Historical milestone · 2009
ImageNet dataset and benchmark
Reviewed through September 18, 2026
2009 · Historical milestone
ImageNet dataset and benchmark
- Era
- 2000s
- Theme
- Infrastructure, efficiency & open ecosystems
- Evidence form
- Built a WordNet-organized image database with millions of labeled images and introduced large-scale visual recognition tasks
- School / paradigm
- Large-scale datasets / benchmarking
- Institution / context
- Princeton / Stanford
- Researchers
- Jia Deng; Wei Dong; Richard Socher; Li-Jia Li; Kai Li; Fei-Fei Li
Understand
Plain-language record, transferred from the reviewed source module.
Theory or experimental setup. Created the data and evaluation substrate that later exposed the advantage of deep convolutional networks.
Result / historical claim. Web-sourced labels, taxonomy bias, spurious correlations, and benchmark concentration affect validity.
Apply
Professional implication, only where the reviewed record states one.
The checked-in record does not state a separate professional application for this entry. The topic page places it in the wider research lineage: .
Verify
Evidence status, stated limitations, and the external sources this record actually carries.
Evidence form. Built a WordNet-organized image database with millions of labeled images and introduced large-scale visual recognition tasks
Limitation / debate. Scaling laws for data, foundation vision models, benchmark ecosystems, and dataset governance.
Source status. This milestone row does not carry a primary-source URL in the approved export, and we do not have a verified link for it in our own research. We do not guess one.
No primary-source URL is recorded for this entry in our reviewed data. Rather than manufacture a citation, we link the Implement Agentic research page that carries the record.
Reproduce
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A reproduction tutorial is not yet available for this entry. The closest reviewed material is .
Cite or share
APA-like: This historical record carries a year only, and no author or publisher of record in the checked-in data. An APA reference would have to invent that metadata.
BibTeX: BibTeX requires an author and publication venue. Historical lineage entries store a narrative record and its source link, not structured authorship, so the field would be fabricated.