Paper, researchers, and primary source
Major lab research / computer_vision
YOLO reframes object detection as a single regression problem that predicts boxes and class probabilities directly from a full image.
Contribution 1
Unified localization and classification in one end-to-end neural network.
Contribution 2
Enabled real-time detection with a single image evaluation.
Contribution 3
Analyzed characteristic localization and background-error tradeoffs against two-stage detectors.
Research context
computer_vision / 2015
You Only Look Once: Unified, Real-Time Object Detection places yolo inside the broader computer vision discussion at University of Washington / Allen Institute for AI, with object detection supplying a second analytical lens. Read together, the source records three advances: Unified localization and classification in one end-to-end neural network; Enabled real-time detection with a single image evaluation; and Analyzed characteristic localization and background-error tradeoffs against two-stage detectors. Keeping those moves together prevents real time vision from being detached from its evidence. For an implementation review, the relevant consequence is that A fixed grid and early detection design struggle with small or clustered objects, and results on benchmark images do not guarantee deployment robustness.
Methods and evidence reading
1 cataloged method notes
Method 1
The experimental design in You Only Look Once: Unified, Real-Time Object Detection tests unified localization and classification in one end-to-end neural network and enabled real-time detection with a single image evaluation against the paper's documented baselines, datasets, model variants, or systems workloads.
How to read the evidence
The evidentiary value of You Only Look Once: Unified, Real-Time Object Detection comes from the relationship among its reported moves. Two entries define the method-level claim: Unified localization and classification in one end-to-end neural network; and Enabled real-time detection with a single image evaluation. The cataloged result is: Analyzed characteristic localization and background-error tradeoffs against two-stage detectors. On that basis, A fixed grid and early detection design struggle with small or clustered objects, and results on benchmark images do not guarantee deployment robustness. The catalog nevertheless records that the claim attached to You Only Look Once: Unified, Real-Time Object Detection is conditional on selected metrics, prompt protocol, image resolution, reported datasets, input modalities, and domain coverage, so it cannot be generalized from the paper title alone. Reproduction work should separate genuine yolo transfer from behavior caused by a changed object detection setup. Rechecking You Only Look Once: Unified, Real-Time Object Detection calls for an explicit yolo baseline, controlled object detection changes, and a trace of cases that challenge Enabled real-time detection with a single image evaluation under the new setup.
Findings in the source record
1 paper-specific findings
- The reported evidence in You Only Look Once: Unified, Real-Time Object Detection supports analyzed characteristic localization and background-error tradeoffs against two-stage detectors.
Practical implication for AI builders
University of Washington / Allen Institute for AI / 2015
Proposed BrokenGPT application
Research blueprint / proposed status
Proposed route placement / yolo
For a proposed BrokenGPT experiment based on You Only Look Once: Unified, Real-Time Object Detection, use detection only behind a task-specific benchmark covering small objects, occlusion, lighting, false positives, and latency on deployment hardware. Keep the yolo path isolated, versioned, and attributable to this research record.
Proposed acceptance test / object detection
Validate the proposed yolo route against the paper's reported outcome: Analyzed characteristic localization and background-error tradeoffs against two-stage detectors. For the You Only Look Once: Unified, Real-Time Object Detection prototype, collect boundary errors, task accuracy, transfer robustness, and calibration and audit object detection slices independently before promoting the yolo configuration.
Proposed decision boundary / real time vision
Balance resolution, latency, and domain shift before promoting the proposed real time vision design. Because A deployment review should isolate media rights, representation bias, memorization, source provenance, accessibility, and misuse when translating the yolo contribution into a different system, adoption remains conditional on replay under BrokenGPT's selected model, runtime, and policy configuration.
Limitations, verification, and source
Boundaries recorded with the paper
Limitations
- The claim attached to You Only Look Once: Unified, Real-Time Object Detection is conditional on selected metrics, prompt protocol, image resolution, reported datasets, input modalities, and domain coverage, so it cannot be generalized from the paper title alone.
- A deployment review should isolate media rights, representation bias, memorization, source provenance, accessibility, and misuse when translating the yolo contribution into a different system.
PRIMARY SOURCES
- 01You Only Look Once: Unified, Real-Time Object Detection
University of Washington / Allen Institute for AI — Primary primary arXiv paper / 8 June 2015 / Joseph Redmon, Santosh Divvala, Ross Girshick, and 1 more