README.md

Abstract

• Instruction

  In this folder, we provide the structure txt and parameters of the model searched by TinyNAS.
  
  

• Use the searching configs for Classification

  sh tools/dist_search.sh configs/MBV2_FLOPs.py

  MBV2_FLOPs.py is the config for searching MBV2-like model within the budget of FLOPs.

  R50_FLOPs.py is the config for searching R50-like model within the budget of FLOPs.

  deepmad_R18_FLOPs.py is the config for searching R18-like model within the budget of FLOPs using DeepMAD.

  deepmad_R34_FLOPs.py is the config for searching R34-like model within the budget of FLOPs using DeepMAD.

  deepmad_R50_FLOPs.py is the config for searching R50-like model within the budget of FLOPs using DeepMAD.

  deepmad_29M_224.py is the config for searching 29M SoTA model with 224 resolution within the budget of FLOPs using DeepMAD.

  deepmad_29M_288.py is the config for searching 29M SoTA model with 288 resolution within the budget of FLOPs using DeepMAD.

  deepmad_50M.py is the config for searching 50M SoTA model within the budget of FLOPs using DeepMAD.

  deepmad_89M.py is the config for searching 89M SoTA model within the budget of FLOPs using DeepMAD.
  
  

• Use searched models in your own training pipeline

  copy tinynas/deploy/cnnnet to your pipeline, then

  from cnnnet import CnnNet
  # for classifictaion
  model = CnnNet(num_classes=classes, 
                  structure_txt=structure_txt,
                  out_indices=(4,),
                  classfication=True)
  
  # if load with pretrained model
  model.init_weights(pretrained=pretrained_pth)

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Results and Models

Backbone	size	Param (M)	FLOPs (G)	Top-1	Structure	Download
R18-like	224	10.8	1.7	78.44	txt	model
R50-like	224	21.3	3.6	80.04	txt	model
R152-like	224	53.5	10.5	81.59	txt	model

Note:

1. These models are trained on ImageNet dataset with 8 NVIDIA V100 GPUs.
2. Use SGD optimizer with momentum 0.9; weight decay 5e-5 for ImageNet; initial learning rate 0.1 with 480 epochs.

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Citation

If you find this toolbox useful, please support us by citing this work as

@inproceedings{cvpr2023deepmad,
	title      = {DeepMAD: Mathematical Architecture Design for Deep Convolutional Neural Network},
	author     = {Xuan Shen, Yaohua Wang, Ming Lin, Dylan Huang, Hao Tang, Xiuyu Sun, Yanzhi Wang},
	booktitle  = {Conference on Computer Vision and Pattern Recognition 2023},
	year       = {2023},
	url        = {https://arxiv.org/abs/2303.02165}
}

@inproceedings{zennas,
	title     = {Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition},
	author    = {Ming Lin and Pichao Wang and Zhenhong Sun and Hesen Chen and Xiuyu Sun and Qi Qian and Hao Li and Rong Jin},
	booktitle = {2021 IEEE/CVF International Conference on Computer Vision},  
	year      = {2021},
}