scb-mt-en-th-2020 is an English-Thai machine translation dataset with over 1 million segment pairs, curated from various sources, namely news, Wikipedia articles, SMS messages, task-based dialogs, web-crawled
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The 3DSeg-8 is a collection of several publicly available 3D segmentation datasets from different medical imaging modalities, e.g. magnetic resonance imaging (MRI) and computed tomography (CT), with various
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…It also contains images (as Photoshop and GIMP files) showing hand-segmentation of the Hoechst images into regions containing single nuclei.
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…Annotations include bounding boxes and instance segmentation masks for 6 classes.
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FSS-1000 is a 1000 class dataset for few-shot segmentation.
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…Provide: * four cameras RAW images * corresponding RGB images * detection labels * segmentation labels
VinDr-RibCXR is a benchmark dataset for automatic segmentation and labeling of individual ribs from chest X-ray (CXR) scans.
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…automatically generated subset of COCO val dataset, collecting partially occluded objects for a large variety of categories in real images in a scalable manner, where target object is partially occluded but the segmentation
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The SemanticPOSS dataset for 3D semantic segmentation contains 2988 various and complicated LiDAR scans with large quantity of dynamic instances.
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The Pascal Panoptic Parts dataset consists of annotations for the part-aware panoptic segmentation task on the PASCAL VOC 2010 dataset.
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Separated COCO is automatically generated subsets of COCO val dataset, collecting separated objects for a large variety of categories in real images in a scalable manner, where target object segmentation
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The PROMISE12 dataset was made available for the MICCAI 2012 prostate segmentation challenge.
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…To create this dataset, a large number of text segments extracted from English language Wikipedia are perturbed, and then verified these with crowd-sourced annotations.
ClevrTex is a new benchmark designed as the next challenge to compare, evaluate and analyze algorithms for unsupervised multi-object segmentation.
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Kazakh offline Handwritten Text dataset (KOHTD) has 3000 handwritten exam papers and more than 140335 segmented images and there are approximately 922010 symbols.
…Each image has segmentation mask of facial attributes corresponding to CelebA.
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…This dataset includes the scans, registrations to the SMPL model, scans segmented in clothing parts, garment category and size labels.
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IDD is a dataset for road scene understanding in unstructured environments used for semantic segmentation and object detection for autonomous driving.
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…The dataset may be used for evaluation of different perception algorithms for segmentation, detection, classification, etc. The dataset creators provide manually annotated pixel-wise ground truth segmentation masks for 6 classes: Obstacle, Trail, Sky, Grass, Vegetation, and Void.
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…polyp segmentation, lung infection segmentation), agriculture (e.g., locust detection to prevent invasion), and art (e.g., recreational art). The high intrinsic similarities between the targets and non-targets make COD far more challenging than traditional object segmentation/detection.
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…The dataset can be used for the human part segmentation task.
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…Each RGB image has a corresponding depth and segmentation map. As many as 700 object categories are labeled. The training and testing sets contain 5285 and 5050 images, respectively.
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Ciona17 is a semantic segmentation dataset with pixel-level annotations pertaining to invasive species in a marine environment.
…RaidaR consists of 58,542 real rainy images containing several rain-induced artifacts: fog, droplets, road reflections, etc. 5,000/3,658 images were carefully semantic/instance segmentated, respectively
…respiratory flow ranging on 180-240 L/min.Each audio recording was sampled with a 8KHz sampling frequency, as a mono channel WAV file, at 8-bit depth.The audio recordings were segmented The obtained segments (of non-mixed states) were of variable length and, for some methods, were further segmented into frames of fixed length for the purposes of feature extraction.The constructed database overall consisted of 193 drug actuation segments, 319 inhalation and 620 exhalation segments and 505 noise segments, ready to be used for audio sound recognition using different sets of features
Please find more details of this dataset at https://alex-xun-xu.github.io/ProjectPage/CVPR_18/index.html 3D motion segmentation has been the key problem in computer vision research due to the application Traditional motion segmentation approaches are often evaluated on artificial dataset like Hopkins 155 [1] and its variants. We proposed the KT3DMoSeg to address the 3D motion segmentation problem in real world scenes. A Benchmark for the Comparison of 3-D Motion Segmentation Algorithms. CVPR, 2007. [2] A. Geiger, P. Lenz, C. Stiller, and R. Urtasun. Vision meets robotics: The kitti dataset.
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…The corpus is extracted from Wikipedia, and 10K segments per language pair are annotated.
…All images have an associated ground truth annotation of breed, head ROI, and pixel-level trimap segmentation.
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Comprises of 171,191 video segments from 346 high-quality soccer games.
The ADE20K semantic segmentation dataset contains more than 20K scene-centric images exhaustively annotated with pixel-level objects and object parts labels.
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…The framework used for the annotation process draws on crowdsourcing to segment surfaces from photos, and then annotate them with rich surface properties, including material, texture and contextual information
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WORD is a dataset for organ semantic segmentation that contains 150 abdominal CT volumes (30,495 slices) and each volume has 16 organs with fine pixel-level annotations and scribble-based sparse annotation
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CHASE_DB1 is a dataset for retinal vessel segmentation which contains 28 color retina images with the size of 999×960 pixels which are collected from both left and right eyes of 14 school children.
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The GOT-10k dataset contains more than 10,000 video segments of real-world moving objects and over 1.5 million manually labelled bounding boxes.
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…Opinionlevel Sentiment Intensity (MOSI) contains: (1) multimodal observations including transcribed speech and visual gestures as well as automatic audio and visual features, (2) opinion-level subjectivity segmentation
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This brain anatomy segmentation dataset has 1300 2D US scans for training and 329 for testing. For every collected image ventricles and septum pellecudi are manually segmented by an expert ultrasonographer.
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…A LLM's task is to segment out the one snake in each image with a circle at its tip. The dataset includes 200,000 images and 200,000 segmentation masks, one for each image.
A large-scale aerial farmland image dataset for semantic segmentation of agricultural patterns.
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Automatic segmentation, tokenization and morphological and syntactic annotations of raw texts in 45 languages, generated by UDPipe (http://ufal.mff.cuni.cz/udpipe), together with word embeddings of dimension
CH-SIMS is a Chinese single- and multimodal sentiment analysis dataset which contains 2,281 refined video segments in the wild with both multimodal and independent unimodal annotations.
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…The dataset can be used for lesion recognition tasks such as lesion segmentation, lesion detection and lesion classification.
…The dataset contains 12 categories of prohibited items in 47, 677 X-ray images with high-quality annotated segmentation masks and bounding boxes.
…with identity-replaced forgery approaches / fake with identity-remained forgery approaches), and n-way (real and 15 respective forgery approaches) classification. 2) Spatial Forgery Localization, which segments This task is important because attackers in real world are free to manipulate any target frame. and 4) Temporal Forgery Localization, to localize the temporal segments which are manipulated. video-level approaches), perturbations (36 independent and more mixed perturbations) and annotations (6.3 million classification labels, 2.9 million manipulated area annotations and 221,247 temporal forgery segment
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…Robotic grasping requires a variety of computer vision tasks such as object detection, segmentation, grasp prediction, pick planning, etc. The proposed dataset contains 100,000 images and 25 different object types, and is split into 5 difficulties to evaluate object detection and segmentation model performance in different grasping scenarios We also propose a new layout-weighted performance metric alongside the dataset for evaluating object detection and segmentation performance in a manner that is more appropriate for robotic grasp applications This repository contains the first phase of MetaGraspNet benchmark dataset which includes detailed object detection, segmentation, layout annotations, and a script for layout-weighted performance metric