Artificial intelligence (AI) is predicted to have profound effects on the future of video capsule endoscopy (VCE) technology. The potential lies in improving anomaly detection while reducing manual labour. However, medical data is often sparse andunavailable to the research community, and qualified medical personnel rarely have time for the tedious labelling work. Inthis respect, we presentKvasir-Capsule, a large VCE dataset collected from examinations at Bærum Hospital in Norway. Kvasir-Capsuleconsists of118videos from which we can generate2, 830,089 image frames. We have labelled and medicallyverified44, 260frames with a bounding box around detected anomalies from 13 different classes of findings. In addition to theselabelled images, there are2, 785,829 unlabelled frames included in the dataset. Initial experiments demonstrate the potentialbenefits of AI-based computer-assisted diagnosis systems for VCE. However, they also show that there is great potentialfor improvements, and theKvasir-Capsuledataset can play a valuable role in developing better algorithms in order for VCEtechnology to reach its true potential