{"id":13751853,"url":"https://github.com/zhaohui-yang/CARS","last_synced_at":"2025-05-09T18:32:38.862Z","repository":{"id":87397428,"uuid":"207943524","full_name":"zhaohui-yang/CARS","owner":"zhaohui-yang","description":"[CVPR2020] CARS: Continuous Evolution for Efficient Neural Architecture Search","archived":false,"fork":false,"pushed_at":"2020-06-30T05:39:42.000Z","size":1193,"stargazers_count":104,"open_issues_count":4,"forks_count":20,"subscribers_count":10,"default_branch":"master","last_synced_at":"2024-11-03T02:02:13.842Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/zhaohui-yang.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2019-09-12T02:02:33.000Z","updated_at":"2024-10-24T13:46:00.000Z","dependencies_parsed_at":"2023-05-07T11:30:34.136Z","dependency_job_id":null,"html_url":"https://github.com/zhaohui-yang/CARS","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zhaohui-yang%2FCARS","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zhaohui-yang%2FCARS/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zhaohui-yang%2FCARS/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zhaohui-yang%2FCARS/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/zhaohui-yang","download_url":"https://codeload.github.com/zhaohui-yang/CARS/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":224876976,"owners_count":17384699,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-08-03T09:00:55.947Z","updated_at":"2024-11-16T04:31:50.318Z","avatar_url":"https://github.com/zhaohui-yang.png","language":null,"funding_links":[],"categories":["神经网络结构搜索 Neural Architecture Search"],"sub_categories":[],"readme":"# CARS\n\n[1] Zhaohui Yang, Yunhe Wang, Xinghao Chen, Boxin Shi, Chao Xu, Chunjing Xu, Qi Tian, and Chang Xu. [CARS: Continuous Evolution for Efficient Neural Architecture Search](https://openaccess.thecvf.com/content_CVPR_2020/papers/Yang_CARS_Continuous_Evolution_for_Efficient_Neural_Architecture_Search_CVPR_2020_paper.pdf). CVPR 2020. [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Yang_CARS_Continuous_Evolution_for_Efficient_Neural_Architecture_Search_CVPR_2020_paper.pdf)] [[code](https://github.com/huawei-noah/vega)]\n\n\n\n## Introduction\n\nThis repository contains the supplementary material of CARS. All the searching and training codes have been embedded in Huawei AutoML pipeline and will be released together.\n\n\n## Results and Architectures\n\nCARS searches on the CIFAR-10 dataset, and evaluate on CIFAR-10 and ImageNet datasets. Multi-objectives are taken into consideration during searching.\n\n### Objectives: Acc + Params\n\nResults on the CIFAR-10 dataset.\n\n\u003ctable border=\"0\"\u003e\n\u003ctr\u003e\n    \u003ctd\u003e\n    \u003cimg src=\"./figs/results/cars_cifar.png\" width=\"100%\" /\u003e\n    \u003c/td\u003e\n\u003c/tr\u003e\n\u003c/table\u003e\n\nResults on the ImageNet dataset.\n\n\u003ctable border=\"0\"\u003e\n\u003ctr\u003e\n    \u003ctd\u003e\n    \u003cimg src=\"./figs/results/cars_imagenet.png\" width=\"100%\" /\u003e\n    \u003c/td\u003e\n\u003c/tr\u003e\n\u003c/table\u003e\n\n```python\n\nCARS_A = Genotype(normal=[('skip_connect', 0), ('sep_conv_5x5', 1), ('max_pool_3x3', 0), ('avg_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 1), ('sep_conv_3x3', 0), ('dil_conv_5x5', 2)], normal_concat=range(2, 6), reduce=[('avg_pool_3x3', 0), ('max_pool_3x3', 1), ('max_pool_3x3', 0), ('skip_connect', 1), ('max_pool_3x3', 0), ('dil_conv_5x5', 1), ('dil_conv_5x5', 0), ('skip_connect', 2)], reduce_concat=range(2, 6))\n\nCARS_B = Genotype(normal=[('sep_conv_5x5', 0), ('dil_conv_3x3', 1), ('sep_conv_3x3', 0), ('avg_pool_3x3', 2), ('dil_conv_3x3', 0), ('max_pool_3x3', 1), ('avg_pool_3x3', 0), ('skip_connect', 1)], normal_concat=range(2, 6), reduce=[('sep_conv_5x5', 0), ('skip_connect', 1), ('sep_conv_3x3', 0), ('max_pool_3x3', 1), ('avg_pool_3x3', 0), ('avg_pool_3x3', 1), ('dil_conv_3x3', 3), ('max_pool_3x3', 0)], reduce_concat=range(2, 6))\n\nCARS_C = Genotype(normal=[('sep_conv_5x5', 0), ('skip_connect', 1), ('skip_connect', 0), ('skip_connect', 1), ('skip_connect', 0), ('max_pool_3x3', 1), ('sep_conv_5x5', 0), ('sep_conv_3x3', 1)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 1), ('max_pool_3x3', 0), ('sep_conv_5x5', 0), ('sep_conv_5x5', 1), ('dil_conv_5x5', 0), ('max_pool_3x3', 1), ('sep_conv_5x5', 0), ('dil_conv_3x3', 1)], reduce_concat=range(2, 6))\n\nCARS_D = Genotype(normal=[('sep_conv_5x5', 0), ('dil_conv_3x3', 1), ('skip_connect', 0), ('avg_pool_3x3', 1), ('skip_connect', 0), ('max_pool_3x3', 1), ('sep_conv_5x5', 0), ('sep_conv_3x3', 1)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 0), ('sep_conv_3x3', 1), ('dil_conv_5x5', 0), ('max_pool_3x3', 1), ('sep_conv_5x5', 0), ('dil_conv_3x3', 1)], reduce_concat=range(2, 6))\n\nCARS_E = Genotype(normal=[('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('skip_connect', 0), ('sep_conv_3x3', 2), ('avg_pool_3x3', 1), ('sep_conv_3x3', 2), ('skip_connect', 3), ('skip_connect', 4)], normal_concat=range(2, 6), reduce=[('skip_connect', 0), ('dil_conv_3x3', 1), ('avg_pool_3x3', 0), ('skip_connect', 2), ('sep_conv_3x3', 2), ('max_pool_3x3', 0), ('avg_pool_3x3', 0), ('sep_conv_3x3', 4)], reduce_concat=range(2, 6))\n\nCARS_F = Genotype(normal=[('skip_connect', 0), ('sep_conv_5x5', 1), ('sep_conv_5x5', 0), ('skip_connect', 2), ('sep_conv_5x5', 3), ('max_pool_3x3', 0), ('skip_connect', 0), ('sep_conv_3x3', 1)], normal_concat=range(2, 6), reduce=[('avg_pool_3x3', 0), ('sep_conv_5x5', 1), ('dil_conv_3x3', 0), ('dil_conv_5x5', 1), ('sep_conv_5x5', 1), ('skip_connect', 2), ('max_pool_3x3', 0), ('max_pool_3x3', 1)], reduce_concat=range(2, 6))\n\nCARS_G = Genotype(normal=[('max_pool_3x3', 0), ('dil_conv_5x5', 1), ('sep_conv_3x3', 0), ('skip_connect', 1), ('dil_conv_5x5', 0), ('sep_conv_5x5', 1), ('avg_pool_3x3', 0), ('sep_conv_3x3', 1)], normal_concat=range(2, 6), reduce=[('max_pool_3x3', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('sep_conv_5x5', 1), ('sep_conv_3x3', 0), ('skip_connect', 1), ('avg_pool_3x3', 0), ('dil_conv_3x3', 1)], reduce_concat=range(2, 6))\n\nCARS_H = Genotype(normal=[('sep_conv_5x5', 0), ('sep_conv_3x3', 1), ('sep_conv_3x3', 0), ('dil_conv_5x5', 2), ('avg_pool_3x3', 0), ('skip_connect', 1), ('sep_conv_5x5', 2), ('max_pool_3x3', 0)], normal_concat=range(2, 6), reduce=[('sep_conv_5x5', 0), ('max_pool_3x3', 1), ('sep_conv_3x3', 0), ('skip_connect', 1), ('dil_conv_3x3', 2), ('max_pool_3x3', 0), ('sep_conv_5x5', 0), ('avg_pool_3x3', 3)], reduce_concat=range(2, 6))\n\nCARS_I = Genotype(normal=[('sep_conv_3x3', 0), ('sep_conv_3x3', 1), ('skip_connect', 0), ('sep_conv_5x5', 1), ('skip_connect', 2), ('sep_conv_3x3', 3), ('sep_conv_3x3', 0), ('dil_conv_5x5', 4)], normal_concat=range(2, 6), reduce=[('dil_conv_3x3', 0), ('skip_connect', 1), ('max_pool_3x3', 0), ('max_pool_3x3', 2), ('skip_connect', 1), ('sep_conv_5x5', 3), ('dil_conv_3x3', 1), ('max_pool_3x3', 4)], reduce_concat=range(2, 6))\n\n```\n\n### Objectives: Acc + Latency\n\n\u003ctable border=\"0\"\u003e\n\u003ctr\u003e\n    \u003ctd\u003e\n    \u003cimg src=\"./figs/results/cars_imagenet_latency.png\" width=\"100%\" /\u003e\n    \u003c/td\u003e\n\u003c/tr\u003e\n\u003c/table\u003e\n\n```python\n\nCARS_Lat_A = Genotype(normal=[('skip_connect',0),('max_pool_3x3',1),('avg_pool_3x3',0),('max_pool_3x3',1),('skip_connect',2),('skip_connect',3),('skip_connect',2),('skip_connect',3)],normal_concat=range(2,6),reduce=[('skip_connect',0),('sep_conv_5x5',1),('dil_conv_5x5',0),('max_pool_3x3',2),('skip_connect',0),('max_pool_3x3',1),('skip_connect',1),('avg_pool_3x3',4)],reduce_concat=range(2,6))\n\nCARS_Lat_B = Genotype(normal=[('skip_connect',0),('skip_connect',1),('skip_connect',1),('dil_conv_3x3',2),('skip_connect',2),('skip_connect',3),('max_pool_3x3',0),('max_pool_3x3',2)],normal_concat=range(2,6),reduce=[('max_pool_3x3',0),('max_pool_3x3',1),('skip_connect',1),('max_pool_3x3',0),('sep_conv_3x3',0),('max_pool_3x3',1),('dil_conv_5x5',0),('avg_pool_3x3',2)],reduce_concat=range(2,6))\n\nCARS_Lat_C = Genotype(normal=[('skip_connect',0),('avg_pool_3x3',1),('skip_connect',0),('skip_connect',1),('max_pool_3x3',1),('skip_connect',3),('dil_conv_3x3',2),('skip_connect',4)],normal_concat=range(2,6),reduce=[('skip_connect',0),('sep_conv_5x5',1),('avg_pool_3x3',1),('sep_conv_5x5',2),('max_pool_3x3',0),('max_pool_3x3',1),('dil_conv_5x5',2),('skip_connect',4)],reduce_concat=range(2,6))\n\nCARS_Lat_D = Genotype(normal=[('sep_conv_3x3',0),('skip_connect',1),('skip_connect',0),('skip_connect',1),('skip_connect',1),('avg_pool_3x3',3),('dil_conv_3x3',2),('skip_connect',4)],normal_concat=range(2,6),reduce=[('dil_conv_5x5',0),('sep_conv_5x5',1),('avg_pool_3x3',1),('sep_conv_5x5',2),('max_pool_3x3',0),('max_pool_3x3',1),('dil_conv_5x5',2),('skip_connect',4)],reduce_concat=range(2,6))\n\nCARS_Lat_E = Genotype(normal=[('dil_conv_5x5',0),('skip_connect',1),('skip_connect',1),('avg_pool_3x3',2),('skip_connect',1),('avg_pool_3x3',2),('skip_connect',0),('max_pool_3x3',1)],normal_concat=range(2,6),reduce=[('skip_connect',0),('dil_conv_3x3',1),('sep_conv_3x3',0),('sep_conv_3x3',2),('dil_conv_3x3',0),('avg_pool_3x3',3),('sep_conv_3x3',1),('sep_conv_5x5',2)],reduce_concat=range(2,6))\n\nCARS_Lat_F = Genotype(normal=[('sep_conv_5x5',0),('skip_connect',1),('skip_connect',0),('avg_pool_3x3',1),('dil_conv_3x3',1),('max_pool_3x3',0),('skip_connect',0),('skip_connect',1)],normal_concat=range(2,6),reduce=[('sep_conv_5x5',0),('max_pool_3x3',1),('sep_conv_5x5',1),('skip_connect',2),('sep_conv_5x5',0),('sep_conv_5x5',1),('sep_conv_5x5',3),('dil_conv_3x3',4)],reduce_concat=range(2,6))\n\nCARS_Lat_G = Genotype(normal=[('sep_conv_5x5',0),('skip_connect',1),('sep_conv_3x3',0),('sep_conv_5x5',2),('dil_conv_3x3',1),('max_pool_3x3',0),('skip_connect',0),('skip_connect',1)],normal_concat=range(2,6),reduce=[('sep_conv_5x5',0),('max_pool_3x3',1),('sep_conv_3x3',0),('avg_pool_3x3',2),('sep_conv_5x5',0),('sep_conv_5x5',1),('sep_conv_5x5',3),('dil_conv_3x3',4)],reduce_concat=range(2,6))\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzhaohui-yang%2FCARS","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fzhaohui-yang%2FCARS","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzhaohui-yang%2FCARS/lists"}