The Medical Segmentation Decathlon is a collection of medical image segmentation datasets.
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Test dataset for Semantic Segmentation. The datasets includes 500 RGB - images with the relative single-channel binary masks.
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The Follicular-Segmentation dataset consists of 6900 cropped typical image patches of 1024x1024 pixels containing: follicular areas, colloid areas, and the other blank background areas.
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Lesion Boundary Segmentation Dataset is a dataset for lesion segmentation from the ISIC2018 challenge. The dataset contains skin lesions and their corresponding annotations.
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Panoramic Video Panoptic Segmentation Dataset is a large-scale dataset that offers high-quality panoptic segmentation labels for autonomous driving.
…The last task relates to automatcially segmenting polyps. Please cite "The EndoTect 2020 Challenge: Evaluation andComparison of Classification, Segmentation and Inference Time for Endoscopy" if you use the dataset.
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This is a video and image segmentation dataset for human head and shoulders, relevant for creating elegant media for videoconferencing and virtual reality applications.
Contains squared blocks of 48×48 pixels including 13 Sentinel-2 bands. Each 480-m block was mined from a large geographical area of interest (102 km × 42 km) located north of Munich, Germany.
This dataset were acquired with the Airphen (Hyphen, Avignon, France) six-band multi-spectral camera configured using the 450/570/675/710/730/850 nm bands with a 10 nm FWHM. And acquired on the site of INRAe in Montoldre (Allier, France, at 46°20'30.3"N 3°26'03.6"E) within the framework of the “RoSE challenge” founded by the French National Research Agency (ANR). Images contains bean, with various natural weeds (yarrows, amaranth, geranium, plantago, etc) and sowed ones (mustards, goosefoots, mayweed and ryegrass) with very distinct characteristics in terms of illumination (shadow, morning, evening, full sun, cloudy, rain, ...) The ground truth is defined for each images with polygons around leafs boundaries: In addition, each polygons are labeled into crop or weed. (2020-06-11)
VISOR is a dataset of pixel annotations and a benchmark suite for segmenting hands and active objects in egocentric video.
The “Medico automatic polyp segmentation challenge” aims to develop computer-aided diagnosis systems for automatic polyp segmentation to detect all types of polyps (for example, irregular polyp, smaller The main goal of the challenge is to benchmark semantic segmentation algorithms on a publicly available dataset, emphasizing robustness, speed, and generalization. Medico Multimedia Task at MediaEval 2020:Automatic Polyp Segmentation (https://arxiv.org/pdf/2012.15244.pdf)
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Persian Text Image Segmentation (PTI SEG) This dataset is part of a paper titled "Persis: A Persian Font Recognition Pipeline Using Convolutional Neural Networks". A dataset in order to solve image segmentation for the Persian texts.
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…Nevertheless, this means, that an instance segmentation of all components and objects of interest into disjoint entities from the CT data is necessary. As of currently, no adequate computer-assisted tools for automated or semi-automated segmentation of such XXL-airplane data are available, in a first step, an interactive data annotation and object labelling
…BSD100 is the testing set of the Berkeley segmentation dataset BSD300.
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The Daimler Urban Segmentation Dataset is a dataset for semantic segmentation. It consists of video sequences recorded in urban traffic.
The Berkeley Motion Segmentation Dataset (BMS-26) is a dataset for motion segmentation, which consists of 26 video sequences with pixel-accurate segmentation annotation of moving objects.
GAS (Grasp Area Segmentation) dataset consists of 10089 RGB images of cluttered scenes grouped into 1121 grasp-area segmentation tasks. For each RGB image we provide a binary segmentation map with the graspable and non-graspable regions for every object in the scene. For creating the GAS dataset we use the RGB images and corresponding ground truth segmentation masks from the GraspNet 1-Billion dataset.
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Berkeley Segmentation Data Set 500 (BSDS500) is a standard benchmark for contour detection.
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Multimodal material segmentation (MCubeS) dataset contains 500 sets of images from 42 street scenes. The dataset provides annotated ground truth labels for both material and semantic segmentation for every pixel.
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The BCSS dataset contains over 20,000 segmentation annotations of tissue regions from breast cancer images from The Cancer Genome Atlas (TCGA). It enables the generation of highly accurate machine-learning models for tissue segmentation.
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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…dataset consists of images of 158 filled out bank checks containing various complex backgrounds, and handwritten text and signatures in the respective fields, along with both pixel-level and patch-level segmentation “A Novel Segmentation Dataset for Signatures on Bank Checks.” ArXiv:2104.12203 [Cs], Apr. 2021. arXiv.org, http://arxiv.org/abs/2104.12203. Acknowledgements 1 P. Dansena, S. Bag, and R.
Retinal OCTA SEgmentation dataset (ROSE) consists of 229 OCTA images with vessel annotations at either centerline-level or pixel level.
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The Segmentation of Underwater IMagery (SUIM) dataset contains over 1500 images with pixel annotations for eight object categories: fish (vertebrates), reefs (invertebrates), aquatic plants, wrecks/ruins
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UV6K is a high-resolution remote sensing urban vehicle segmentation dataset. Images: 6,313 Vehicle: 245,141 Resolution: 0.1m Image Size: 1024x1024
The Dense Material Segmentation Dataset (DMS) consists of 3 million polygon labels of material categories (metal, wood, glass, etc) for 44 thousand RGB images. The dataset is described in the research paper, A Dense Material Segmentation Dataset for Indoor and Outdoor Scene Parsing.
CaDIS: a Cataract Dataset for Image Segmentation is a dataset for semantic segmentation created by Digital Surgery Ltd. on top of the CATARACTS dataset.
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LiTS17 is a liver tumor segmentation benchmark. The data and segmentations are provided by various clinical sites around the world.
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The colorectal nuclear segmentation and phenotypes (CoNSeP) dataset consists of 41 H&E stained image tiles, each of size 1,000×1,000 pixels at 40× objective magnification.
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The directory HiCIS contains two datasets for instance segmentation of honeycombs in concrete in COCO Format.
Spine or vertebral segmentation is a crucial step in all applications regarding automated quantification of spinal morphology and pathology. The tasks evaluated for include: vertebral labelling and segmentation.
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This is the first general Underwater Image Instance Segmentation (UIIS) dataset containing 4,628 images for 7 categories with pixel-level annotations for underwater instance segmentation task
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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…There are two major challenges to allowing such an attractive learning modality for segmentation tasks: i) a large-scale benchmark for assessing algorithms is missing; ii) unsupervised shape representation We propose a new problem of large-scale unsupervised semantic segmentation (LUSS) with a newly created benchmark dataset to track the research progress. Based on the ImageNet dataset, we propose the ImageNet-S dataset with 1.2 million training images and 50k high-quality semantic segmentation annotations for evaluation.
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SegTHOR (Segmentation of THoracic Organs at Risk) is a dataset dedicated to the segmentation of organs at risk (OARs) in the thorax, i.e. the organs surrounding the tumour that must be preserved from irradiations
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To build the highly accurate Dichotomous Image Segmentation dataset (DIS5K), we first manually collected over 12,000 images from Flickr1 based on our pre-designed keywords.
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The Freiburg-Berkeley Motion Segmentation Dataset (FBMS-59) is a dataset for motion segmentation, which extends the BMS-26 dataset with 33 additional video sequences.
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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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To reveal and systematically investigate the effectiveness of the proposed method in the real world, a real low-light image dataset for instance segmentation is necessary and urgently needed. Considering there is no suitable dataset, therefore, we collect and annotate a Low-light Instance Segmentation (LIS) dataset using a Canon EOS 5D Mark IV camera.
Embrapa Wine Grape Instance Segmentation Dataset (WGISD) contains grape clusters properly annotated in 300 images and a novel annotation methodology for segmentation of complex objects in natural images
We introduce a large-scale image dataset EasyPortrait for portrait segmentation and face parsing. Segmentation masks were created from polygons for each annotation.
PASTIS is a benchmark dataset for panoptic and semantic segmentation of agricultural parcels from satellite image time series.
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The ORVS dataset has been newly established as a collaboration between the computer science and visual-science departments at the University of Calgary.
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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
Extension of the official KITTI'15 dataset. independently moving instance segmentation ground truth to cover all moving objects, not just a selection of cars and vans. Instance Motion Segmentation of all moving objects Binary Motion Segmentation (background/foreground) Validation Masks Dataset contains: Instance Motion Segmenation for the training split of the KITTI
Egocentric Dataset of the University of Barcelona – Segmentation (EDUB-Seg) is a dataset for egocentric event segmentation acquired by the Narrative Clip, which takes a picture every 30 seconds.
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