CDARTS

Cyclic Differentiable Architecture Search

This is an official implementation of CDARTS

In this work, we propose new joint optimization objectives and a novel Cyclic Differentiable ARchiTecture Search framework, dubbed CDARTS. Considering the structure difference, CDARTS builds a cyclic feedback mechanism between the search and evaluation networks with introspective distillation. First, the search network generates an initial architecture for evaluation, and the weights of the evaluation network are optimized. Second, the architecture weights in the search network are further optimized by the label supervision in classification, as well as the regularization from the evaluation network through feature distillation. Repeating the above cycle results in a joint optimization of the search and evaluation networks and thus enables the evolution of the architecture to fit the final evaluation network.

Model Zoo

For evaluation, we provide the checkpoints and configs of our models in Google Drive.

After downloading the models, you can do the evaluation following the description in SETUP.md.

Model download links:

DARTS Search Space

CIFAR10

Top-1 Acc. %	97.60	97.45	97.52	97.53	97.54	97.77
Cell Download link	Cell-1	Cell-2	Cell-3	Cell-4	Cell-5	Cell-6

ImageNet

Top-1 Acc. %	75.90	75.93	76.40	76.60	76.44
Cell Download link	Cell-1	Cell-2	Cell-3	Cell-4	Cell-5

NATS-Bench

Model	CIFAR10 Validation	CIFAR10 Test	CIFAR100 Validation	CIFAR100 Test	ImageNet-16-120 Validation	ImageNet-16-120 Test	Download link
Cell-1	91.50%	94.37%	73.31%	73.09%	45.59%	46.33%	Cell, Log
Cell-2	91.37%	94.09%	72.64%	72.57%	45.46%	45.63%	Cell, Log
Cell-3	90.51%	93.62%	70.43	70.10%	44.23%	44.57%	Cell, Log

Chain-structured Search Space

Model	Params.	Flops	Top-1 Acc. %	Download link
CDARTS-a	7.0M	294M	77.4	Model, Config, Log
CDARTS-b	6.4M	394M	78.2	Model, Config, Log

Object Detection

Backbone	Input Size	Params.	Flops	AP	AP_50	AP_75	AP_S	AP_M	AP_L	Download link
CDARTS-a	1280x800	6.0G	7.0M	35.2	55.5	37.5	19.8	38.7	47.5	Model, Config, Log
CDARTS-b	1280x800	8.1G	6.4M	36.2	56.7	38.3	20.9	39.8	48.5	Model, Config, Log

Semantic Segmentation

Dataset	Encoder	Input Size	Params.	Flops	mIoU %	Download link
Cityscapes	CDARTS-b	1024x2048	5.9M	20.7G	78.1	Model, Config, Log
ADE20K	CDARTS-b	640x640	2.7M	5.9G	40.4	Model, Config, Log

Bibtex

If this repo is helpful for you, please consider to cite it. Thank you! :)

@article{CDARTS,
  title={Cyclic Differentiable Architecture Search},
  author={Yu, Hongyuan and Peng, Houwen and Huang, Yan and Fu, Jianlong and Du, Hao and Wang, Liang and Ling, Haibin},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
  year={2022}
}