SOOD-ImageNet: a Large-Scale Dataset for Semantic Out-Of-Distribution Image Classification and Semantic Segmentation
<|reference_start|>SOOD-ImageNet: a Large-Scale Dataset for Semantic Out-Of-Distribution Image Classification and Semantic Segmentation: Out-of-Distribution (OOD) detection in computer vision is a crucial research area, with related benchmarks playing a vital role in assessing the generalizability of models and their applicability in real-world scenarios. However, existing OOD benchmarks in the literature suffer from two main limitations: (1) they often overlook semantic shift as a potential challenge, and (2) their scale is limited compared to the large datasets used to train modern models. To address these gaps, we introduce SOOD-ImageNet, a novel dataset comprising around 1.6M images across 56 classes, designed for common computer vision tasks such as image classification and semantic segmentation under OOD conditions, with a particular focus on the issue of semantic shift. We ensured the necessary scalability and quality by developing an innovative data engine that leverages the capabilities of modern vision-language models, complemented by accurate human checks. Through extensive training and evaluation of various models on SOOD-ImageNet, we showcase its potential to significantly advance OOD research in computer vision. The project page is available at https://github.com/bach05/SOODImageNet.git.<|reference_end|>
@article{bacchin2024sood-imagenet:,
    title={SOOD-ImageNet: a Large-Scale Dataset for Semantic Out-Of-Distribution
  Image Classification and Semantic Segmentation},
    author={Alberto Bacchin, Davide Allegro, Stefano Ghidoni, Emanuele Menegatti},
    journal={arXiv preprint arXiv:2409.01109},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.01109},
    primaryClass={cs.CV cs.LG}
}