Omkar2118/datasets huggingface dataset:
HO-3D v2

A hand-object interaction dataset with 3D pose annotations of hand and object. The dataset contains 66,034 training images and 11,524 test images from a total of 68 sequences. The sequences are captured in multi-camera and single-camera setups and contain 10 different subjects manipulating 10 different objects from YCB dataset. The annotations are automatically obtained using an optimization algorithm. The hand pose annotations for the test set are withheld and the accuracy of the algorithms on the test set can be evaluated with standard metrics using the CodaLab challenge submission(see project page). The object pose annotations for the test and train set are provided along with the dataset.

Introducing paper: HOnnotate: A method for 3D Annotation of Hand and Object Poses
Retailrocket

The dataset consists of three files: a file with behaviour data (events.csv), a file with item properties (itemproperties.сsv) and a file, which describes category tree (categorytree.сsv). The data has been collected from a real-world ecommerce website. It is raw data, i.e. without any content transformations, however, all values are hashed due to confidential issues. The purpose of publishing is to motivate researches in the field of recommender systems with implicit feedback.