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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.