Omkar2118/datasets huggingface dataset:
InScope

Perception systems of autonomous vehicles are susceptible to occlusion, especially when examined from a vehicle-centric perspective. Such occlusion can lead to overlooked object detections, e.g., larger vehicles such as trucks or buses may create blind spots where cyclists or pedestrians could be obscured, accentuating the safety concerns associated with such perception system limitations. To mitigate these challenges, the vehicle-to-everything (V2X) paradigm suggests employing an infrastructure-side perception system (IPS) to complement autonomous vehicles with a broader perceptual scope. Nevertheless, the scarcity of real-world 3D infrastructure-side datasets constrains the advancement of V2X technologies. To bridge these gaps, this paper introduces a new 3D infrastructure-side collaborative perception dataset, abbreviated as inscope. Notably, InScope is the first dataset dedicated to addressing occlusion challenges by strategically deploying multiple-position Light Detection and Ranging (LiDAR) systems on the infrastructure side. Specifically, InScope encapsulates a 20-day capture duration with 303 tracking trajectories and 187,787 3D bounding boxes annotated by experts. Through analysis of benchmarks, four different benchmarks are presented for open traffic scenarios, including collaborative 3D object detection, multisource data fusion, data domain transfer, and 3D multiobject tracking tasks. Additionally, a new metric is designed to quantify the impact of occlusion, facilitating the evaluation of detection degradation ratios among various algorithms. The Experimental findings showcase the enhanced performance of leveraging InScope to assist in detecting and tracking 3D multiobjects in real-world scenarios, particularly in tracking obscured, small, and distant objects. The dataset and benchmarks are available at https://github.com/xf-zh/InScope.
RoboRefIt

We contribute a new challenging visual grounding dataset for robotic perception and reasoning in indoor environments, called RoboRefIt. The RoboRefIt collects 10,872 real-world RGB and depth images from cluttered daily life scenes, and generates 50,758 referring expressions in the form of robot language instructions. Moreover, nearly half of the images involve ambiguous object recognition. We hope that the RoboRefIt provides a distinctive training bed of visual grounding tasks for the robot interactive grasp.

Introducing paper: VL-Grasp: a 6-Dof Interactive Grasp Policy for Language-Oriented Objects in Cluttered Indoor Scenes
CROHME 2016

Source: [ICFHR2016 CROHME: Competition on Recognition of Online Handwritten Mathematical Expressions](https://www.semanticscholar.org/paper/ICFHR2016-CROHME%3A-Competition-on-Recognition-of-Mouch%C3%A8re-Viard-Gaudin/47d5053e651e8fee54b0d2fee6424385dad5beab?sort=total-citations)
CROHME 2023

Source: [CROHME 2023](https://crohme2023.ltu-ai.dev/)
CROHME 2014

* Benchmark for HMER and OHMER
Source: [CROHME 2014](https://ieeexplore.ieee.org/document/6981117)
CROHME 2019

Source: [ICDAR 2019 CROHME + TFD: Competition on Recognition of Handwritten Mathematical Expressions and Typeset Formula Detection](https://ieeexplore.ieee.org/document/8978036)
MLHME-38K

Source: [MLHME-38K](https://ai.100tal.com/icdar)
HME100K

Source: [HME100K](https://github.com/Phymond/HME100K)