Claim · 2017-04-17 · publication · Stated in source · Supported

The paper presents a class of models called MobileNets, intended for mobile and embedded vision applications.

One recorded claim. The claim panel gives its qualification, the source passages with their place in each source, and how it was checked.

What it connects

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications → states its contribution → The paper presents a class of models called MobileNets, intended for mobile and embedded vision applications.

Where it appears

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Claim and evidence

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationsstates its contribution ↓The paper presents a class of models called MobileNets, intended for mobile and embedded vision applications.

Machine-checked, not reviewed by an editorStated in sourceSupported

Supporting evidence: primary · 1 source

Date: 2017-04-17 · publication Records dated 2017 →

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

The evidence 01

primary sourceSupports the claim

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

arXiv · 2017-04-17

Cite the original source

Cite the original document, not Selekoa. Check these details against it.

Source excerpt

“We present a class of efficient models called MobileNets for mobile and embedded vision applications.”

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

Machine-checked · 2026-10-31

Source details and check notes

Authors: Andrew G. Howard; Menglong Zhu; Bo Chen; Dmitry Kalenichenko; Weijun Wang; Tobias Weyand; Marco Andreetto; Hartwig Adam
Version: arXiv:1704.04861v1, submitted 17 April 2017 (UTC). Retrieved for the candidate pack on 2026-10-28 (SHA-256 9fd87b02ecc96c1ecf2b8c2166faf969a2acb187ab3e1e9333b26d226387ff55). The author list is the byline as printed on the first page.
Version date: 2017-04-17
Canonical address: Open the canonical source ↗
Accessed: 2026-10-28
Language: English (en)
Attribution: Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam, “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications”, 2017-04-17.
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 (15 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 17 April 2017 (UTC); later versions may word it differently.

Record ID kp-states-mobilenets-efficient-convolutional-neural-networks-2017-1