Time Of Day Dataset
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Time Of Day Dataset
Datasets
Time Of Day Dataset
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Time Of Day Dataset
Use Case
Computer Vision
Description
Discover our curated Time of Day Dataset, featuring a diverse collection of high-quality images capturing sunrise, daytime, and nighttime scenes.
Description:
Experience the essence of different moments in a day with our Daylight Moments Dataset. This carefully curated collection captures the diverse moods and lighting conditions at various times. Dawn and dusk are grouped together, showcasing the beauty of these transitional periods.
About the Dataset:
Our Daylight Moments Dataset offers a rich collection of images that capture the essence of different times of the day. From the serene hues of sunrise and sunset to the vibrant scenes of daytime, and even the mysterious allure of nighttime, this dataset provides a comprehensive view of the natural light cycle.
The images, carefully sourced from the renowned online photography community Unsplash, display a variety of locations and weather conditions, including clear skies and cloudy days. Moreover, each image has been standardized to a resolution of 224 x 224 pixels. This ensures consistency and compatibility for various computer vision applications.
Purpose: Designed for training and evaluating computer vision models, this dataset helps developers and researchers build strong algorithms that can recognize and classify images based on the time of day and lighting conditions. Whether you are enhancing scene understanding in autonomous vehicles, optimizing surveillance systems, or improving photo editing applications, the Daylight Moments Dataset is a valuable resource. Consequently, it advances visual recognition technologies and supports various applications.
Key Features:
A meticulously curated collection of 2671 high-quality images
Comprehensive coverage of sunrise/sunset, daytime, and nighttime scenes
Images sourced from Unsplash and licensed under the Unsplash License for unrestricted usage
Standardized resolution of 224 x 224 pixels for seamless integration into machine learning pipelines
Carefully labeled and annotated to ensure accuracy and reliability in model training and evaluation
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