Omkar2118/datasets huggingface dataset:
iDesigner
Full name: iDesigner

Fashion trends are constantly evolving, but a trained eye can estimate with some accuracy the signature elements of a particular designer's style.

50,000 runway images, spanning 50 fashion designers, from our large repository of proprietary front row images from fashion shows over the past 15 years. The data includes a variety of fashion items, including: shoes, bags, dress, jackets etc.
UnrealEgo

**UnrealEgo** is a dataset that provides in-the-wild stereo images with a large variety of motions for 3D human pose estimation. The in-the-wild stereo images are stereo fisheye images and depth maps with a resolution of 1024×1024 pixels each with 25 frames per second and a total of 450k  (900k images) are captured for the dataset. Metadata is provided for each frame, including 3D joint positions, camera positions, and 2D coordinates of reprojected joint positions in the fisheye views.

Introducing paper: UnrealEgo: A New Dataset for Robust Egocentric 3D Human Motion Capture
OmniCity

**OmniCity** is a dataset for omnipotent city understanding from multi-level and multi-view images. It contains multi-view satellite images as well as street-level panorama and mono-view images, constituting over 100K pixel-wise annotated images that are well-aligned and collected from 25K geo-locations in New York City. This dataset introduces a new task of fine-grained building instance segmentation on street-level panorama images. It also provides new problem settings for existing tasks, such as cross-view image matching, synthesis, segmentation, detection, etc., and facilitates the developing of new methods for large-scale city understanding, reconstruction, and simulation.

Introducing paper: OmniCity: Omnipotent City Understanding with Multi-level and Multi-view Images