```
+bash ./scripts/base-train.sh 0 cifar-10 ResNet110
+bash ./scripts/train-nas.sh  0 cifar-10 GDAS_V1
+bash ./scripts/train-nas.sh  0 cifar-10 GDAS_V2
+bash ./scripts/train-nas.sh  0 cifar-10  SETN
+bash ./scripts/train-nas.sh  0 cifar-10 NASNet
+bash ./scripts/train-nas.sh  0 cifar-10 ENASNet
+bash ./scripts/train-nas.sh  0 cifar-10 AmoebaNet
+bash ./scripts/train-nas.sh  0 cifar-10 PNASNet
+bash ./scripts/train-nas.sh  0 cifar-100 SETN
+```
+The first argument is the GPU-ID to train your program, the second argument is the dataset name, and the last one is the model name.
+Please use `./scripts/base-train.sh` for ResNet and use `./scripts/train-nas.sh` for NAS-searched models.
+
+
+### Project Structure
+```
+.
+├──train_cifar.py [Training CNN models]
+├──lib [Library for dataset, models, and others]
+│  └──models  
+│     ├──__init__.py [Import useful Classes and Functions in models]  
+│     ├──resnet.py [Define the ResNet models]
+│     ├──operations.py [Define the atomic operation in NAS search space]
+│     ├──genotypes.py [Define the topological structure of different NAS-searched models]
+│     └──nas_net.py [Define the macro structure of NAS models]
+│  └──utils
+│     ├──__init__.py [Import useful Classes and Functions in utils]  
+│     ├──meter.py [Define the AverageMeter class to count the accuracy and loss]
+│     ├──time_utils.py [Define some functions to print date or convert seconds into hours]
+│     └──data_utils.py [Define data augmentation functions and dataset reader for CIFAR]
+└──scripts [Scripts for running]  
+```
+
+
+### Citation
+If you find that this project helps your research, please consider citing these papers:
+```
+@inproceedings{dong2019one,
+  title     = {One-Shot Neural Architecture Search via Self-Evaluated Template Network},
+  author    = {Dong, Xuanyi and Yang, Yi},
+  booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
+  year      = {2019}
+}
+@inproceedings{dong2019search,
+  title     = {Searching for A Robust Neural Architecture in Four GPU Hours},
+  author    = {Dong, Xuanyi and Yang, Yi},
+  booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
+  pages     = {1761--1770},
+  year      = {2019}
+}
+@inproceedings{liu2018darts,
+  title     = {Darts: Differentiable architecture search},
+  author    = {Liu, Hanxiao and Simonyan, Karen and Yang, Yiming},
+  booktitle = {ICLR},
+  year      = {2018}
+}
+@inproceedings{pham2018efficient,
+  title     = {Efficient Neural Architecture Search via Parameter Sharing},
+  author    = {Pham, Hieu and Guan, Melody and Zoph, Barret and Le, Quoc and Dean, Jeff},
+  booktitle = {International Conference on Machine Learning (ICML)},
+  pages     = {4092--4101},
+  year      = {2018}
+}
+@inproceedings{liu2018progressive,
+  title     = {Progressive neural architecture search},
+  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},
+  booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
+  pages     = {19--34},
+  year      = {2018}
+}
+@inproceedings{zoph2018learning,
+  title     = {Learning transferable architectures for scalable image recognition},
+  author    = {Zoph, Barret and Vasudevan, Vijay and Shlens, Jonathon and Le, Quoc V},
+  booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
+  pages     = {8697--8710},
+  year      = {2018}
+}
+@inproceedings{real2019regularized,
+  title     = {Regularized evolution for image classifier architecture search},
+  author    = {Real, Esteban and Aggarwal, Alok and Huang, Yanping and Le, Quoc V},
+  booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
+  pages     = {4780--4789},
+  year      = {2019}
+}
+```