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Faster R-CNN

499 papers tagged archive 2025-07-28

Introduced by Shaoqing Ren et al. in Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

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

Faster R-CNN is an object detection model that improves on Fast R-CNN by utilising a region proposal network (RPN) with the CNN model. The RPN shares full-image convolutional features with the detection network, enabling nearly cost-free region proposals. It is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. RPN and Fast R-CNN are merged into a single network by sharing their convolutional features: the RPN component tells the unified network where to look.

As a whole, Faster R-CNN consists of two modules. The first module is a deep fully convolutional network that proposes regions, and the second module is the Fast R-CNN detector that uses the proposed regions.

PaperSourceSee Code · chenyuntc/simple-faster-rcnn-pytorch

Papers archive 2025-07-28

30 shown of 499, 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 302 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 Detection326
object-detection303
Object179
Region Proposal38
Image Classification35
Semantic Segmentation32
Instance Segmentation24
image-classification24
General Classification23
Deep Learning20
Transfer Learning19
Data Augmentation18
Pedestrian Detection17
Autonomous Driving15
Classification14
Domain Adaptation14
GPU13
Object Recognition12
Segmentation12
2D Object Detection11

Usage over time archive 2025-07-28

Papers per year tagged with Faster R-CNN: 2015 to 2025, peak 78 78 0 2015: 3 papers 2015 2016: 13 papers 2016 2017: 32 papers 2017 2018: 64 papers 2018 2019: 70 papers 2019 2020: 74 papers 2020 2021: 78 papers 2021 2022: 61 papers 2022 2023: 42 papers 2023 2024: 45 papers 2024 2025: 17 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (499 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

Object Detection Models

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