We invite the Kaggle community to engage with this dataset, utilize it in developing innovative solutions, and contribute to the enhancement of license plate detection technology.

MNIST-100 MNIST-100 Datasets MNIST-100 File MNIST-100 Use Case Computer Vision Description The MNIST-100 dataset is a specialized subset of the […]

A “roads segmentation dataset” typically consists of images or aerial photographs that have been annotated to highlight and classify road areas within the images. This dataset is commonly used in computer vision and machine learning tasks, particularly for training and evaluating algorithms related to road detection and segmentation. The annotations in such a dataset provide pixel-level or object-level labels to distinguish road areas from other elements in the images, making it valuable for applications like autonomous driving, urban planning, and map generation. Researchers and developers use roads segmentation datasets to train models to identify and segment roads accurately from various types of imagery.

CASIA Iris Syn CASIA Iris Syn Datasets CASIA Iris Syn File CASIA Iris Syn Use Case Computer Vision Description This […]

Explore the DeepFashion-MultiModal Dataset, a premier resource for AI and fashion technology, featuring high-quality, aligned images.

Amur Tiger Re-Identification Amur Tiger Re-Identification Datasets Amur Tiger Re-Identification File Amur Tiger Re-Identification Use Case Computer Vision Description The […]

Discover Afro-MNIST, the innovative dataset tailored for machine learning with African numeral systems including Ge`ez, Vai, Osmanya, and N’Ko.

The dataset features over 10,000 video clips from various classes, with each class contributing 10 to 40 videos.

Explore our unique Child and Adult Emotions Dataset, featuring high-resolution images capturing basic moods like seriousness, sadness, and happiness.

At Globose Technology Solutions Private Limited, we’re committed to using the Children’s Dental Panoramic Radiographs Dataset to advance pediatric dental care.