Omkar2118/datasets huggingface dataset:
HOPE-Image
Full name: Household Objects for Pose Estimation

The NVIDIA HOPE datasets consist of RGBD images and video sequences with labeled 6-DoF poses for 28 toy grocery objects. The toy grocery objects are readily available for purchase and have ideal size and weight for robotic manipulation. 3D textured meshes for generating synthetic training data are provided.

The HOPE-Image dataset shows the objects in 50 scenes from 10 household/office environments, and contains 188 test images taken in 8 environments, with a total of 40 scenes (unique camera and object poses). Up to 5 lighting variations are captured for each scene, including backlighting and angled direct lighting with cast shadows. Scenes are cluttered with varying levels of occlusion.

An additional 50 validation images are included from 2 environments in 10 scene arrangements.

Within each scene, up to 5 lighting variations are captured with the same camera and object poses. For example, the captures in `valid/scene_0000/*.json` all depict the same camera pose and arrangement of objects, but each individual capture (0000.json, 0001.json, ...) has a different lighting condition. For this reason, each image should be treated independently for purposes of pose prediction. The most favorable lighting condition for each scene is found in `image 0000.json`.

Images were captured using a RealSense D415 RGBD camera. Systematic errors were observed in the depth values relative to the estimated distance of a calibration grid. To correct for this, depth frames are scaled by a factor of 0.98042517 before registering to RGB. Annotations were made manually using these corrected RGBD frames.

NOTE: Only validation set annotations are included. Test annotations are managed by the BOP challenge.

Introducing paper: 6-DoF Pose Estimation of Household Objects for Robotic Manipulation: An Accessible Dataset and Benchmark
HOPE-Video
Full name: Household Objects for Pose Estimation

The HOPE-Video dataset contains 10 video sequences (2038 frames) with 5-20 objects on a tabletop scene captured by a robot arm-mounted RealSense D415 RGBD camera. In each sequence, the camera is moved to capture multiple views of a set of objects in the robotic workspace. First COLMAP was applied to refine the camera poses (keyframes at 6~fps) provided by forward kinematics and RGB calibration from RealSense to Baxter's wrist camera. 3D dense point cloud was then generated via CascadeStereo (included for each sequence in 'scene.ply'). Ground truth poses for the HOPE objects models in the world coordinate system were annotated manually using the CascadeStereo point clouds. The following are provided for each frame:

*Camera intrinsics/extrinsics
*RGB images of 640x480
*Depth images of 640x480
*3D scene reconstruction from CascadeStereo
*Object pose annotation in the camera frame

Objects consist of a set of 28 toy grocery items selected for compatibility with robot manipulation and widespread availability. Textured models were generated by an EinScan-SE 3D Scanner, units were converted to centimeters, and the centers/rotations of the meshes were aligned to a canonical pose.

Introducing paper: 6-DoF Pose Estimation of Household Objects for Robotic Manipulation: An Accessible Dataset and Benchmark
Chest x-ray landmark dataset

Set of landmark annotations for JSRT, Montgomery, Shenzhen and a subset of Padchest datasets

Introducing paper: Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis
Montgomery County X-ray Set

X-ray images in this data set have been acquired from the tuberculosis control program of the Department of Health andHuman Services of Montgomery County, MD, USA. This set contains 138 posterior-anterior x-rays, of which 80 x-rays are normal and 58 x-rays areabnormal with manifestations of tuberculosis. All images are de-identified and available in DICOM format. The set covers a wide range of abnormalities,including effusions and miliary patterns. The data set includes radiology readings available as a text files and summary of its content
Shenzhen Hospital X-ray Set

X-ray images in this data set have been collected by Shenzhen No.3 Hospital in Shenzhen, Guangdong providence,China. The x-rays were acquired as part of the routine care at Shenzhen Hospital. The set contains images in JPEG format. There are 326 normal x-raysand 336 abnormal x-rays showing various manifestations of tuberculosis.
ARQMath

The goal of ARQMath is to advance techniques for mathematical information retrieval, in particular, retrieving answers to mathematical questions (Task 1), and formula retrieval (Task 2).
Using the question posts from Math Stack Exchange, participating systems are given a question or a formula from a question and asked to return a ranked list of either potential answers to the question or potentially useful formulae (in the case of a formula query). Relevance is determined by the expected utility of each returned item. These tasks allow participating teams to explore leveraging math notation together with text to improve the quality of retrieval results.
ACDC (Adverse Conditions Dataset with Correspondences)
Full name: Adverse Conditions Dataset with Correspondences

We introduce ACDC, the Adverse Conditions Dataset with Correspondences for training and testing semantic segmentation methods on adverse visual conditions. It comprises a large set of 4006 images which are evenly distributed between fog, nighttime, rain, and snow. Each adverse-condition image comes with a high-quality fine pixel-level semantic annotation, a corresponding image of the same scene taken under normal conditions and a binary mask that distinguishes between intra-image regions of clear and uncertain semantic content.

ACDC supports two tasks:
1. standard semantic segmentation
2. uncertainty-aware semantic segmentation
RR
Full name: Review-Rebuttal

Review-Rebuttal (RR) dataset is introduced to facilitate the study of argument pair extraction in the peer review and rebuttal domain.

Introducing paper: APE: Argument Pair Extraction from Peer Review and Rebuttal via Multi-task Learning