Omkar2118/datasets huggingface dataset:
CAFD
Full name: Central Asian Food Dataset

This dataset comprises 16 499 images with 42 classes encompassing the most popular Central Asian cuisine consumed locally.
ImageNet-1k vs iNaturalist

A benchmark dataset for out-of-distribution detection. ImageNet-1k is in-distribution, while iNaturalist is out-of-distribution.

Introducing paper: MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space
ImageNet-1k vs SUN

A benchmark dataset for out-of-distribution detection. ImageNet-1k is in-distribution, while SUN is out-of-distribution.

Introducing paper: MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space
ImageNet-1k vs Places

A benchmark dataset for out-of-distribution detection. ImageNet-1k is in-distribution, while Places is out-of-distribution.

Introducing paper: MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space
ImageNet-1k vs Textures

A benchmark dataset for out-of-distribution detection. ImageNet-1k is in-distribution, while Textures is out-of-distribution.

Introducing paper: MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space
ImageNet-1k vs OpenImage-O

OpenImage-O is built for the ID dataset ImageNet-1k. It is manually annotated, comes with a naturally diverse distribution, and has a large scale. It is built to overcome several shortcomings of existing OOD benchmarks. OpenImage-O is image-by-image filtered from the test set of OpenImage-V3, which has been collected from Flickr without a predefined list of class names or tags, leading to natural class statistics and avoiding an initial design bias.

Introducing paper: ViM: Out-Of-Distribution with Virtual-logit Matching
MPDD
Full name: Metal Parts Defect Detection Dataset

MPDD is a dataset aimed at benchmarking visual defect detection methods in industrial metal parts manufacturing. It consists of more than 1000 images with pixel-precise defect annotation masks. The dataset is divided into the training subset with anomaly-free samples and the validation subset that contains both normal and anomalous samples. The dataset can be downloaded at the following link.

Introducing paper: Deep learning-based defect detection of metal parts: evaluating current methods in complex conditions