Methods › Computer Vision › Image Model Blocks › ShuffleNet V2 Downsampling Block

ShuffleNet V2 Downsampling Block

14 papers tagged archive 2025-07-28

Introduced by Ningning Ma et al. in ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

ShuffleNet V2 Downsampling Block is a block for spatial downsampling used in the ShuffleNet V2 architecture. Unlike the regular ShuffleNet V2 block, the channel split operator is removed so the number of output channels is doubled.

PaperSourceSee Code · osmr/imgclsmob

Papers archive 2025-07-28

14 shown of 14, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

16 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Image Classification4
Object Detection4
Neural Architecture Search3
object-detection3
Deep Learning2
General Classification2
Semantic Segmentation2
image-classification2
Adversarial Attack1
Adversarial Robustness1
Autonomous Driving1
Decision Making1
Real-Time Object Detection1
Real-Time Semantic Segmentation1
Segmentation1
Self-Driving Cars1

Usage over time archive 2025-07-28

Papers per year tagged with ShuffleNet V2 Downsampling Block: 2018 to 2023, peak 4 4 0 2018: 3 papers 2018 2019: 4 papers 2019 2020: 2 papers 2020 2021: 3 papers 2021 2022: 0 papers 2022 2023: 2 papers 2023
Papers per year the archive tags with this method, by the paper's archive date (14 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Image Model Blocks

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