The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.
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Kvasir-SEG is an open-access dataset of gastrointestinal polyp images and corresponding segmentation masks, manually annotated by a medical doctor and then verified by an experienced gastroenterologist.
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PASCAL VOC 2007 is a dataset for image recognition. The twenty object classes that have been selected are:
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The KVASIR Dataset was released as part of the medical multimedia challenge presented by MediaEval. It is based on images obtained from the GI tract via an endoscopy procedure. The dataset is composed of images that are annotated and verified by medical doctors, and captures 8 different classes. The classes are based on three anatomical landmarks (z-line, pylorus, cecum), three pathological findings (esophagitis, polyps, ulcerative colitis) and two other classes (dyed and lifted polyps, dyed resection margins) related to the polyp removal process. Overall, the dataset contains 8,000 endoscopic images, with 1,000 image examples per class.
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Argoverse-HD is a dataset built for streaming object detection, which encompasses real-time object detection, video object detection, tracking, and short-term forecasting. It contains the video data from Argoverse 1.1 with our own MS COCO-style bounding box annotations with track IDs. The annotations are backward-compatible with COCO as one can directly evaluate COCO pre-trained models on this dataset to estimate the efficiency or the cross-dataset generalization capability of the models. The dataset contains high-quality and temporally-dense annotations for high-resolution videos (1920 x 1200 @ 30 FPS). Overall, there are 70,000 image frames and 1.3 million bounding boxes.
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Consists of annotated frames containing GI procedure tools such as snares, balloons and biopsy forceps, etc. Beside of the images, the dataset includes ground truth masks and bounding boxes and has been verified by two expert GI endoscopists.
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HyperKvasir dataset contains 110,079 images and 374 videos where it captures anatomical landmarks and pathological and normal findings. A total of around 1 million images and video frames altogether.
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A challenge that consists of three tasks, each targeting a different requirement for in-clinic use. The first task involves classifying images from the GI tract into 23 distinct classes. The second task focuses on efficiant classification measured by the amount of time spent processing each image. The last task relates to automatcially segmenting polyps.
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