Minecraft Segmentation is a segmentation dataset for the Minecraft House that adds semantic segmentation labels for sub-components of the house.
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The instances were drawn randomly from a database of 7 outdoor images. The images were handsegmented to create a classification for every pixel. Each instance is a 3x3 region.
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.
Training data for Hebrew morphological word segmentation
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…"Segmentation in the Wild (SegInW)" Challenge is a part of X-Decoder, that proposed a new benchmark to evaluate the transfer ability of pre-trained vision models. This benchmark presents a diverse set of downstream segmentation datasets, measuring the ability of pre-training models on both the segmentation accuracy and their transfer efficiency in a new task, in This SegInW Challenge consists of 25 free public Segmentation datasets, crowd-sourced on roboflow.com. For more details about the challenge submission format, please refer to X-Decoder for SGinW.
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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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Contains 350 tweets with more than 8,000 words including 3,000 unique words written in Egyptian dialect. The tweets have much dialectal content covering most of dialectal Egyptian phonological, morphological, and syntactic phenomena. It also includes Twitter-specific aspects of the text, such as #hashtags, @mentions, emoticons and URLs.
This prostate MRI segmentation dataset is collected from six different data sources.
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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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Segmentation of robotic instruments is an important problem for robotic assisted minimially invasive surgery. In this challenge we invite applicants to participate in 3 different tasks: binary segmentation, multi-label segmentation and instrument recognition. Binary segmentation involves just separating the image into instruments and background, whereas multi-label segmentation requires the user to also recognize which parts of the instrument body correspond The final recogition task tests whether the user can recognize which segmentation corresponds to which da Vinci instrument type. Description from: Robotic Instrument Segmentation Sub-Challenge
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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.
Semantic segmentation of drone images is critical for various aerial vision tasks as it provides essential semantic details to understand scenes on the ground. Ensuring high accuracy of semantic segmentation models for drones requires access to diverse, large-scale, and high-resolution datasets, which are often scarce in the field of aerial image processing. It's expected that our dataset will generate considerable interest in drone image segmentation and serve as a foundation for other drone vision tasks.
This is a video and image segmentation dataset for human head and shoulders, relevant for creating elegant media for videoconferencing and virtual reality applications.
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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)
…For the dispersed and network morphologies, because these two morphologies are harder to visually distinguish, we have created manual segmentation labels of the nanowires (included in these two morphology Percolation analysis was done on these manually segmented nanowires to provide quantitative metric on whether the nanowires form a network in the image. seg_mask_5_resolutions.zip contains ground truth 2D binary encoding of segmented nanowires at 5 resolutions.
VISOR is a dataset of pixel annotations and a benchmark suite for segmenting hands and active objects in egocentric video.
…A total of 787 satellite images of size 256 × 256 are collected at a high resolution (HR) of 1.193 meters per pixel and hand tagged for built-up region segmentation using an online tool Label-Box.
…All images and segmentations have been fully de-identified in the NIFTI format. "expert_annotations" contains ground truth prostate segmentations annotated by our expert urologist. "non_expert_annotations" contains prostate segmentations annotated by a graduate student. "expert_annotations" contains ground truth prostate segmentations by the expert urologist. "master_student_annotations" contains segmentations by a master's student. "medical_student_annotations" contains segmentations by a medical student. "clinician_annotations" contains segmentations by a urologist with limited experience in reading micro-ultrasound images. "MicroSegNet: A deep learning approach for prostate segmentation on micro-ultrasound images." Computerized Medical Imaging and Graphics (2024): 102326.
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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…MICCAI'2015 Gland Segmentation Challenge Contest Dataset Welcome to the challenge on gland segmentation in histology images. This challenge was held in conjuction with MICCAI 2015, Munich, Germany. Objective of the Challenge We aim to bring together researchers who are interested in the gland segmentation problem, to validate the performance of their existing or newly invented algorithms on the same
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.
…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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This dataset for the semantic segmentation of potholes and cracks on the road surface was assembled from 5 other datasets already publicly available, plus a very small addition of segmented images on our This is the dataset used in the SHREC2022 competition and it is the dataset that allowed us to train the neural networks for semantic segmentation capable of obtaining the nice images and videos that you
Berkeley Segmentation Data Set 500 (BSDS500) is a standard benchmark for contour detection.
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The Densely Annotation Video Segmentation dataset (DAVIS) is a high quality and high resolution densely annotated video segmentation dataset under two resolutions, 480p and 1080p.
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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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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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MeViS is a large-scale dataset for motion expressions guided video segmentation, which focuses on segmenting objects in video content based on a sentence describing the motion of the objects.
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OVIS is a new large scale benchmark dataset for video instance segmentation task.
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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.
The time series segmentation benchmark (TSSB) currently contains 75 annotated time series (TS) with 1-9 segments. Each TS is constructed from one of the UEA & UCR time series classification datasets. We group TS by label and concatenate them to create segments with distinctive temporal patterns and statistical properties. We annotate the offsets at which we concatenated the segments as change points (CPs).
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…However, datasets and models for video semantic segmentation of LC are lacking. Recognizing fine-grained hepatocystic anatomy through semantic segmentation could help surgeons better assess the critical view of safety (CVS), a universally recommended technique consisting in well exposing Additionally, segmentation masks of hepatocystic structures could be leveraged by deep learning models for automatic assessment of CVS and surgical action recognition to improve their performance. We believe that generating a dataset for video semantic segmentation of hepatocystic anatomy will promote surgical data science research and accelerate the development of applications for surgical safety Overall, 1933 regularly spaced video frames from 201 LC videos were annotated with segmentation mask for 29 classes of the hepatocystic triangle, respectively. performed in double by specifically trained
…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.
CoMplex video Object SEgmentation (MOSE) is a dataset to study the tracking and segmenting objects in complex environments. MOSE contains 2,149 video clips and 5,200 objects from 36 categories, with 431,725 high-quality object segmentation masks.
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AVSBench is a pixel-level audio-visual segmentation benchmark that provides ground truth labels for sounding objects. Accordingly, three settings are studied: 1) semi-supervised audio-visual segmentation with a single sound source 2) fully-supervised audio-visual segmentation with multiple sound sources 3) fully-supervised audio-visual semantic segmentation
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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