This repository provides the evaluation code of our paper: Blockwisely Supervised Neural Architecture Search with Knowledge Distillation.

• Our searched models have been trained from scratch and can be found in: https://drive.google.com/drive/folders/1Oqc2gq8YysrJq2i6RmPMLKqheGfB9fWH.

• Here is a summary of our searched models:

  Model	FLOPs	Params	Acc@1	Acc@5
  DNA-a	348M	4.2M	77.1%	93.3%
  DNA-b	394M	4.9M	77.5%	93.3%
  DNA-c	466M	5.3M	77.8%	93.7%
  DNA-d	611M	6.4M	78.4%	94.0%

• Install PyTorch (pytorch.org)
• Install third-party requirements
  • pip install timm==0.1.14 We use this Pytorch-Image-Models codebase to validate our models.
• Download the ImageNet dataset and move validation images to labeled subfolders
  • To do this, you can use the following script: https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.shvalprep.sh
  • Only the validation set is needed in the evaluation process.

The Training Module is simplified from the repo: pytorch-image-models

• Modify the run_example.sh: change data path and hyper-params according to your requirements
• ./run_example.sh

• You can evaluate our models with the following command:
  python validate.py PATH/TO/ImageNet/validation --model DNA_a --checkpoint PATH/TO/model.pth.tar
  • PATH/TO/ImageNet/validation should be replaced by your validation data path.
  • --model : DNA_a can be replaced by DNA_b, DNA_c, DNA_d for our different models.
  • --checkpoint : Suggest the path of your downloaded checkpoint here.

Searching code will be released in the future.