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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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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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.
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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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
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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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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 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.
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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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.
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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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HASCD (Human Activity Segmentation Challenge Dataset) contains 250 annotated multivariate time series capturing 10.7 h of real-world human motion smartphone sensor data from 15 bachelor computer science
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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The SWIMSEG dataset contains 1013 images of sky/cloud patches, along with their corresponding binary segmentation maps.
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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
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Semantic segmentation of drone images is critical for various aerial vision tasks as it provides essential seman- tic 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.
The SWINSEG dataset contains 115 nighttime images of sky/cloud patches along with their corresponding binary ground truth maps. The ground truth annotation was done in consultation with experts from Singapore Meteorological Services. All images were captured in Singapore using WAHRSIS, a calibrated ground-based whole sky imager, over a period of 12 months from January to December 2016. All image patches are 500x500 pixels in size, and were selected considering several factors such as time of the image capture, cloud coverage, and seasonal variations.
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…The images in the SWINySeg dataset are taken from two of our earlier sky/cloud image segmentation datasets -- SWIMSEG and SWINSEG.
Synthetic training dataset of 50,000 depth images and 320,000 object masks using simulated heaps of 3D CAD models.
PASTIS is a benchmark dataset for panoptic and semantic segmentation of agricultural parcels from satellite image time series.
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dacl10k stands for damage classification 10k images and is a multi-label semantic segmentation dataset for 19 classes (13 damages and 6 objects) present on bridges.
Extension of the PASTIS benchmark with radar and optical image time series.
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EgoHOS is a labeled dataset consisting of 11243 egocentric images with per-pixel segmentation labels of hands and objects being interacted with during a diverse array of daily activities.
DOORS is a dataset designed for boulders recognition, centroid regression, segmentation, and navigation applications. It can be used to perform navigation, boulder recognition, segmentation, and centroid regression. Segmentation: Contain images, masks, and labels of 2 datasets: DS1 and DS2. DS1 is made of the same images of the Regression dataset but is specifically designed for segmentation.
FractureAtlas is a musculoskeletal bone fracture dataset with annotations for deep learning tasks like classification, localization, and segmentation.
5987 high spatial resolution (0.3 m) remote sensing images from Nanjing, Changzhou, and Wuhan Focus on different geographical environments between Urban and Rural Advance both semantic segmentation and domain adaptation tasks Three considerable challenges: Multi-scale objects Complex background samples Inconsistent class distributions Two contests are held on the Codalab: <b>LoveDA Semantic Segmentation
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Large-scale and open-access LiDAR dataset intended for the evaluation of real-time semantic segmentation algorithms.
The increasing use of deep learning techniques has reduced interpretation time and, ideally, reduced interpreter bias by automatically deriving geological maps from digital outcrop models. However, accurate validation of these automated mapping approaches is a significant challenge due to the subjective nature of geological mapping and the difficulty in collecting quantitative validation data. Additionally, many state-of-the-art deep learning methods are limited to 2D image data, which is insufficient for 3D digital outcrops, such as hyperclouds. To address these challenges, we present Tinto, a multi-sensor benchmark digital outcrop dataset designed to facilitate the development and validation of deep learning approaches for geological mapping, especially for non-structured 3D data like point clouds. Tinto comprises two complementary sets: 1) a real digital outcrop model from Corta Atalaya (Spain), with spectral attributes and ground-truth data, and 2) a synthetic twin that uses latent
Projection of RibFrac CT dataset to a 2D plane to imitate X-Ray data for a total of 880 images with multi-label segmentation masks.
A Sentinel-2 based time series multi country benchmark dataset, tailored for agricultural monitoring applications with Machine and Deep Learning. Sen4AgriNet dataset is annotated from farmer declarations collected via the Land Parcel Identification System (LPIS) for harmonizing country wide labels. Sen4AgriNet is the only multi-country, multi-year dataset that includes all spectral information. It is constructed to cover the period 2016-2020 for Catalonia and France, while it can be extended to include additional countries. Currently, it contains 42.5 million parcels, which makes it significantly larger than other available archives.
SAMRS is a remote sensing segmentation dataset which provides object category, location, and instance information that can be used for semantic segmentation, instance segmentation, and object detection
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ZeroWaste is a dataset for automatic waste detection and segmentation. This dataset contains over 1,800 fully segmented video frames collected from a real waste sorting plant along with waste material labels for training and evaluation of the segmentation methods, as well ZeroWaste also provides frames of the conveyor belt before and after the sorting process, comprising a novel setup that can be used for weakly-supervised segmentation.
YouTubeVIS is a new dataset tailored for tasks like simultaneous detection, segmentation and tracking of object instances in videos and is collected based on the current largest video object segmentation
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The AIRS (Aerial Imagery for Roof Segmentation) dataset provides a wide coverage of aerial imagery with 7.5 cm resolution and contains over 220,000 buildings. The task posed for AIRS is defined as roof segmentation.
The Person In Context (PIC) dataset is a dataset for human-centric relation segmentation (HRS), which contains 17,122 high-resolution images and densely annotated entity segmentation and relations, including
PASCAL VOC 2011 is an image segmentation dataset. It contains around 2,223 images for training, consisting of 5,034 objects. Testing consists of 1,111 images with 2,028 objects. In total there are over 5,000 precisely segmented objects for training.
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…The images are annotated by segmentation masks of the object(s) of interest. The original purpose of the data collection is for gesture-aware object-agnostic segmentation tasks.
The Vocal Folds dataset is a dataset for automatic segmentation of laryngeal endoscopic images. The dataset consists of 8 sequences from 2 patients containing 536 hand segmented in vivo colour images of the larynx during two different resection interventions with a resolution of 512x512 pixels.
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