A comprehensive X-ray image dataset for bone fracture detection has been created to support computer vision projects. The dataset includes images categorized by fracture types such as Elbow Positive, Fingers Positive, Forearm Fracture, Humerus Fracture, Shoulder Fracture, and Wrist Positive. Each image is annotated with bounding boxes or pixel-level segmentation masks to indicate fracture locations. This dataset is ideal for training and evaluating machine learning models, particularly for object detection algorithms aimed at automated fracture detection. It accelerates the development of computer vision solutions for medical diagnostics, enhancing patient care.

The Age Detection dataset is built upon a collection of selfies and ID card images, featuring high-quality facial photographs of individuals between the ages of 18 and 60. The dataset is divided into five distinct age groups: 18–20, 21–30, 31–40, 41–50, and 51–60, with separate folders for training and testing purposes. Each image is accompanied by a CSV file containing rich metadata, including the individual’s exact age, true gender, country, ethnicity, as well as the file extension and resolution for each photo. The demographic diversity within the dataset—covering various ethnicities, genders, and nationalities—makes it highly suitable for developing and evaluating deep learning models for age estimation, facial recognition, and biometric analysis. The full commercial version contains over 95,000 photos and is available for purchase via the TrainingData platform. In addition, several supplementary datasets are offered, including selfie-video datasets, bald-person datasets, and anti-spoofing datasets, making this a comprehensive resource for advanced biometric system development.

he Human Activity Recognition (HAR) dataset consists of over 12,000 labeled images spanning 15 distinct human activity classes, including actions such as calling, dancing, running, and sleeping. Each image represents a single activity and is organized into separate folders corresponding to its class label. The objective is to develop a convolutional neural network (CNN)-based image classification model capable of accurately predicting the activity being performed in each image. A separate test set of 5,400 unlabeled images is provided for evaluation, along with a submission template that specifies the required format for output predictions. This task falls under the broader domain of computer vision and has practical applications in surveillance, healthcare monitoring, human-computer interaction, and behavior analysis.

This dataset contains a vast collection of 26.9k images, which have been carefully annotated for the specific purpose of ship detection. The bounding box annotations are presented in the YOLO format, which allows for accurate and efficient detection of the ships in the images. The dataset has been curated to include images of only one class - "ship" - thus enabling streamlined and precise analysis.

The detection of ships or vessels within an image is a vital task that has significant practical applications. Maritime safety is one such application, as the detection of ships can help prevent accidents at sea by providing early warnings of potential collisions or obstacles. Fisheries management is another important use case, where the detection of fishing vessels can aid in monitoring fishing activities and preventing overfishing. In addition, ship detection can be used for marine pollution monitoring, defense and maritime security, protection from piracy, illegal immigration, and a range of other purposes.