Omkar2118/datasets huggingface dataset:
JHU CoSTAR Block Stacking Dataset

Involves data where a robot interacts with 5.1 cm colored blocks to complete an order-fulfillment style block stacking task. It contains dynamic scenes and real time-series data in a less constrained environment than comparable datasets. There are nearly 12,000 stacking attempts and over 2 million frames of real data. 

Source: [The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints](/paper/training-frankensteins-creature-to-stack)

Introducing paper: The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints
JHU-CROWD

(JHU-CROWD) a crowd counting dataset that contains 4,250 images with 1.11 million annotations. This dataset is collected under a variety of diverse scenarios and environmental conditions. Specifically, the dataset includes several images with weather-based degradations and illumination variations in addition to many distractor images, making it a very challenging dataset. Additionally, the dataset consists of rich annotations at both image-level and head-level.

Introducing paper: Pushing the Frontiers of Unconstrained Crowd Counting: New Dataset and Benchmark Method
JIT Dataset
Full name: Jejueo Interview Transcripts

The **Jejueo Interview Transcripts** (JIT) dataset is a parallel corpus containing 170k+ Jejueo-Korean sentences.

Source: [https://arxiv.org/abs/1911.12071](https://arxiv.org/abs/1911.12071)

Introducing paper: Jejueo Datasets for Machine Translation and Speech Synthesis
JRDB
Full name: JackRabbot Dataset and Benchmark

A novel egocentric dataset collected from social mobile manipulator JackRabbot. The dataset includes 64 minutes of annotated multimodal sensor data including stereo cylindrical 360 degrees RGB video at 15 fps, 3D point clouds from two Velodyne 16 Lidars, line 3D point clouds from two Sick Lidars, audio signal, RGB-D video at 30 fps, 360 degrees spherical image from a fisheye camera and encoder values from the robot's wheels.

Source: [JRDB: A Dataset and Benchmark of Egocentric Visual Perception for Navigation in Human Environments](/paper/jrdb-a-dataset-and-benchmark-for-visual)

Introducing paper: JRDB: A Dataset and Benchmark of Egocentric Robot Visual Perception of Humans in Built Environments
JSUT Corpus

JSUT Corpus is a free large-scale speech corpus that can be shared between academic institutions and commercial companies has an important role. However, such a corpus for Japanese speech synthesis does not exist.

Source: [JSUT corpus: free large-scale Japanese speech corpus for end-to-end speech synthesis](/paper/jsut-corpus-free-large-scale-japanese-speech)

Introducing paper: JSUT corpus: free large-scale Japanese speech corpus for end-to-end speech synthesis
JTA
Full name: Joint Track Auto

JTA is a dataset for people tracking in urban scenarios by exploiting a photorealistic videogame. It is up to now the vastest dataset (about 500.000 frames, almost 10 million body poses) of human body parts for people tracking in urban scenarios. 

Source: [Learning to Detect and Track Visible and Occluded Body Joints in a Virtual World](/paper/learning-to-detect-and-track-visible-and)

Introducing paper: Learning to Detect and Track Visible and Occluded Body Joints in a Virtual World
KAIST Multispectral Pedestrian Detection Benchmark

KAIST Multispectral Pedestrian Dataset

The KAIST Multispectral Pedestrian Dataset is imaging hardware consisting of a color camera, a thermal camera and a beam splitter to capture the aligned multispectral (RGB color + Thermal) images. With this hardware, we captured various regular traffic scenes at day and night time to consider changes in light conditions. and, consists of 95k color-thermal pairs (640x480, 20Hz) taken from a vehicle. All the pairs are manually annotated (person, people, cyclist) for the total of 103,128 dense annotations and 1,182 unique pedestrians. The annotation includes temporal correspondence between bounding boxes like Caltech Pedestrian Dataset.

For more information, read [Multispectral Pedestrian Detection: Benchmark Dataset and Baseline (CVPR 2015)](https://openaccess.thecvf.com/content_cvpr_2015/papers/Hwang_Multispectral_Pedestrian_Detection_2015_CVPR_paper.pdf) or visit [this website](https://soonminhwang.github.io/rgbt-ped-detection/)
Kannada-MNIST

The **Kannada-MNIST** dataset is a drop-in substitute for the standard MNIST dataset for the Kannada language.

Source: [https://github.com/vinayprabhu/Kannada_MNIST](https://github.com/vinayprabhu/Kannada_MNIST)
Image Source: [https://github.com/vinayprabhu/Kannada_MNIST](https://github.com/vinayprabhu/Kannada_MNIST)

Introducing paper: Kannada-MNIST: A new handwritten digits dataset for the Kannada language
KaoKore

Consists of faces extracted from pre-modern Japanese artwork.

Source: [KaoKore: A Pre-modern Japanese Art Facial Expression Dataset](/paper/kaokore-a-pre-modern-japanese-art-facial)

Introducing paper: KaoKore: A Pre-modern Japanese Art Facial Expression Dataset
KaWAT

A new word analogy task dataset for Indonesian.

Source: [KaWAT: A Word Analogy Task Dataset for Indonesian](/paper/kawat-a-word-analogy-task-dataset-for)

Introducing paper: KaWAT: A Word Analogy Task Dataset for Indonesian
KELM

KELM is a large-scale synthetic corpus of Wikidata KG as natural text.

Source: [KELM](https://github.com/google-research-datasets/KELM-corpus)

Introducing paper: Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training