Omkar2118/datasets huggingface dataset:
DISC-Law-SFT

DISC-Law-SFT comprises two subsets, DISC-Law-SFT-Pair and DISC-Law-SFT-Triplet. The former aims to introduce legal reasoning abilities to the LLM, while the latter helps enhance the model's capability to utilize external legal knowledge.

Introducing paper: DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services
DeVAn
Full name: Dense Video Annotation for Video-Language Models

DeVAn is a multi-modal dataset containing 8.5K video clips carefully selected from previously published YouTube-based video datasets (YouTube-8M and YT-Temporal-1B) that integrate visual and auditory information. Over the span of 10 months, a team of 24 human annotators (college and graduate level students) created 5 short captions (1 sentence each) and 5 long summaries (3-10 sentences) for each video clip, resulting in a rich and comprehensive human-annotated dataset that serves as a robust ground truth for subsequent model training and evaluation.

Introducing paper: DeVAn: Dense Video Annotation for Video-Language Models
Yo'LLaVA

40 personalized concepts

Introducing paper: Yo'LLaVA: Your Personalized Language and Vision Assistant
SpaGBOL
Full name: Spatial-Graph-Based Orientated Cross-View Geo-Localisation

Cross-View Geo-Localisation within urban regions is challenging in part due to the lack of geo-spatial structuring within current datasets and techniques. We propose utilising graph representations to model sequences of local observations and the connectivity of the target location. Modelling as a graph enables generating previously unseen sequences by sampling with new parameter configurations. 
SpaGBOL contains 98,855 panoramic streetview images across different seasons, and 19,771 corresponding satellite images from 10 mostly densely populated international cities. This translates to 5 panoramic images and one satellite image per graph node. Downloading instructions below.

Introducing paper: SpaGBOL: Spatial-Graph-Based Orientated Localisation
Railway Track Misalignment Detection Image Dataset
Full name: A Vision-based Solution for Track Misalignment Detection

The dataset has railway track images of two types: normal and defective

The task is to classify a given image into normal or defective. 

The defective images have misalignment problems: buckling or hogging

If you find this dataset useful in your research, please cite the below paper:

@INPROCEEDINGS{9643106,

  author={Jerripothula, Koteswar Rao and Ansari, Sharik Ali and Nijhawan, Rahul},

  booktitle={2021 34th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)}, 

  title={A Vision-based Solution for Track Misalignment Detection}, 

  year={2021},

  pages={271-277},

  keywords={Graphics;Annotations;Railway accidents;Transfer learning;Buildings;Feature extraction;Rail transportation;railway;transfer learning;VGG;Inception;buckling;hogging;misalignment},

  doi={10.1109/SIBGRAPI54419.2021.00044}

}
Khayyam Offline Persian Handwriting Dataset

Handwriting analysis is still an important application in machine learning. A basic requirement for any handwriting recognition application is the availability of comprehensive datasets. Standard labelled datasets play a significant role in training and evaluating learning algorithms. In this paper, we present the Khayyam dataset as another large unconstrained handwriting dataset for elements (words, sentences, letters, digits) of the Persian language. We intentionally concentrated on collecting Persian word samples which are rare in the currently available datasets. Khayyam's dataset contains 44000 words, 60000 letters, and 6000 digits. Moreover, the forms were filled out by 400 native Persian writers. To show the applicability of the dataset, machine learning algorithms are trained on the digits, letters, and word data and results are reported. This dataset is available for research and academic use.