Methods › Computer Vision › Object Detection Modules › Grid Sensitive

Grid Sensitive

102 papers tagged archive 2025-07-28

Introduced by Alexey Bochkovskiy et al. in YOLOv4: Optimal Speed and Accuracy of Object Detection

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

Grid Sensitive is a trick for object detection introduced by YOLOv4. When we decode the coordinate of the bounding box center x and y, in original YOLOv3, we can get them by

x=s ·(gₓ+σ(pₓ)) y=s ·(g_y+σ(p_y))

where σ is the sigmoid function, gₓ and g_y are integers and s is a scale factor. Obviously, x and y cannot be exactly equal to s ·gₓ or s ·(gₓ+1). This makes it difficult to predict the centres of bounding boxes that just located on the grid boundary. We can address this problem, by changing the equation to

x=s ·(gₓ+α·σ(pₓ)-(α-1) / 2) y=s ·(g_y+α·σ(p_y)-(α-1) / 2)

This makes it easier for the model to predict bounding box center exactly located on the grid boundary. The FLOPs added by Grid Sensitive are really small, and can be totally ignored.

PaperSource

Papers archive 2025-07-28

30 shown of 102, 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 124 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 Detection60
object-detection56
Object30
Real-Time Object Detection11
GPU8
Data Augmentation7
Deep Learning7
Autonomous Driving6
Segmentation5
Semantic Segmentation5
Transfer Learning5
2D Object Detection4
Domain Adaptation3
Image Classification3
Instance Segmentation3
Object Tracking3
Self-Driving Cars3
Traffic Sign Detection3
image-classification3
Adversarial Attack2

Usage over time archive 2025-07-28

Papers per year tagged with Grid Sensitive: 2016 to 2025, peak 34 34 0 2016: 1 paper 2016 2017: 0 papers 2017 2018: 0 papers 2018 2019: 0 papers 2019 2020: 14 papers 2020 2021: 24 papers 2021 2022: 34 papers 2022 2023: 17 papers 2023 2024: 5 papers 2024 2025: 7 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (102 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 Modules

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