Omkar2118/datasets huggingface dataset:
POPCORN
Full name: POPCORN: Fictional and Synthetic Intelligence Reports for Named Entity Recognition and Relation Extraction Tasks

POPCORN is a French dataset consisting of 400 validation texts and 400 training texts, all written and annotated manually. The texts are concise and factual, resembling information reports. The annotations, based on the ontology described below, allow for the training and evaluation of models in Information Extraction tasks, including Named Entity Recognition, Coreference Resolution, and Relation Extraction.

Additionally, 400 more texts have been provided for the TextMine 2025 challenge on Relation Extraction, which can be accessed via the following link: https://www.kaggle.com/competitions/defi-text-mine-2025/
KITTI-360-SR
Full name: KITTI-360 modification for Scene Recognition task

Scene Recognition is a problem, where a set of visible objects must be correctly associated with objects marked on a semantic map - this problem is also sometimes called a Data Association. Please note, that Scene Recognition in terms where the observed scene must be labeled in terms such as 'kitchen', 'bedroom' and so on is a different problem.

Scene Recognition is used in different mobile robot localization problems, such as semantic mapping, global localization, kidnapping, loop-closing and so on. But problem is that there is no special dataset for that task (at least I found none). Such dataset should have the following features:
 -  a Semantic map with globally marked objects;
 -  scenes data (views from robot camera), where objects are also labeled and their IDs are correctly matched with IDs on the Semantic map.

For full info please follow https://github.com/MoscowskyAnton/scene_recognition_kitti_360