Omkar2118/datasets huggingface dataset:
UR5 Tool Dataset

In this dataset UR5 robot used 6 tools: metal-scissor, metal-whisk, plastic-knife, plastic-spoon, wooden-chopstick, and wooden-fork to perform 6 behaviors: look, stirring-slow, stirring-fast, stirring-twist, whisk, and poke. The robot explored 15 objects: cane-sugar, chia-seed, chickpea, detergent, empty, glass-bead, kidney-bean, metal-nut-bolt, plastic-bead, salt, split-green-pea, styrofoam-bead, water, wheat, and wooden-button kept cylindrical containers. The robot performed 10 trials on each object using a tool, resulting in 5,400 interactions (6 tools x 6 behaviors x 15 objects x 10 trials). The robot records multiple sensory data (audio, RGB images, depth images, haptic, and touch images) while interacting with the objects.

Introducing paper: Cross-Tool and Cross-Behavior Perceptual Knowledge Transfer for Grounded Object Recognition
3DOH50K

3DOH50K is the first real 3D human dataset for the problem of human reconstruction and pose estimation in occlusion scenarios. It contains 51600 images with accurate 2D pose and 3D pose, SMPL parameters, and binary mask.

Introducing paper: Object-Occluded Human Shape and Pose Estimation From a Single Color Image
Jung

Dataset for document shadow removal

Introducing paper: Water-Filling: An Efficient Algorithm for Digitized Document Shadow Removal
VNAT
Full name: VPN/NONVPN NETWORK APPLICATION TRAFFIC DATASET

This dataset is a collection of labelled PCAP files, both encrypted and unencrypted, across 10 applications, as well as a pandas dataframe in HDF5 format containing detailed metadata summarizing the connections from those files. It was created to assist the development of machine learning tools that would allow operators to see the traffic categories of both encrypted and unencrypted traffic flows. In particular, features of the network packet traffic timing and size information (both inside of and outside of the VPN) can be leveraged to predict the application category that generated the traffic.

Introducing paper: Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification
Kligler

Dataset for document shadow removal

Introducing paper: Document Enhancement Using Visibility Detection
AutoFR Dataset

**AutoFR Dataset** is broken down by each site that we crawl within a zip file. It contains multiple different experiments that we conducted in our paper. The overall dataset contains 1042 sites that we crawled where we detected ads within the Top-5K.

Source: [AutoFR Dataset](https://athinagroup.eng.uci.edu/projects/ats-on-the-web/autofr-dataset/)

Introducing paper: AutoFR: Automated Filter Rule Generation for Adblocking
FMD (materials)
Full name: Flickr Material Dataset

Sharan, Lavanya, Ruth Rosenholtz, and Edward Adelson.
     "Material perception: What can you see in a brief glance?."
     Journal of Vision 9.8 (2009): 784-784.   
     http://people.csail.mit.edu/celiu/CVPR2010/FMD/FMD.zip