mlsanityData to 14 Oct 2026

ICONIC-444: A 3.1-Million-Image Dataset for OOD Detection Research

arXiv 2601.10802 ↗First posted 15 Jan 2026cs.CVcited by 1

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Abstract (arXiv)

Current progress in out-of-distribution (OOD) detection is limited by the lack of large, high-quality datasets with clearly defined OOD categories across varying difficulty levels (near- to far-OOD) that support both fine- and coarse-grained computer vision tasks. To address this limitation, we introduce ICONIC-444 (Image Classification and OOD Detection with Numerous Intricate Complexities), a specialized large-scale industrial image dataset containing over 3.1 million RGB images spanning 444 classes tailored for OOD detection research. Captured with a prototype industrial sorting machine, ICONIC-444 closely mimics real-world tasks. It complements existing datasets by offering structured, diverse data suited for rigorous OOD evaluation across a spectrum of task complexities. We define four reference tasks within ICONIC-444 to benchmark and advance OOD detection research and provide baseline results for 22 state-of-the-art post-hoc OOD detection methods.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 49 of its 72 references that are in the map. First posted on arXiv 15 Jan 2026.

Builds on

49 of 72 references.

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37 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Unified Multi-Layer Subspace Modeling for Cross-Domain OOD Detectioncited by 0