Omkar2118/datasets huggingface dataset:
TUM-VIE
Full name: TUM Stereo Visual-Inertial Event Dataset

**TUM-VIE** is an event camera dataset for developing 3D perception and navigation algorithms. It contains handheld and head-mounted sequences in indoor and outdoor environments with rapid motion during sports and high dynamic range. TUM-VIE includes challenging sequences where state-of-the art VIO fails or results in large drift. Hence, it can help to push the boundary on event-based visual-inertial algorithms.

Introducing paper: TUM-VIE: The TUM Stereo Visual-Inertial Event Dataset
AutoChart

**AutoChart** is a dataset for chart-to-text generation, a task that consists on generating analytical descriptions of visual plots.

Introducing paper: AutoChart: A Dataset for Chart-to-Text Generation Task
DAHLIA
Full name: DAily Human Life Activity

DAHLIA dataset [1] is devoted to human activity recognition, which is a major issue for adapting smart-home services such as user assistance.
DAHLIA has been realized in Mobile Mii Platform by CEA LIST, and has been partly supported by ITEA 3 Emospaces Project (https://itea3.org/project/emospaces.html)

Videos were recorded in realistic conditions, with 3 Kinect v2 sensors located as they would be in a real context. The long-range activities were performed in an unconstrained way (participants received only few instructions), and in a continuous (untrimmed) sequence, resulting in long videos (40 min in average per subject). Contrary to previously published databases, in which labeled actions are very short and have low-semantic level, this new database focuses on high-level semantic activities such as « Preparing lunch » or « House Working ».

[1] G. Vaquette, A. Orcesi, L. Lucat and C. Achard, "The DAily Home LIfe Activity Dataset: A High Semantic Activity Dataset for Online Recognition," 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), 2017, pp. 497-504, doi: 10.1109/FG.2017.67.
PIDray

**PIDray** is a large-scale dataset which covers various cases in real-world scenarios for prohibited item detection, especially for deliberately hidden items. The dataset contains 12 categories of prohibited items in 47, 677 X-ray images with high-quality annotated segmentation masks and bounding boxes.

Introducing paper: Towards Real-World Prohibited Item Detection: A Large-Scale X-ray Benchmark
CallOptionBSM

This dataset collects 88,077 numerical samples of call options on Shanghai Stock Exchange from 2015-02 to 2020-07.  After the pre-processing, 83,427 samples remain in the data set. This data set records only original quotation of call options on Shanghai
Stock Exchange, and does not include derivative indicators published by stock brokerage firms.
Dataset to "Easing the Conscience with OPC UA: An Internet-Wide Study on Insecure Deployments"

This is the dataset to "Easing the Conscience with OPC UA: An Internet-Wide Study on Insecure Deployments" [In ACM Internet Measurement Conference (IMC ’20)]. It contains our weekly scanning results between 2020-02-09 and 2020-08-31 complied using our zgrab2 extensions, i.e, it contains an Internet-wide view on OPC UA deployments and their security configurations. To compile the dataset, we anonymized the output of zgrab2, i.e., we removed host and network identifiers from that dataset. More precisely, we mapped all IP addresses, fully qualified hostnames, and autonomous system IDs to numbers as well as removed certificates containing any identifiers. See the README file for more information. Using this dataset we showed that 93% of Internet-facing OPC UA deployments have problematic security configurations, e.g., missing access control (on 24% of hosts), disabled security functionality (24%), or use of deprecated cryptographic primitives (25%). Furthermore, we discover several hundred devices in multiple autonomous systems sharing the same security certificate, opening the door for impersonation attacks. Overall, with the analysis of this dataset we underpinned that secure protocols, in general, are no guarantee for secure deployments if they need to be configured correctly following regularly updated guidelines that account for basic primitives losing their security promises.

Introducing paper: Easing the Conscience with OPC UA: An Internet-Wide Study on Insecure Deployments
MHMD
Full name: Modern Historical Movies Dataset

**MHMD** (Modern Historical Movies Dataset) is a dataset for old image colorization, built from historical movies. It consists of 1,353,166 images and 42 labels of eras, nationalities, and garment types for automatic colorization from 147 historical movies or TV series made in modern time.

Introducing paper: Focusing on Persons: Colorizing Old Images Learning from Modern Historical Movies
DensePASS

DensePASS - a novel densely annotated dataset for panoramic segmentation under cross-domain conditions, specifically built to study the Pinhole-to-Panoramic transfer and accompanied with pinhole camera training examples obtained from Cityscapes. DensePASS covers both, labelled- and unlabelled 360-degree images, with the labelled data comprising 19 classes which explicitly fit the categories available in the source domain (i.e. pinhole) data.

Introducing paper: DensePASS: Dense Panoramic Semantic Segmentation via Unsupervised Domain Adaptation with Attention-Augmented Context Exchange