Omkar2118/datasets huggingface dataset:
MMCU
Full name: Measuring Massive Multitask Chinese Understanding

We propose a test to measure the multitask accuracy of large Chinese language models. We constructed a large-scale, multi-task test consisting of single and multiple-choice questions from various branches of knowledge. The test encompasses the fields of medicine, law, psychology, and education, with medicine divided into 15 sub-tasks and education into 8 sub-tasks. The questions in the dataset were manually collected by professionals from freely available online resources, including university medical examinations, national unified legal professional qualification examinations, psychological counselor exams, graduate entrance examinations for psychology majors, and the Chinese National College Entrance Examination.
In total, we collected 11,900 questions, which we divided into a few-shot development set and a test set. The few-shot development set contains 5 questions per topic, amounting to 55 questions in total. The test set comprises 11,845 questions.

Introducing paper: Measuring Massive Multitask Chinese Understanding
New Plant Diseases Dataset
Full name: Image dataset containing different healthy and unhealthy crop leaves.

This dataset is recreated using offline augmentation from the original dataset. The original dataset can be found on this github repo. This dataset consists of about 87K rgb images of healthy and diseased crop leaves which is categorized into 38 different classes. The total dataset is divided into 80/20 ratio of training and validation set preserving the directory structure. A new directory containing 33 test images is created later for prediction purpose.

Introducing paper: Meta-Heuristic Based Deep Learning Model for Leaf Diseases Detection.
vReLoc

A total of 18 sequences were collected of various lengths. Since the Velodyne LiDAR, RealSense camera and Vicon motion tracker system run in different frequencies, we synchronized these systems so that the image and LiDAR in each timestamp has the same 6-DoF pose. For the static scenario, there are no moving objects in the scene. For other scenarios, there are people randomly walking in the scene. Sequences 01-10 come from the static environment, sequences 11-15 are the one-person moving scenario, and sequences 16-18 are two-persons moving scenario.

Introducing paper: PointLoc: Deep Pose Regressor for LiDAR Point Cloud Localization
DBP1M FR-EN

A large-scale cross-lingual dataset for entity alignment

Introducing paper: LargeEA: Aligning Entities for Large-scale Knowledge Graphs
IMUPoser

The **IMUPoser** Dataset is a dataset for estimating body pose using IMUs already in devices that many users own -- namely smartphones, smartwatches, and earbuds.

Source: [IMUPoser: Full-Body Pose Estimation using IMUs in Phones, Watches, and Earbuds](https://arxiv.org/pdf/2304.12518v1.pdf)

Image Source: [IMUPoser | Github Repo](https://github.com/FIGLAB/IMUPoser/blob/main/LICENSE)

Introducing paper: IMUPoser: Full-Body Pose Estimation using IMUs in Phones, Watches, and Earbuds