Image colorization is the process of adding color to grayscale or black-and-white images to make them appear as if they were originally captured or created in color. This technique is commonly used in various applications, such as restoring historical photographs, enhancing visual aesthetics, and improving the visual quality of images.

Globose Technology Solutions excels in bird species identification with the BIRDS 525 SPECIES dataset. Our advanced algorithms aid in studying birds, monitoring their diversity, and contributing to environmental conservation efforts.

Ecuadorian Facial Expressions (EFE) can add a special touch to your AI projects. Our AI data collection company is committed to giving you the resources and knowledge to use these expressions well.

The HAM10000 dataset is a publicly available dataset used in the field of dermatology and machine learning for research related to the classification of skin lesions, particularly for the detection of skin cancer. “HAM” stands for “Human Against Machine,” signifying the goal of improving automated dermatological diagnoses through machine learning.

As biometric security becomes more common, we can’t ignore the risk of silicone masks in biometric attacks. Our AI data collection company is dedicated to giving you all the latest info on this changing threat.

This dataset is designed for those who are looking to undertake a binary classification project that’s both engaging and challenging.

The LISA Traffic Light Dataset is a well-known and widely used dataset in computer vision and autonomous driving research. It is designed to support the development and evaluation of algorithms for traffic light detection and recognition.

This data set contains 9 different types of flowers. Your task is to build a machine-learning or deep-learning model that can classify these images as flowers.

Found this dataset valuable? Support its growth and visibility in the community with an upvote. Your feedback helps enhance its reach and utility, making it a cornerstone for future explorations in aerial technology.

Semantic segmentation is a crucial computer vision technique used in the context of self-driving cars to understand and interpret the environment surrounding the vehicle. It involves the process of assigning semantic labels to each pixel in an image or a series of images, effectively segmenting the image into various meaningful objects or regions.