Methods › Computer Vision › Image Model Blocks › ResNeXt Block

ResNeXt Block

132 papers tagged archive 2025-07-28

Introduced by Saining Xie et al. in Aggregated Residual Transformations for Deep Neural Networks

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

A ResNeXt Block is a type of residual block used as part of the ResNeXt CNN architecture. It uses a "split-transform-merge" strategy (branched paths within a single module) similar to an Inception module, i.e. it aggregates a set of transformations. Compared to a Residual Block, it exposes a new dimension, cardinality (size of set of transformations) C, as an essential factor in addition to depth and width.

Formally, a set of aggregated transformations can be represented as: ℱ(x)=∑ᵢ₌₁^C𝒯ᵢ(x), where 𝒯ᵢ(x) can be an arbitrary function. Analogous to a simple neuron, 𝒯ᵢ should project x into an (optionally low-dimensional) embedding and then transform it.

PaperSourceSee Code · pytorch/vision

Papers archive 2025-07-28

30 shown of 132, 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

20 shown of 161 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 Detection39
Image Classification28
object-detection25
Semantic Segmentation24
Segmentation20
General Classification19
image-classification16
Instance Segmentation15
Object14
Classification8
Transfer Learning8
Action Recognition7
Panoptic Segmentation6
Image Segmentation5
Neural Architecture Search5
Real-Time Object Detection5
2D Object Detection4
Action Classification4
Deep Learning4
GPU4

Usage over time archive 2025-07-28

Papers per year tagged with ResNeXt Block: 2016 to 2025, peak 43 43 0 2016: 2 papers 2016 2017: 8 papers 2017 2018: 27 papers 2018 2019: 43 papers 2019 2020: 19 papers 2020 2021: 18 papers 2021 2022: 4 papers 2022 2023: 3 papers 2023 2024: 6 papers 2024 2025: 2 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (132 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 BlocksSkip Connection Blocks

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