Methods › Computer Vision › Convolutional Neural Networks › RepVGG

RepVGG

13 papers tagged archive 2025-07-28

Introduced by Xiaohan Ding et al. in RepVGG: Making VGG-style ConvNets Great Again

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

RepVGG is a VGG-style convolutional architecture. It has the following advantages:

PaperSource

Papers archive 2025-07-28

13 shown of 13, 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

18 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
Semantic Segmentation3
GPU2
Quantization2
Classification1
Computational Efficiency1
Contrastive Learning1
Deep Learning1
Diagnostic1
Image Classification1
Image Segmentation1
Multi-Task Learning1
Network Pruning1
Object Detection1
Segmentation1
Speaker Recognition1
Word Embeddings1
object-detection1
regression1

Usage over time archive 2025-07-28

Papers per year tagged with RepVGG: 2021 to 2024, peak 5 5 0 2021: 3 papers 2021 2022: 2 papers 2022 2023: 3 papers 2023 2024: 5 papers 2024
Papers per year the archive tags with this method, by the paper's archive date (13 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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