Methods › Computer Vision › Image Model Blocks › Two-Way Dense Layer

Two-Way Dense Layer

3 papers tagged archive 2025-07-28

Introduced by Jun Wang et al. in Pelee: A Real-Time Object Detection System on Mobile Devices

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

Two-Way Dense Layer is an image model block used in the PeleeNet architectures. Motivated by GoogLeNet, the 2-way dense layer is used to get different scales of receptive fields. One way of the layer uses a 3x3 kernel size. The other way of the layer uses two stacked 3x3 convolution to learn visual patterns for large objects.

PaperSourceSee Code · osmr/imgclsmob

Papers archive 2025-07-28

3 shown of 3, 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

9 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
Object Detection2
Real-Time Object Detection2
Attribute1
Computational Efficiency1
Emotion Recognition1
Facial Expression Recognition (FER)1
Image Classification1
RTE1
object-detection1

Usage over time archive 2025-07-28

Papers per year tagged with Two-Way Dense Layer: 2018 to 2020, peak 1 1 0 2018: 1 paper 2018 2019: 1 paper 2019 2020: 1 paper 2020
Papers per year the archive tags with this method, by the paper's archive date (3 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

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