Open Set Learning

Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection (MIMII) is a sound dataset of industrial machine sounds. Source: MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection

The NINCO (No ImageNet Class Objects) dataset is introduced in the ICML 2023 paper In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation. The images in this dataset are free from objects that belong to any of the 1000 classes of ImageNet-1K (ILSVRC2012), which makes NINCO suitable for evaluating out-of-distribution detection on ImageNet-1K . The NINCO main dataset consists of 64 OOD classes with a total of 5879 samples. These OOD classes were selected to have no categorical overlap with any classes of ImageNet-1K. Each sample was inspected individually by the authors to not contain ID objects. Besides NINCO, included are (in the same .tar.gz file) truly OOD versions of 11 popular OOD datasets with in total 2715 OOD samples. Further included are 17 OOD unit-tests, with 400 samples each. Code for loading and evaluating on each of the three datasets is provided at https://github.com/j-cb/NINCO. When using NINCO, please consider citing (besides the bibtex given below) the following data sources that were used to create NINCO: Hendrycks et al.: ”Scaling out-of-distribution detection for real-world settings”, ICML, 2022. Bossard et al.: ”Food-101 – mining discriminative components with random forests”, ECCV 2014. Zhou et al.: ”Places: A 10 million image database for scene recognition”, IEEE PAMI 2017. Huang et al.: ”Mos: Towards scaling out-of-distribution detection for large semantic space”, CVPR 2021. Li et al.: ”Caltech 101 (1.0)”, 2022. Ismail et al.: ”MYNursingHome: A fully-labelled image dataset for indoor object classification.”, Data in Brief (V. 32) 2020. The iNaturalist project: https://www.inaturalist.org/ When using NINCO_popular_datasets_subsamples, additionally to the above, please consider citing: Cimpoi et al.: ”Describing textures in the wild”, CVPR 2014. Hendrycks et al.: ”Natural adversarial examples”, CVPR 2021. Wang et al.: ”Vim: Out-of-distribution with virtual-logit matching”, CVPR 2022. Bendale et al.: ”Towards Open Set Deep Networks”, CVPR 2016. Vaze et al.: ”Open-set Recognition: a Good Closed-set Classifier is All You Need?”, ICLR 2022. Wang et al.: ”Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition.” ICML, 2022. Galil et al.: “A framework for benchmarking Class-out-of-distribution detection and its application to ImageNet”, ICLR 2023.

ToyADMOS dataset is a machine operating sounds dataset of approximately 540 hours of normal machine operating sounds and over 12,000 samples of anomalous sounds collected with four microphones at a 48kHz sampling rate, prepared by Yuma Koizumi and members in NTT Media Intelligence Laboratories. The ToyADMOS dataset is designed for anomaly detection in machine operating sounds (ADMOS) research. It is designed for three tasks of ADMOS: product inspection (toy car), fault diagnosis for fixed machine (toy conveyor), and fault diagnosis for moving machine (toy train). Source: https://github.com/YumaKoizumi/ToyADMOS-dataset

Contains normal driving videos together with a set of anomalous actions in its training set. In the test set of the DAD dataset, there are unseen anomalous actions that still need to be winnowed out from normal driving. Source: Driver Anomaly Detection: A Dataset and Contrastive Learning Approach

The NINCO (No ImageNet Class Objects) dataset is introduced in the ICML 2023 paper In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation. The images in this dataset are free from objects that belong to any of the 1000 classes of ImageNet-1K (ILSVRC2012), which makes NINCO suitable for evaluating out-of-distribution detection on ImageNet-1K . The NINCO main dataset consists of 64 OOD classes with a total of 5879 samples. These OOD classes were selected to have no categorical overlap with any classes of ImageNet-1K. Each sample was inspected individually by the authors to not contain ID objects. Besides NINCO, included are (in the same .tar.gz file) truly OOD versions of 11 popular OOD datasets with in total 2715 OOD samples. Further included are 17 OOD unit-tests, with 400 samples each. Code for loading and evaluating on each of the three datasets is provided at https://github.com/j-cb/NINCO. When using NINCO, please consider citing (besides the bibtex given below) the following data sources that were used to create NINCO: Hendrycks et al.: ”Scaling out-of-distribution detection for real-world settings”, ICML, 2022. Bossard et al.: ”Food-101 – mining discriminative components with random forests”, ECCV 2014. Zhou et al.: ”Places: A 10 million image database for scene recognition”, IEEE PAMI 2017. Huang et al.: ”Mos: Towards scaling out-of-distribution detection for large semantic space”, CVPR 2021. Li et al.: ”Caltech 101 (1.0)”, 2022. Ismail et al.: ”MYNursingHome: A fully-labelled image dataset for indoor object classification.”, Data in Brief (V. 32) 2020. The iNaturalist project: https://www.inaturalist.org/ When using NINCO_popular_datasets_subsamples, additionally to the above, please consider citing: Cimpoi et al.: ”Describing textures in the wild”, CVPR 2014. Hendrycks et al.: ”Natural adversarial examples”, CVPR 2021. Wang et al.: ”Vim: Out-of-distribution with virtual-logit matching”, CVPR 2022. Bendale et al.: ”Towards Open Set Deep Networks”, CVPR 2016. Vaze et al.: ”Open-set Recognition: a Good Closed-set Classifier is All You Need?”, ICLR 2022. Wang et al.: ”Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition.” ICML, 2022. Galil et al.: “A framework for benchmarking Class-out-of-distribution detection and its application to ImageNet”, ICLR 2023.

Unconstrained Face Detection and Open-Set Face Recognition Challenge * Paper: https://arxiv.org/abs/1708.02337 * Official website: https://vast.uccs.edu/Opensetface * UnOfficial website: https://exposing.ai/uccs Face detection and recognition benchmarks have shifted toward more difficult environments. The challenge presented in this paper addresses the next step in the direction of automatic detection and identification of people from outdoor surveillance cameras. While face detection has shown remarkable success in images collected from the web, surveillance cameras include more diverse occlusions, poses, weather conditions and image blur. Although face verification or closed-set face identification have surpassed human capabilities on some datasets, open-set identification is much more complex as it needs to reject both unknown identities and false accepts from the face detector. We show that unconstrained face detection can approach high detection rates albeit with moderate false accept rates. By contrast, open-set face recognition is currently weak and requires much more attention.