VISOR is a dataset of pixel annotations and a benchmark suite for segmenting hands and active objects in egocentric video.
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Youtube-VOS is a Video Object Segmentation dataset that contains 4,453 videos - 3,471 for training, 474 for validation, and 508 for testing. It also contains Instance Segmentation annotations. It has more than 7,800 unique objects, 190k high-quality manual annotations and more than 340 minutes in duration.
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ODMS is a dataset for learning Object Depth via Motion and Segmentation. ODMS training data are configurable and extensible, with each training example consisting of a series of object segmentation masks, camera movement distances, and ground truth object depth.
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The Freiburg-Berkeley Motion Segmentation Dataset (FBMS-59) is an extension of the BMS dataset with 33 additional video sequences. A total of 720 frames is annotated. It has pixel-accurate segmentation annotations of moving objects. FBMS-59 comes with a split into a training set and a test set.
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…Partial and Unusual Masks for Video Object Segmentation (PUMaVOS) dataset has the following properties: - 24 videos, 21187 densely-annotated frames; - Covers complex practical use cases such as object
SegTrack v2 is a video segmentation dataset with full pixel-level annotations on multiple objects at each frame within each video.
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DAVIS17 is a dataset for video object segmentation. It contains a total of 150 videos - 60 for training, 30 for validation, 60 for testing
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DAVIS16 is a dataset for video object segmentation which consists of 50 videos in total (30 videos for training and 20 for testing). Per-frame pixel-wise annotations are offered.
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…Segmentation masks Bounding boxes For the full description of labels and metadata, check out the README.
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