Omkar2118/datasets huggingface dataset:
TIC Powerline Dataset
Full name: Tower Insulator Conductors Dataset for high voltage powerline inspection

The TIC Dataset consists of 2056 images (512x640) of transmission line network footage in Greece (Northeast Attica) and annotations of three object classes, i.e. towers, insulators and conductors. The dataset file contains the following: i. train dataset folder ("train"):

-"Images" file with 1483 images and a JSON file with corresponding annotations (polygons).

-"Labels" file with txt annotation files (YOLO format) for each train image.

ii. validation dataset folder ("valid"):

-"Images" file with 366 images and a JSON filewith corresponding annotations (polygons).

-"Labels" file with txt annotation files (YOLO format) for each train image.

iii. Test dataset folder ("test")":

-"Images" file with 207 images and a JSON file with corresponding annotations (polygons).

Introducing paper: Aerial Inspection of High-Voltage Power Lines Using YOLOv8 Real-Time Object Detector
TURSpider: A Turkish Text-to-SQL Dataset

TURSpider is a novel Turkish Text-to-SQL dataset that includes complex queries, akin to those in the original Spider dataset. TURSpider dataset comprises two main subsets: a dev set and a training set, aligned with the structure and scale of the popular Spider dataset. The dev set contains 1034 data rows with 1023 unique questions and 584 distinct SQL queries. In the training set, there are 8659 data rows, 8506 unique questions, and corresponding SQL queries.
RawRipe Dataset
Full name: Fruit Maturity Recognition from Agricultural, Market and Automation Perspectives, IECON'21

The dataset comprises of images of 10 types of fruits in both raw and ripe states. 

The fruit-types include: apple, banana, coconut, gauva, litchi, mango, orange, papaya, pomegranate and strawberry. 

The dataset can be used two solve three types classification problem:

1) raw vs ripe for a specific fruit (agricultural perspective)

2) raw vs ripe for any fruit (market perspective)

3) multi-class/label classification (automation perspective) 

For more details, refer to the following paper:

Fruit Maturity Recognition from Agricultural, Market and Automation Perspectives, IECON'21 

Paper link: https://ieeexplore.ieee.org/document/9589215 

Please cite the paper if you use the dataset in your projects/papers.

@INPROCEEDINGS{9589215,

  author={Rao Jerripothula, Koteswar and Kumar Shukla, Sarvesh and Jain, Samyak and Singh, Shudhanshu},

  booktitle={IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society},

  title={Fruit Maturity Recognition from Agricultural, Market and Automation Perspectives},

  year={2021},

  pages={1-6},

  doi={10.1109/IECON48115.2021.9589215}

}