Omkar2118/datasets huggingface dataset:
MP-3DHP: Multi-Person 3D Human Pose Dataset

Multi-Person 3D HumanPose Dataset (MP-3DHP) is a depth sensor-based dataset, which was constructed to facilitate the development of multi-person 3D pose estimation methods targeting real-world challenges. The dataset includes 177k training data and 33k validation data where both the 3D human poses and body segments are avaliable. The dataset also include 9k clean background data and 4k testing data including multi-person 3D poses.

Introducing paper: PoP-Net: Pose over Parts Network for Multi-Person 3D Pose Estimation from a Depth Image
3D Lane Synthetic Dataset

This is a synthetic dataset constructed to stimulate the development and evaluation of 3D lane detection methods.

Introducing paper: Gen-LaneNet: A Generalized and Scalable Approach for 3D Lane Detection
Yelp2018
Full name: Yelp2018

The Yelp2018 dataset is adopted from the 2018 edition of the yelp challenge. Wherein local businesses like restaurants and bars are viewed as items. We use the same 10-core setting in order to ensure data quality.

Introducing paper: Neural Graph Collaborative Filtering
CEAHB2021-5
Full name: Chinese Ethnic Ancient Handwritten Books database

Ancient books script identification of Chinese ethnic minorities with deep convolutional neural networks via multi-branch and spatial pyramid pooling

Automatic classification of ancient books is an important component of the digital platform of ancient books. In view of the ancient books script identification task of different ethnic minorities in China, we build a dataset of Chinese ethnic ancient handwritten books(Tai Le, Tibet, Naxi, Yi, Shui), crop and standardize preprocessing images of ancient books. 

Source:[CEAHB2021-5](https://github.com/yddcode/CEAHB2021-5)
TLHDIBD2021
Full name: Tai Le historical document image binarization dataset

Hybrid-CBF: A hybrid classification and binarization framework for historical Tai Le document image binarization
The binarization of historical documents is very important and more challenging than the binarization of ordinary documents. As a result of the serious noise pollution found on the historical Tai Le documents, a new hybrid classification and binarization framework (Hybrid-CBF) is proposed for the binarization of historical Tai Le document images. The Tai Le historical document image binarization dataset (TLHDIBD2021) containing 2,780 image pairs is constructed. Due to the different degrees of document background pollution, the single method has a poor effect on the binarization of historical Tai Le documents. First, Hybrid-CBF clusters the historical Tai Le document images according to the noise level estimation to obtain document images with different noise levels. Second, the corresponding optimal binarization method is used for historical Tai Le documents with different noise levels. In Hybrid-CBF, two binarization methods of historical Tai Le documents based on a deep neural network are proposed.

Source:[TLHDIBD2021](https://github.com/yddcode/TLHDIBD2021)
WDC LSPM

Many e-shops have started to mark-up product data within their HTML pages using the schema.org vocabulary. The Web Data Commons project regularly extracts such data from the Common Crawl, a large public web crawl. The Web Data Commons Training and Test Sets for Large-Scale Product Matching contain product offers from different e-shops in the form of binary product pairs (with corresponding label "match" or "no match") for four product categories, computers, cameras, watches and shoes. 

In order to support the evaluation of machine learning-based matching methods, the data is split into training, validation and test sets. For each product category, we provide training sets in four different sizes (2.000-70.000 pairs). Furthermore there are sets of ids for each training set for a possible validation split (stratified random draw) available. The test set for each product category consists of 1.100 product pairs. The labels of the test sets were manually checked while those of the training sets were derived using shared product identifiers from the Web via weak supervision. 

The data stems from the WDC Product Data Corpus for Large-Scale Product Matching - Version 2.0 which consists of 26 million product offers originating from 79 thousand websites.