Precise recognition of soybean pods is a crucial need for acquiring phenotypic characteristics, such as the number of productive pods and the quantity of seeds per plant. There exist several techniques for counting seeds, each with their own boundaries. An automated procedure, such as a machine learning algorithm, that takes a image as input and outputs the discrete count of a certain object of interest in the image, canbe used for this type of work.

Health is a growing concern in modern society, and monitoring physiological indicators is an important part of maintaining health. Traditional health monitoring methods often require the use of contact sensors to monitor the human body, which is less convenient and comfortable, and often only measures relatively single physiological indicators, such as heart rate and blood oxygen. Traditional monitoring methods require complex instrumentation and sampling processes that require manual intervention, which is impractical for routine testing.

This database contains Synthetic High-Voltage Power Line Insulator Images.

There are two sets of images: one for image segmentation and another for image classification.

The first set contains images with different types of materials and landscapes, including the following landscape types: Mountains, Forest, Desert, City, Stream, Plantation. Each of the above-mentioned landscape types consists of 2,627 images per insulator type, which can be Ceramic, Polymeric or made of Glass, with a total of 47,286 distinct images.

This dataset encapsulates a comprehensive collection of eye movement recordings captured during sleep, exceeding 100 distinct episodes. The recordings are primarily categorized into Rapid Eye Movement (REM), Slow Eye Movement (SEM), and non-movement phases, providing a rich resource for sleep research. Each video is meticulously recorded in high-definition .mp4 format, ensuring clarity and precision in capturing subtle ocular dynamics.