Claim · 2013-11-11 · publication · Stated in source · Supported

The paper reports that a convolutional network trained for ImageNet classification can be adapted to detect objects in PASCAL, with a mean average precision of 48% on VOC 2007.

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What it connects

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation → states its contribution → The paper reports that a convolutional network trained for ImageNet classification can be adapted to detect objects in PASCAL, with a mean average precision of 48% on VOC 2007.

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Claim and evidence

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentationstates its contribution ↓The paper reports that a convolutional network trained for ImageNet classification can be adapted to detect objects in PASCAL, with a mean average precision of 48% on VOC 2007.

Machine-checked, not reviewed by an editorStated in sourceSupported

Supporting evidence: primary · 1 source

Date: 2013-11-11 · publication Records dated 2013 →

Scope and limitsThe paper's own description of its contribution; not an assessment of it.

The evidence 01

primary sourceSupports the claim

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

arXiv · 2013-11-11

Cite the original source

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Source excerpt

“We show that the answer is yes, and that the resulting system is simple, scalable, and boosts mean average precision, relative to the venerable deformable part model, by more than 40% (achieving a final mAP of 48% on VOC 2007).”

Location in source arXiv v1, PDF p. 1, abstract

Machine-checked · 2026-10-31

Source details and check notes

Authors: Ross Girshick; Jeff Donahue; Trevor Darrell; Jitendra Malik
Version: arXiv:1311.2524v1, submitted 11 November 2013 (UTC). Retrieved for the candidate pack on 2026-10-28 (SHA-256 3cb7a18aa362f810665b5becd2581649116ead4933efd133038d59d255163fc6). The author list is the byline as printed on the first page.
Version date: 2013-11-11
Canonical address: Open the canonical source ↗
Accessed: 2026-10-28
Language: English (en)
Attribution: Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik, “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation”, 2013-11-11.
Reuse: No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution.
Check notes: Candidate pack passage, key papers (research/key-papers). The quotation (40 words) matched the text retrieved for the candidate pack on 2026-10-28 (research/key-papers/verification.json). Matching ignored differences of whitespace, case, quotation marks, dashes and line-break hyphens, so the source’s typography can differ. No human editorial review.

Status and meaning

Editorial status. Not reviewed by an editor. An exploratory record that no editor has accepted. This status does not say whether a person has read any linked passage. Each evidence record above shows how it was checked.

Relation. A paper or other publication describes its own contribution, quoted from the expression the record names, usually its abstract or introduction. The record is the document’s own description, not an assessment: a claim of priority or novelty it contains is the document’s claim, flagged as such, and says nothing about earlier or later work.

Date. Date of the arXiv version quoted, submitted 11 November 2013 (UTC); later versions may word it differently.

Record ID kp-states-rich-feature-hierarchies-accurate-object-2013-2