Popular Computer Vision Datasets for Object Detection
COCO (Common Objects in Context)
Dataset link: https://cocodataset.org/#home
This dataset released by Microsoft has 328k images. These images are annotated for tasks like object detection, segmentation, and image captioning. Its complex scenes and diverse object categories make it a standard benchmark for various computer vision tasks.
Pascal VOC
Dataset link: http://host.robots.ox.ac.uk/pascal/VOC/
Based on object detection and image segmentation, Pascal Visual Object Classes (VOC) is used as the dataset. It also contains of 10 object classes among which there are people and face, animals, vehicles and indoor objects. Pascal VOC also has annotations for object boundaries, object segmentation masks as well as objects’ classes.
Open Images Dataset
Dataset link: https://storage.googleapis.com/openimages/web/index.html
Open images are a large scale dataset obtained from Google and at least contains almost 9 million images that has image level annotation and bounding boxes of objects, object segmentation masks, visual relationships and located narratives. It has a large scope of object classes and is applied to numerous computer visions tasks, such as object detection, segmentation, and visual relations detection.
Dataset for Computer Vision
Computer Vision is an area in the field of Artificial Intelligence that enables machines to interpret and understand visual information. As in case of any other AI application, Computer vision also requires huge amount of data to give accurate results. These datasets provide all the necessary training material for these algorithms.
A dataset that will well-prepared and maintained will allow the model to learn from examples, recognize pattern and then make predictions about the unseen data. Therefore, the quality of datasets matters a lot, as it impacts the performance and robustness of computer vision applications.