Methods › Computer Vision › Convolutional Neural Networks › RegNetX

RegNetX

2 papers tagged archive 2025-07-28

Introduced by Ilija Radosavovic et al. in Designing Network Design Spaces

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

RegNetX is a convolutional network design space with simple, regular models with parameters: depth d, initial width w₀ > 0, and slope wₐ > 0, and generates a different block width uⱼ for each block j < d. The key restriction for the RegNet types of model is that there is a linear parameterisation of block widths (the design space only contains models with this linear structure):

uⱼ = w₀ + wₐ·j

For RegNetX we have additional restrictions: we set b = 1 (the bottleneck ratio), 12 ≤d ≤28, and wₘ ≥2 (the width multiplier).

PaperSourceSee Code · facebookresearch/pycls

Papers archive 2025-07-28

2 shown of 2, 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

8 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
Ensemble Learning1
Fracture detection1
Image Classification1
Medical Object Detection1
Object Detection1
Out-of-Distribution Generalization1
Transfer Learning1
object-detection1

Usage over time archive 2025-07-28

Papers per year tagged with RegNetX: 2020 to 2021, peak 1 1 0 2020: 1 paper 2020 2021: 1 paper 2021
Papers per year the archive tags with this method, by the paper's archive date (2 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

Convolutional Neural Networks

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