Omkar2118/datasets huggingface dataset:
MFAQ

** MFAQ** is a multilingual FAQ dataset publicly available. It contains around 6M FAQ pairs from the web, in 21 different languages. Although this is significantly larger than existing FAQ retrieval datasets, it comes with its own challenges: duplication of content and uneven distribution of topics.

Introducing paper: MFAQ: a Multilingual FAQ Dataset
JDDC 2.0

**JDDC 2.0** is a large-scale multimodal multi-turn dialogue dataset collected from a mainstream Chinese E-commerce platform JD.com, containing about 246 thousand dialogue sessions, 3 million utterances, and 507 thousand images, along with product knowledge bases and image category annotations. The dataset is divided into the training set, the validation set, and the test set according to the ratio of 80%, 10%, and 10%.

Introducing paper: The JDDC 2.0 Corpus: A Large-Scale Multimodal Multi-Turn Chinese Dialogue Dataset for E-commerce Customer Service
RAFT
Full name: Realworld Annotated Few-shot Tasks

The RAFT benchmark (Realworld Annotated Few-shot Tasks) focuses on naturally occurring tasks and uses an evaluation setup that mirrors deployment.

RAFT is a few-shot classification benchmark that tests language models:

- across multiple domains (lit reviews, medical data, tweets, customer interaction, etc.)
- on economically valuable classification tasks (someone inherently cares about the task)
- with evaluation that mirrors deployment (50 labeled examples per task, info retrieval allowed, hidden test set)

Description from: [https://raft.elicit.org/](https://raft.elicit.org/)

Image source: [https://raft.elicit.org/](https://raft.elicit.org/)

Introducing paper: RAFT: A Real-World Few-Shot Text Classification Benchmark
JARVIS-DFT

JARVIS-DFT is a repository of density functional theory based calculation data for materials.

Introducing paper: The Joint Automated Repository for Various Integrated Simulations (JARVIS) for data-driven materials design
MiniHack

MiniHack is a sandbox framework for easily designing rich and diverse environments for Reinforcement Learning (RL). MiniHack includes a collection of example environments that can be used to test various capabilities of RL agents, as well as serve as building blocks for researchers wishing to develop their own environments. MiniHack's navigation tasks challenge the agent to reach the goal position by overcoming various difficulties on their way, such as fighting monsters in corridors, crossing a river by pushing boulders into it, navigating through complex, procedurally generated mazes, etc. MiniHack's skill acquisition tasks enable utilising the rich diversity of NetHack objects, monsters and dungeon features, and the interactions between them. The skill acquisition tasks feature a large action space (75 actions), where the actions are instantiated differently depending on which object they are acting on.

Introducing paper: MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research
SCIMAT

**SCIMAT** is a large question-answer dataset for mathematics and science problems; such dataset can have impact on online education, intelligent tutoring and automated grading.

Introducing paper: SCIMAT: Science and Mathematics Dataset
iShape

**iShape** is an irregular shape dataset for instance segmentation. iShape contains six sub-datasets with one real and five synthetics, each represents a scene of a typical irregular shape.

Introducing paper: iShape: A First Step Towards Irregular Shape Instance Segmentation
Riedones3D

**Riedones3D** is a dataset of 2,070 scans of coins. With this dataset, the authors propose two benchmarks, one for point cloud registration, essential for coin die recognition, and a benchmark of coin die clustering

Introducing paper: Riedones3D: a celtic coin dataset for registration and fine-grained clustering
Contextualised Polyseme Word Sense Dataset v2

This is a revised and extended second version of a Contextualised Polyseme Word Sense Dataset. The dataset contains two human annotated measures of word sense similarity for polysemic target words used in contexts invoking different sense interpretations. The first set contains graded similarity judgements for highlighted target words displayed in two different contexts. The second set contains co-predication acceptability judgements for sentence constructions combining the sentence pairs from the first set.

Introducing paper: Patterns of Lexical Ambiguity in Contextualised Language Models
DVSMOTION20

This dataset is designed to enhance the progress of event-based optical flow algorithms. The data was collected using the IniVation DAViS346 camera, which has a 346 x 260 spatial resolution. The dataset is classified into camera motion data (stationary scene and moving camera) and object motion data (stationary camera and moving objects). The camera motion data contains four real indoor sequences (namely, checkerboard, classroom, conference room, and conference room translation) with ground truth motion inferred from IMU. The movement of the camera in this category was restricted by a gimbal, and the IMU was calibrated before each collection.  The object motion data includes two real sequences (called hands and cars) containing multiple object motions. This category does not have ground-truth motion since the object motion cannot be inferred from IMU.

Introducing paper: Distance Surface for Event-Based Optical Flow
BKAI-IGH NeoPolyp-Small

This dataset contains 1200 images (1000 WLI images and 200 FICE images) with fine-grained segmentation annotations. The training set consists of 1000 images, and the test set consists of 200 images. All polyps are classified into neoplastic or non-neoplastic classes denoted by red and green colors, respectively.  This dataset is a part of a bigger dataset called NeoPolyp.

Introducing paper: NeoUNet: Towards accurate colon polyp segmentation and neoplasm detection
OV

# Description
OV dataset is the camera calibration dataset. There are 16 lenses ranging from 90° to 180° FOV:

* 2012-A0 (single-plane target)
* 3136-H0 (single-plane target)
* 5501-C4 (single-plane target)
* 130108MP (single-plane target)
* ov00—ov07 (corner target)
* ov00—ov03 (cube target)

Note: We also provide the other datasets evaluated in the [BabelCalib](https://arxiv.org/abs/2109.09704) paper: [Kalibr](https://vision.in.tum.de/research/vslam/double-sphere), [OCamCalib](https://sites.google.com/site/scarabotix/ocamcalib-toolbox) and [UZH](https://fpv.ifi.uzh.ch/datasets/).

# Format
The datasets have a Deltille format. The [Deltille detector](https://github.com/facebookarchive/deltille) is a robust deltille and checkerboard detector. It comes with detector library, example detector code, and MATLAB bindings. [BabelCalib](https://ylochman.github.io/babelcalib) provides functions for calibration and evaluation using the Deltille software's outputs. Calibration from Deltille detections requires format conversion which is peformed by [`import_ODT`](https://github.com/ylochman/babelcalib/blob/main/core/feature/import_ODT.m). Please see a complete [calibration example](https://github.com/ylochman/babelcalib/blob/main/calib_run_opt2.m) from Deltille data.

Introducing paper: BabelCalib: A Universal Approach to Calibrating Central Cameras
Corn Seeds Dataset

This dataset is the images of corn seeds considering the top and bottom view independently (two images for one corn seed: top and bottom). There are four classes of the corn seed (Broken-B, Discolored-D, Silkcut-S, and Pure-P) 17802 images are labeled by the experts at the AdTech Corp. and 26K images were unlabeled out of which 9k images were labeled using the Active Learning (BatchBALD)

We have created three different dataset: (1). Primary dataset: contains the 17802 images labeled by the experts. Top-view(8901) and Bottom-view(8901).

(2). Dataset with fake images: We generated fake images using Conditional GAN (BigGAN) as follows: broken-2937, discolored-5823, pure-2937, silkcut-5823 instances and added them into the train set to balance the data set.

(3). Balanced dataset: In this case of adding newly captured images labeled using the Batch Active Learning method, new 9000 labeled images are added into the primary dataset. This new dataset contains 26,802 images split into train and validation set 80: 20, respectively. Contains the 17802 images and the 9K images labeled by the Active Learning (BatchBALD).
CaDIS
Full name: Cataract Dataset for Image Segmentation

CaDIS: a Cataract Dataset for Image Segmentation is a dataset for semantic segmentation created by Digital Surgery Ltd. on top of the CATARACTS dataset. CaDIS consists of 4670 images sampled from the 25 videos on CATARACTS' training set. Each pixel in each image is labeled with its respective instrument or anatomical class from a set of 36 identified classes. More details about the dataset could be found in the paper (https://arxiv.org/pdf/1906.11586.pdf).

Introducing paper: CaDIS: Cataract Dataset for Image Segmentation
!Optimizer 2021 Data

The data used for !Optimizer 2021 competition, based on seven biological model organisms.

Introducing paper: Multi-Feasibility Variable Selection
Galaxy Zoo DECaLS

Approx. 300,000 images of galaxies labelled by shape.

Labels are from www.galaxyzoo.org volunteers, and are noisy.
Images are from the DECaLS telescope survey.
Also includes predictions from an ensemble of EfficientNets, each using MC Dropout and a novel probabilistic loss function.

Introducing paper: Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies
RNADesign

An environment for RNA design given structure constraints with structures from different datasets to choose from.

Introducing paper: Learning to Design RNA