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Deep net architectures for visual-based clothing image recognition on large database

Research article (Soft Computing, 2017) · cited 19× · AI/ML
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Summary

Deep net architectures for visual-based clothing image recognition on large database is a scholarly article[1].

Key Facts

  • Deep net architectures for visual-based clothing image recognition on large database's instance of is recorded as scholarly article[2].

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Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

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Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.

APA 4ort.xyz Knowledge Graph. (2026). Deep net architectures for visual-based clothing image recognition on large database. Retrieved May 24, 2026, from https://4ort.xyz/entity/deep-net-architectures-for-visual-based-clothing-image-recognition-on-large-database
MLA “Deep net architectures for visual-based clothing image recognition on large database.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/deep-net-architectures-for-visual-based-clothing-image-recognition-on-large-database.
BibTeX @misc{4ortxyz_deep-net-architectures-for-visual-based-clothing-image-recognition-on-large-database_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Deep net architectures for visual-based clothing image recognition on large database}}, year = {2026}, url = {https://4ort.xyz/entity/deep-net-architectures-for-visual-based-clothing-image-recognition-on-large-database}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Deep net architectures for visual-based clothing image recognition on large database — https://4ort.xyz/entity/deep-net-architectures-for-visual-based-clothing-image-recognition-on-large-database (retrieved 2026-05-24)

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