Methods › Computer Vision › Convolutional Neural Networks › RegNetX
RegNetX
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).
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.
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Fracture Detection in Wrist X-ray Images Using Deep Learning-Based Object Detection Models 14 Nov 2021 · 0 repositories · arXiv:2111.07355
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Designing Network Design Spaces 30 Mar 2020 · 26 repositories · arXiv:2003.13678Syntology ran 11 of 53 samples · 42 unverified
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.
| Task | Papers |
|---|---|
| Ensemble Learning | 1 |
| Fracture detection | 1 |
| Image Classification | 1 |
| Medical Object Detection | 1 |
| Object Detection | 1 |
| Out-of-Distribution Generalization | 1 |
| Transfer Learning | 1 |
| object-detection | 1 |
Usage over time archive 2025-07-28
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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