Omkar2118/datasets huggingface dataset:
KayifamilyTv Video Solution
Full name: KayifamilyTv Video Solution: Scalable Video-Mining Pipeline

At KayifamilyTv, we introduce a powerful and scalable video-mining solution designed to enhance captioning capabilities by transferring supervision from image datasets to video and audio content. Using this innovative pipeline, we mine paired video clips and captions, starting with the Conceptual Captions 3M (CC3M) image dataset as our foundation. The result of this process is VideoCC3M—a large-scale collection of millions of video clips weakly paired with text captions, which we plan to make publicly available.

The heart of our approach is simple yet effective: we begin with an existing image-caption dataset and, for each image-caption pair, locate frames within videos that closely resemble the original image. From these matches, we extract short video clips and transfer the corresponding captions to them. A detailed breakdown of this process is available in our research paper.

For this project, we used the Conceptual Captions 3M dataset, focusing only on the images that remain publicly accessible. This left us with 1.25 million image-caption pairs. We then applied our pipeline to a vast collection of online videos, applying strict filters—selecting videos with over 1,000 views, less than 20 minutes in duration, uploaded within the last 10 years but at least 90 days old, and passing content-appropriateness checks—narrowing down to a pool of 150 million videos.

From this pool, our pipeline produced 10.3 million clip-text pairs, comprising 6.3 million unique video clips (equivalent to 17,500 hours of footage) and 970,000 distinct captions. We proudly present this new dataset as VideoCC3M.

Introducing paper: Video-to-Video Synthesis
Mono kitti

Description:

<a href="https://gts.ai/dataset-download/mono-kitti/" target="_blank">👉 Download the dataset here</a>

Mono KITTI is a specialized version of the KITTI dataset that focuses exclusively on monocular images and the corresponding distance measurements. This dataset is designed to facilitate research and development in the field of monocular absolute distance estimation, providing a rich set of data for training and evaluating machine learning models.

Download Dataset

Key Features:

Monocular Images: The dataset consists solely of images captured from a single camera, making it ideal for tasks that require monocular vision.

Distance Measurements: Each image is paired with accurate distance measurements, enabling precise absolute distance estimation.

High-Quality Data: Derived from the renowned KITTI dataset, Mono KITTI maintains high standards of image quality and accuracy.

Diverse Environments: The dataset includes a variety of scenes, from urban to rural environments, offering a comprehensive set of conditions for model training.

Dataset Composition:

Image Data: High-resolution monocular images covering diverse driving scenarios.

Distance Annotations: Accurate distance labels provided for each image, essential for absolute distance estimation tasks.

Training, Validation, and Test Sets: The data is split into well-defined training, validation, and test sets to support robust model development and evaluation.

Applications:

Monocular Distance Estimation: Ideal for developing and testing algorithms that estimate distances from monocular images.

Autonomous Driving: Supports research in autonomous driving by providing data for critical perception tasks.

Computer Vision Research: A valuable resource for advancing the state-of-the-art in monocular vision and distance estimation.

Methodology:

The distance annotations in Mono KITTI are derived using advanced techniques to ensure high accuracy. This involves leveraging stereo vision data from the original KITTI dataset to extract precise distance measurements, which are then paired with the corresponding monocular images.

This dataset is sourced from Kaggle.
High-Quality Invoice Images for OCR

dataset link : https://www.kaggle.com/datasets/osamahosamabdellatif/high-quality-invoice-images-for-ocr

Overview
High-Quality Invoice Images for OCR is a curated dataset containing professionally scanned and digitally captured invoice documents.
It is designed for training, fine-tuning, and evaluating OCR models, machine learning pipelines, and data extraction systems.

This dataset focuses on clean, structured invoices to simulate real-world scenarios in financial document automation.

What's Inside
📄 Variety of invoice templates from multiple industries (e.g., retail, manufacturing, services)

🖋️ Different currencies, tax formats, and layouts

📸 High-resolution scanned and photographed invoices

🏷️ Optional field annotations (e.g., invoice number, date, total amount, vendor name) for supervised training

Key Applications
Training and fine-tuning OCR and Document AI models

Machine learning for structured and semi-structured data extraction

Intelligent Document Processing (IDP) and Robotic Process Automation (RPA)

Benchmarking table detection, key-value extraction, and layout analysis models

Why Use This Dataset?
✅ High-quality images optimized for OCR and data extraction tasks

✅ Real-world invoice variations to improve model robustness

✅ Ideal for machine learning workflows in finance, ERP, and accounting systems

✅ Supports rapid prototyping for invoice understanding models

Ideal For
Researchers working on OCR and document understanding

Developers building invoice processing systems

Machine learning engineers fine-tuning models for data extraction

Startups and enterprises automating financial workflows

Introducing paper: DeepCPCFG: Deep Learning and Context Free Grammars for End-to-End Information Extraction
Motion-X++

In this paper, we introduce Motion-X++, a large-scale multimodal 3D expressive whole-body human motion dataset. Existing
motion datasets predominantly capture body-only poses, lacking facial expressions, hand gestures, and fine-grained pose descriptions,
and are typically limited to lab settings with manually labeled text descriptions, thereby restricting their scalability. To address this issue,
we develop a scalable annotation pipeline that can automatically capture 3D whole-body human motion and comprehensive textural
labels from RGB videos and build the Motion-X dataset comprising 81.1K text-motion pairs. Furthermore, we extend Motion-X into
Motion-X++ by improving the annotation pipeline, introducing more data modalities, and scaling up the data quantities. Motion-X++
provides 19.5M 3D whole-body pose annotations covering 120.5K motion sequences from massive scenes, 80.8K RGB videos, 45.3K
audios, 19.5M frame-level whole-body pose descriptions, and 120.5K sequence-level semantic labels. Comprehensive experiments
validate the accuracy of our annotation pipeline and highlight Motion-X++’s significant benefits for generating expressive, precise, and
natural motion with paired multimodal labels supporting several downstream tasks, including text-driven whole-body motion generation,
audio-driven motion generation, 3D whole-body human mesh recovery, and 2D whole-body keypoints estimation, etc.

Introducing paper: Motion-X++: A Large-Scale Multimodal 3D Whole-body Human Motion Dataset
CODrone

Applications of unmanned aerial vehicle (UAV) in logistics, agricultural automation, urban management, and emergency response are highly dependent on oriented object detection (OOD) to enhance visual perception. 
Although existing datasets for OOD in UAV provide valuable resources, they are often designed for specific downstream tasks.
Consequently, they exhibit limited generalization performance in real flight scenarios and fail to thoroughly demonstrate algorithm effectiveness in practical environments.
To bridge this critical gap, we introduce CODrone, a comprehensive oriented object detection dataset for UAVs that accurately reflects real-world conditions. 
It also serves as a new benchmark designed to align with downstream task requirements, ensuring greater applicability and robustness in UAV-based OOD.
Based on application requirements, we identify four key limitations in current UAV OOD datasets-low image resolution, limited object categories, single-view imaging, and restricted flight altitudes-and propose corresponding improvements to enhance their applicability and robustness.
Furthermore, CODrone contains a broad spectrum of annotated images collected from multiple cities under various lighting conditions, enhancing the realism of the benchmark. 
To rigorously evaluate CODrone as a new benchmark and gain deeper insights into the novel challenges it presents, we conduct a series of experiments based on 22 classical or SOTA methods.
Our evaluation not only assesses the effectiveness of CODrone in real-world scenarios but also highlights key bottlenecks and opportunities to advance OOD in UAV applications.
Overall, CODrone fills the data gap in OOD from UAV perspective and provides a benchmark with enhanced generalization capability, better aligning with practical applications and future algorithm development.
The dataset has been publicly released and is available at: \url{https://github.com/AHideoKuzeA/CODrone-A-Comprehensive-Oriented-Object-Detection-benchmark-for-UAV}.
ForPKG
Full name: https://github.com/luozhongze/ForPKG

A policy knowledge graph can provide decision support for tasks such as project compliance, policy analysis, and intelligent question answering, and can also serve as an external knowledge base to assist the reasoning process of related large language models. Although there have been many related works on knowledge graphs, there is currently a lack of research on the construction methods of policy knowledge graphs. This paper, focusing on the forestry field, designs a complete policy knowledge graph construction framework, including: firstly, proposing a fine-grained forestry policy domain ontology; then, proposing an unsupervised policy information extraction method, and finally, constructing a complete forestry policy knowledge graph. The experimental results show that the proposed ontology has good expressiveness and extensibility, and the policy information extraction method proposed in this paper achieves better results than other unsupervised methods. Furthermore, by analyzing the application of the knowledge graph in the retrieval-augmented-generation task of the large language models, the practical application value of the knowledge graph in the era of large language models is confirmed. The knowledge graph resource will be released on an open-source platform and can serve as the basic knowledge base for forestry policy-related intelligent systems. It can also be used for academic research. In addition, this study can provide reference and guidance for the construction of policy knowledge graphs in other fields.

Introducing paper: ForPKG: A Framework for Constructing Forestry Policy Knowledge Graph and Application Analysis
ViDAS

100 videos with varying danger levels (on a scale of 0-10) and different scenarios, annotated by 18 human annotators using our annotation pipeline to represent human perception and respective Vision Language model summaries for each of the videos as benchmarks for testing LLMs' danger perceptions.
Traffic demand in NYC and CHI

This dataset includes four real-world sub-datasets about traffic demand. Each dataset spans 16 months, from January to April of the next year.

- **NYCbike**: This dataset covers shared bike usage records in New York City from January 2022 to April 2023. Each entry represents a bike pickup and drop-off event. We divided New York into 200 grids and calculated the bike pickup and drop-off quantities for each grid every hour.

- **NYCtaxi**: This dataset covers taxi usage data from January 2018 to April 2019 in New York City, with hourly usage for each region, including both pick-up and drop-off dimensions. The division of regions is based on the scheme provided by the official website.

- **CHIbike**: The dataset contains shared bike usage records in Chicago from January 2022 to April 2023. Chicago is divided into 200 grids and hourly bike pickup and drop-off quantities for each grid are collected.

- **CHItaxi**: This dataset contains hourly taxi pick-up and drop-off values of census regions in Chicago from January 2016 to April 2017.

Introducing paper: CONTINA: Confidence Interval for Traffic Demand Prediction with Coverage Guarantee