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Computing & AI · Source-linked note

Redmon and colleagues: YOLO unified real-time object detection

Framed object detection as a single-network regression from full images to bounding boxes and class probabilities, with real-time performance in the reported systems.

What is recorded here?

Framed object detection as a single-network regression from full images to bounding boxes and class probabilities, with real-time performance in the reported systems.

Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.

Scientific scope

The record is the exact four-author CVPR 2016 paper and first YOLO formulation. Later YOLO versions, implementations and ownership lineages remain separate; the paper itself reports localization and small-object limitations.

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People and systems

Joseph Redmon; Santosh Divvala; Ross Girshick; Ali Farhadi

The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.

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Documented countries / regions: United States

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English catalogue narrative

Redmon and colleagues: YOLO unified real-time object detection

Framed object detection as a single-network regression from full images to bounding boxes and class probabilities, with real-time performance in the reported systems.

Scientific scope

The record is the exact four-author CVPR 2016 paper and first YOLO formulation. Later YOLO versions, implementations and ownership lineages remain separate; the paper itself reports localization and small-object limitations.

Reading status

This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.

People and systems

Joseph Redmon; Santosh Divvala; Ross Girshick; Ali Farhadi

The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.

Original sources