Omkar2118/datasets huggingface dataset:
CWD30
Full name: Crop Weed Dataset 30 species

CWD30 comprises over 219,770 high-resolution images of 20 weed species and 10 crop species, encompassing various growth stages, multiple viewing angles, and environmental conditions. The images were collected from diverse agricultural fields across different geographic locations and seasons, ensuring a representative dataset.

Introducing paper: CWD30: A Comprehensive and Holistic Dataset for Crop Weed Recognition in Precision Agriculture
HAC
Full name: Hybrid Adverse Conditions

**HAC** is a dataset for learning and benchmarking arbitrary Hybrid Adverse Conditions restoration. HAC contains 31 scenarios composed of an arbitrary combination of five common weather, with a total of 316K adverse-weather/clean pairs.

Source: [Restoring Images Captured in Arbitrary Hybrid Adverse Weather Conditions in One Go](https://arxiv.org/pdf/2305.09996v1.pdf)

Image Source: [Restoring Images Captured in Arbitrary Hybrid Adverse Weather Conditions in One Go](https://arxiv.org/pdf/2305.09996v1.pdf)

Introducing paper: Restoring Images Captured in Arbitrary Hybrid Adverse Weather Conditions in One Go
M3KE
Full name: Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark

**M3KE** is a Massive Multi-Level Multi-Subject Knowledge Evaluation benchmark, which is developed to measure knowledge acquired by Chinese large language models by testing their multitask accuracy in zero- and few-shot settings. We have collected 20,477 questions from 71 tasks. Our selection covers all major levels of Chinese education system, ranging from the primary school to college, as well as a wide variety of subjects, including humanities, history, politics, law, education, psychology, science, technology, art and religion. All questions are multiple-choice questions with four options, hence guaranteeing a standardized and unified assessment process.

Source: [M3KE: A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language Models](https://arxiv.org/pdf/2305.10263v1.pdf)

Image Source: [M3KE: A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language Models](https://arxiv.org/pdf/2305.10263v1.pdf)

Introducing paper: M3KE: A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language Models
Morphological Classification of Galaxies

Dataset can be used by anyone who is interested to perform morphological classification of galaxies. Originally dataset provided by Kaggle user Jay Lin (https://www.kaggle.com/jay1985) 4 years ago. Dataset was used in conference paper "Morphological Classification of Galaxies Using SpinalNet"

Introducing paper: Morphological Classification of Galaxies Using SpinalNet
ATMs fault prediction

The collected dataset consists of multivariate time series (MTS) data belonging to several ATMs banking along with the annotations that the operators did when they performed a maintenance task on any of the machines.

Each sample is a MTS with 144 points, associated with all the 10-minute time windows of that day and 38 dimensions. Each dimension is related to a command type and response type, where the value of each point in the time series represents the number of occurrences of the associated command and response since the last failure event., i.e. in this cycle of failure. Therefore, the time series value is accumulated until the next cycle begins. Additionally, some extra information is included for each sample, such as the cycle of failure and the machine identifier, which can be used to create data partitions without mixing different machines.

The dataset and the labels assigned to each sample was used in the original work to perform a binary classification problem addressed by ML techniques. The goal of the problem was to predict whether a failure will occur within the next 7 days, using only the information from the current day (accumulated since the last error), which is based on an event-log. 

Potential use cases of the dataset:
•	multivariate time series classification-regression-forecasting methodologies;
•	feature learning- feature extraction approaches; 
•	predictive maintenance tasks: failure classification, failure prediction, anomaly detection.

Introducing paper: A hybrid feature learning approach based on convolutional kernels for ATM fault prediction using event-log data