DATA REPORT article
Front. Artif. Intell.
Sec. Pattern Recognition
This article is part of the Research TopicDeep Learning for Computer Vision and Measurement SystemsView all 6 articles
IndiaScene365: A transfer Learning dataset for Indian Scene Understanding in diverse weather condition
Provisionally accepted
- 1Savitribai Phule Pune University Board of College and University Development, Pune, India
- 2Cummins College of Engineering for Women, Pune, India
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
detail [15]. This limits the ability to train models 111 that can generalize well to such conditions. Foggy 112 Zurich [3] and Foggy Cityscapes [4] introduced 113 synthetic fog images by applying masks to original 114 images. Synthetic rain datasets [14] include 115 Rain1400, RainyCityscapes Rain100H, and 116 Rain12. Datasets like, BDD100K, ACDC [11], and 117 IDD [1], contain authentic images collected from 118 various adverse conditions [15], including fog, 119 rain, snow, and nighttime scenarios [5] [6]. The label set used in dataset preparation is identical to that in IDD [1] The hierarchy in the category of class labels adds a higher degree of complexity [5] to our dataset compared to existing datasets like Cityscapes [7], and even when compared to adverse weather datasets such as Foggy Cityscapes [8]
Keywords: deep learning, Pre-trained models, scene understanding, Transfer Learning, DomainAdaptation
Received: 19 Jul 2025; Accepted: 12 Dec 2025.
Copyright: © 2025 Mane, Arora and Shelke. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Deepa Mane
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.