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Methods › Computer Vision › One-Stage Object Detection Models › SSD

SSD

278 papers tagged archive 2025-07-28

Introduced by Wei Liu et al. in SSD: Single Shot MultiBox Detector

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

SSD is a single-stage object detection method that discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. At prediction time, the network generates scores for the presence of each object category in each default box and produces adjustments to the box to better match the object shape. Additionally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes.

The fundamental improvement in speed comes from eliminating bounding box proposals and the subsequent pixel or feature resampling stage. Improvements over competing single-stage methods include using a small convolutional filter to predict object categories and offsets in bounding box locations, using separate predictors (filters) for different aspect ratio detections, and applying these filters to multiple feature maps from the later stages of a network in order to perform detection at multiple scales.

PaperSourceSee Code · amdegroot/ssd.pytorch

Papers archive 2025-07-28

30 shown of 278, 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 234 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 Detection126
object-detection120
Object66
GPU16
Real-Time Object Detection16
Image Classification11
Deep Learning10
Pedestrian Detection10
Transfer Learning10
General Classification8
image-classification8
Autonomous Driving7
CPU7
Semantic Segmentation7
Classification6
regression6
2D Object Detection5
Benchmarking5
Clustering5
Quantization5

Usage over time archive 2025-07-28

Papers per year tagged with SSD: 2015 to 2025, peak 45 45 0 2015: 1 paper 2015 2016: 6 papers 2016 2017: 21 papers 2017 2018: 42 papers 2018 2019: 33 papers 2019 2020: 36 papers 2020 2021: 45 papers 2021 2022: 22 papers 2022 2023: 24 papers 2023 2024: 35 papers 2024 2025: 13 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (278 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

One-Stage Object Detection ModelsObject Detection Models

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