Object detection datasets in computer vision refer to collections of labeled images or videos that are specifically curated and annotated for the task of object detection. These datasets are used to train and evaluate object detection models, which are algorithms designed to identify and locate objects of interest within an image or video.
Object detection datasets typically include images or video frames along with annotations that specify the presence and location of objects within the data. The annotations commonly include bounding boxes that outline the objects in the images or videos. Some datasets may also provide additional information such as object categories, segmentation masks, or keypoints. These datasets are crucial for training and evaluating object detection models, as they provide the necessary ground truth labels that enable the models to learn to detect objects accurately. The availability of diverse and well-annotated datasets is essential for advancing the state-of-the-art in object detection research and developing practical applications in various domains, such as autonomous driving, surveillance, robotics, and more.