Claim · 2012 · publication · Stated in source · Supported

The authors state that they trained a large, deep convolutional neural network to classify the 1.2 million images of the ImageNet LSVRC-2010 contest into 1000 classes.

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

ImageNet classification with deep convolutional neural networks → states its contribution → The authors state that they trained a large, deep convolutional neural network to classify the 1.2 million images of the ImageNet LSVRC-2010 contest into 1000 classes.

Where it appears

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Claim and evidence

ImageNet classification with deep convolutional neural networksstates its contribution ↓The authors state that they trained a large, deep convolutional neural network to classify the 1.2 million images of the ImageNet LSVRC-2010 contest into 1000 classes.

Machine-checked, not reviewed by an editorStated in sourceSupported

Supporting evidence: primary · 1 source

Date: 2012 · publication Records dated 2012 →

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

The evidence 01

primary sourceSupports the claim

ImageNet classification with deep convolutional neural networks

Neural Information Processing Systems · 2012

Cite the original source

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Source excerpt

“We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes.”

Location in source PDF p. 1, abstract

Machine-checked · 2026-10-31

Source details and check notes

Authors: Alex Krizhevsky; Ilya Sutskever; Geoffrey E. Hinton
Version: Paper PDF in the proceedings of Advances in Neural Information Processing Systems 25 (2012). Retrieved for the candidate pack on 2026-10-28 (SHA-256 90137160c57217953d5f61857e64ca58e85f06e1b13b4f475c918b1b582b9771). The author list is the byline as printed on the first page.
Canonical address: Open the canonical source ↗
Accessed: 2026-10-28
Language: English (en)
Attribution: Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, “ImageNet classification with deep convolutional neural networks”, 2012.
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 (25 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 version quoted, as its source gives it (2012); other versions may word it differently.

Record ID kp-states-imagenet-classification-deep-convolutional-neural-2012-1