Papers › Rethinking Channel Dimensions for Efficient Model Design

Rethinking Channel Dimensions for Efficient Model Design

2 Jul 2020CVPR 2021 1arXiv:2007.00992archive 2025-07-28

Dongyoon Han, Sangdoo Yun, Byeongho Heo, Youngjoon Yoo

Designing an efficient model within the limited computational cost is challenging. We argue the accuracy of a lightweight model has been further limited by the design convention: a stage-wise configuration of the channel dimensions, which looks like a piecewise linear function of the network stage. In this paper, we study an effective channel dimension configuration towards better performance than the convention. To this end, we empirically study how to design a single layer properly by analyzing the rank of the output feature. We then investigate the channel configuration of a model by searching network architectures concerning the channel configuration under the computational cost restriction. Based on the investigation, we propose a simple yet effective channel configuration that can be parameterized by the layer index. As a result, our proposed model following the channel parameterization achieves remarkable performance on ImageNet classification and transfer learning tasks including COCO object detection, COCO instance segmentation, and fine-grained classifications. Code and ImageNet pretrained models are available at https://github.com/clovaai/rexnet.

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clovaai/rexnet officialmentioned in papermentioned on GitHubpytorch report
YeongHyeon/ReXNet-TF2 mentioned on GitHubtfMIT report
alexwitt2399/rexnet mentioned on GitHubpytorchBSD-3-Clause report
lyqcom/rexnet mentioned on GitHubmindspore report
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ysbsb/ReXNet-PyTorch mentioned on GitHubpytorchMIT report
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str2bool lyqcom/rexnet/config.py community (archive-listed) ran · violated contract Apache-2.0 (permissive) · 17061f7e01a6fc86 · report
accuracy ysbsb/ReXNet-PyTorch/utils.py community (archive-listed) unverified MIT (permissive) · f84cc119467969e4 · report
full_filepath paperswithcode/model-index/modelindex/utils.py community (archive-listed) unverified MIT (permissive) · 14cb906d9717c748 · report
has_at_least_one_models_key paperswithcode/model-index/modelindex/load_model_index.py community (archive-listed) unverified MIT (permissive) · 141717f5e0516401 · report
load_ckpt ysbsb/ReXNet-PyTorch/utils.py community (archive-listed) unverified MIT (permissive) · 0535326f88e06187 · report
load_raw_from_markdown paperswithcode/model-index/modelindex/utils.py community (archive-listed) unverified MIT (permissive) · 2698f6291a00ce3f · report
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Tasks

Image ClassificationInstance SegmentationObject DetectionSemantic SegmentationTransfer Learningmodelobject-detection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Classification ImageNet ReXNet-R_3.0 Number of params 34.8M #308 of 1060 Archive leaderboard report
Image Classification ImageNet ReXNet-R_3.0 Top 1 Accuracy 84.5% #308 of 1060 Archive leaderboard report
Image Classification ImageNet ReXNet-R_2.0 Number of params 16.5M #452 of 1060 Archive leaderboard report
Image Classification ImageNet ReXNet-R_2.0 Top 1 Accuracy 83.2% #452 of 1060 Archive leaderboard report
Image Classification ImageNet ReXNet_3.0 GFLOPs 3.4 #499 of 1060 Archive leaderboard report
Image Classification ImageNet ReXNet_3.0 Number of params 34.7M #499 of 1060 Archive leaderboard report
Image Classification ImageNet ReXNet_3.0 Top 1 Accuracy 82.8% #499 of 1060 Archive leaderboard report
Image Classification ImageNet ReXNet_2.0 GFLOPs 1.5 #625 of 1060 Archive leaderboard report
Image Classification ImageNet ReXNet_2.0 Number of params 19M #625 of 1060 Archive leaderboard report
Image Classification ImageNet ReXNet_2.0 Top 1 Accuracy 81.6% #625 of 1060 Archive leaderboard report
Image Classification ImageNet ReXNet_1.5 GFLOPs 0.86 #709 of 1060 Archive leaderboard report
Image Classification ImageNet ReXNet_1.5 Number of params 9.7M #709 of 1060 Archive leaderboard report
Image Classification ImageNet ReXNet_1.5 Top 1 Accuracy 80.3% #709 of 1060 Archive leaderboard report
Image Classification ImageNet ReXNet_1.3 GFLOPs 0.66 #751 of 1060 Archive leaderboard report
Image Classification ImageNet ReXNet_1.3 Number of params 7.6M #751 of 1060 Archive leaderboard report
Image Classification ImageNet ReXNet_1.3 Top 1 Accuracy 79.5% #751 of 1060 Archive leaderboard report
Image Classification ImageNet ReXNet_1.0 GFLOPs 0.40 #859 of 1060 Archive leaderboard report
Image Classification ImageNet ReXNet_1.0 Number of params 4.8M #859 of 1060 Archive leaderboard report
Image Classification ImageNet ReXNet_1.0 Top 1 Accuracy 77.9% #859 of 1060 Archive leaderboard report
Image Classification ImageNet ReXNet_0.9 GFLOPs 0.35 #881 of 1060 Archive leaderboard report
Image Classification ImageNet ReXNet_0.9 Number of params 4.1M #881 of 1060 Archive leaderboard report
Image Classification ImageNet ReXNet_0.9 Top 1 Accuracy 77.2% #881 of 1060 Archive leaderboard report
Image Classification ImageNet ReXNet_0.6 Number of params 2.7M #974 of 1060 Archive leaderboard report
Image Classification ImageNet ReXNet_0.6 Top 1 Accuracy 74.6% #974 of 1060 Archive leaderboard report

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