Fisheye cameras are commonly employed for obtaining a large field of view in surveillance, augmented reality and in particular automotive applications. In spite of its prevalence, there are few public datasets for detailed evaluation of computer vision algorithms on fisheye images. WoodScape is an extensive fisheye automotive dataset named after Robert Wood who invented the fisheye camera in 1906. WoodScape comprises of four surround view cameras and nine tasks including segmentation, depth estimation, 3D bounding box detection and soiling detection. Semantic annotation of 40 classes at the instance level is provided for over 10,000 images and annotation for other tasks are provided for over 100,000 images.
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The Stanford Background dataset contains 715 RGB images and the corresponding label images. Images are approximately 240×320 pixels in size and pixels are classified into eight different categories
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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
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Synscapes is a synthetic dataset for street scene parsing created using photorealistic rendering techniques, and show state-of-the-art results for training and validation as well as new types of analysis.
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PanNuke is a semi automatically generated nuclei instance segmentation and classification dataset with exhaustive nuclei labels across 19 different tissue types. The dataset consists of 481 visual fields, of which 312 are randomly sampled from more than 20K whole slide images at different magnifications, from multiple data sources. In total the dataset contains 205,343 labeled nuclei, each with an instance segmentation mask.
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The xBD dataset contains over 45,000KM2 of polygon labeled pre and post disaster imagery. The dataset provides the post-disaster imagery with transposed polygons from pre over the buildings, with damage classification labels.
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DensePASS - a novel densely annotated dataset for panoramic segmentation under cross-domain conditions, specifically built to study the Pinhole-to-Panoramic transfer and accompanied with pinhole camera training examples obtained from Cityscapes. DensePASS covers both, labelled- and unlabelled 360-degree images, with the labelled data comprising 19 classes which explicitly fit the categories available in the source domain (i.e. pinhole) data.
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KITTI Road is road and lane estimation benchmark that consists of 289 training and 290 test images. It contains three different categories of road scenes: * uu - urban unmarked (98/100) * um - urban marked (95/96) * umm - urban multiple marked lanes (96/94) * urban - combination of the three above Ground truth has been generated by manual annotation of the images and is available for two different road terrain types: road - the road area, i.e, the composition of all lanes, and lane - the ego-lane, i.e., the lane the vehicle is currently driving on (only available for category "um"). Ground truth is provided for training images only.
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RoadTracer is a dataset for extraction of road networks from aerial images. It consists of a large corpus of high-resolution satellite imagery and ground truth road network graphs covering the urban core of forty cities across six countries. For each city, the dataset covers a region of approximately 24 sq km around the city center. The satellite imagery is obtained from Google at 60 cm/pixel resolution, and the road network from OSM.
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RELLIS-3D is a multi-modal dataset for off-road robotics. It was collected in an off-road environment containing annotations for 13,556 LiDAR scans and 6,235 images. The data was collected on the Rellis Campus of Texas A&M University and presents challenges to existing algorithms related to class imbalance and environmental topography. The dataset also provides full-stack sensor data in ROS bag format, including RGB camera images, LiDAR point clouds, a pair of stereo images, high-precision GPS measurement, and IMU data.
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3D-FUTURE (3D FUrniture shape with TextURE) is a 3D dataset that contains 20,240 photo-realistic synthetic images captured in 5,000 diverse scenes, and 9,992 involved unique industrial 3D CAD shapes of furniture with high-resolution informative textures developed by professional designers.
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Open Images V4 offers large scale across several dimensions: 30.1M image-level labels for 19.8k concepts, 15.4M bounding boxes for 600 object classes, and 375k visual relationship annotations involving 57 classes. For object detection in particular, 15x more bounding boxes than the next largest datasets (15.4M boxes on 1.9M images) are provided. The images often show complex scenes with several objects (8 annotated objects per image on average). Visual relationships between them are annotated, which support visual relationship detection, an emerging task that requires structured reasoning.
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Our project (STPLS3D) aims to provide a large-scale aerial photogrammetry dataset with synthetic and real annotated 3D point clouds for semantic and instance segmentation tasks.
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Powered by the ImageNet dataset, unsupervised learning on large-scale data has made significant advances for classification tasks. 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 learning is difficult. 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. Our benchmark has a high data diversity and a clear task objective. We also present a simple yet effective baseline method that works surprisingly well for LUSS. In addition, we benchmark related un/weakly/fully supervised methods accordingly, identifying the challenges and possible directions of LUSS.
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DeepFashion2 is a versatile benchmark of four tasks including clothes detection, pose estimation, segmentation, and retrieval. It has 801K clothing items where each item has rich annotations such as style, scale, viewpoint, occlusion, bounding box, dense landmarks and masks. There are also 873K Commercial-Consumer clothes pairs
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InteriorNet is a RGB-D for large scale interior scene understanding and mapping. The dataset contains 20M images created by pipeline:
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PST900 is a dataset of 894 synchronized and calibrated RGB and Thermal image pairs with per pixel human annotations across four distinct classes from the DARPA Subterranean Challenge.
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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, human divers, robots, and sea-floor. The images have been rigorously collected during oceanic explorations and human-robot collaborative experiments, and annotated by human participants.
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The PASCAL-Scribble Dataset is an extension of the PASCAL dataset with scribble annotations for semantic segmentation. The annotations follow two different protocols. In the first protocol, the PASCAL VOC 2012 set is annotated, with 20 object categories (aeroplane, bicycle, ...) and one background category. There are 12,031 images annotated, including 10,582 images in the training set and 1,449 images in the validation set. In the second protocol, the 59 object/stuff categories and one background category involved in the PASCAL-CONTEXT dataset are used. Besides the 20 object categories in the first protocol, there are 39 extra categories (snow, tree, ...) included. This protocol is followed to annotate the PASCAL-CONTEXT dataset. 4,998 images in the training set have been annotated.
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DADA-seg is a pixel-wise annotated accident dataset, which contains a variety of critical scenarios from traffic accidents. It is used for semantic segmentation.
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Gaofen Image Dataset (GID) is a large-scale land-cover dataset constructed with Gaofen-2 (GF-2) satellite images. This dataset has superiorities over the existing land-cover dataset because of its large coverage, wide distribution, and high spatial resolution. It contains 150 GF-2 images annotated at the pixel level for 5 categories: built-up, farmland, forest, meadow, and water.
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OpenEDS (Open Eye Dataset) is a large scale data set of eye-images captured using a virtual-reality (VR) head mounted display mounted with two synchronized eyefacing cameras at a frame rate of 200 Hz under controlled illumination. This dataset is compiled from video capture of the eye-region collected from 152 individual participants and is divided into four subsets: (i) 12,759 images with pixel-level annotations for key eye-regions: iris, pupil and sclera (ii) 252,690 unlabelled eye-images, (iii) 91,200 frames from randomly selected video sequence of 1.5 seconds in duration and (iv) 143 pairs of left and right point cloud data compiled from corneal topography of eye regions collected from a subset, 143 out of 152, participants in the study.
Nighttime Driving is a dataset of road scenes consisting of 35,000 images ranging from daytime to twilight time and to nighttime.
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PhraseCut is a dataset consisting of 77,262 images and 345,486 phrase-region pairs. The dataset is collected on top of the Visual Genome dataset and uses the existing annotations to generate a challenging set of referring phrases for which the corresponding regions are manually annotated.
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Introduced by Da et al. in DigestPath: a Benchmark Dataset with Challenge Review for the Pathological Detection and Segmentation of Digestive-System
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The ISIC 2018 dataset was published by the International Skin Imaging Collaboration (ISIC) as a large-scale dataset of dermoscopy images. This Task 1 dataset is the challenge on lesion segmentation. It includes 2594 images.
Retinal OCTA SEgmentation dataset (ROSE) consists of 229 OCTA images with vessel annotations at either centerline-level or pixel level.
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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 during radiotherapy. In this dataset, the OARs are the heart, the trachea, the aorta and the esophagus, which have varying spatial and appearance characteristics. The dataset includes 60 3D CT scans, divided into a training set of 40 and a test set of 20 patients, where the OARs have been contoured manually by an experienced radiotherapist.
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The DeepWeeds dataset consists of 17,509 images capturing eight different weed species native to Australia in situ with neighbouring flora.
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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, which may be the largest dataset with whole abdominal organs annotation.
The INRIA Aerial Image Labeling dataset is comprised of 360 RGB tiles of 5000×5000px with a spatial resolution of 30cm/px on 10 cities across the globe. Half of the cities are used for training and are associated to a public ground truth of building footprints. The rest of the dataset is used only for evaluation with a hidden ground truth. The dataset was constructed by combining public domain imagery and public domain official building footprints.
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ModaNet is a street fashion images dataset consisting of annotations related to RGB images. ModaNet provides multiple polygon annotations for each image. Each polygon is associated with a label from 13 meta fashion categories. The annotations are based on images in the PaperDoll image set, which has only a few hundred images annotated by the superpixel-based tool.
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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SceneNet is a dataset of labelled synthetic indoor scenes. There are several labeled indoor scenes, including:
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PartImageNet is a large, high-quality dataset with part segmentation annotations. It consists of 158 classes from ImageNet with approximately 24000 images. PartImageNet offers part-level annotations on a general set of classes with non-rigid, articulated objects, while having an order of magnitude larger size compared to existing datasets. It can be utilized in multiple vision tasks including but not limited to: Part Discovery, Semantic Segmentation, Few-shot Learning.
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FoodSeg103 is a new food image dataset containing 7,118 images. Images are annotated with 104 ingredient classes and each image has an average of 6 ingredient labels and pixel-wise masks. It's provided as a large-scale benchmark for food image segmentation.
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The MHP dataset contains multiple persons captured in real-world scenes with pixel-level fine-grained semantic annotations in an instance-aware setting.
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BRATS 2016 is a brain tumor segmentation dataset. It shares the same training set as BRATS 2015, which consists of 220 HHG and 54 LGG. Its testing dataset consists of 191 cases with unknown grades. Image Source: https://sites.google.com/site/braintumorsegmentation/home/brats_2016
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DADA-2000 is a large-scale benchmark with 2000 video sequences (named as DADA-2000) is contributed with laborious annotation for driver attention (fixation, saccade, focusing time), accident objects/intervals, as well as the accident categories, and superior performance to state-of-the-arts are provided by thorough evaluations.
The Paris-Lille-3D is a Benchmark on Point Cloud Classification. The Point Cloud has been labeled entirely by hand with 50 different classes. The dataset consists of around 2km of Mobile Laser System point cloud acquired in two cities in France (Paris and Lille).
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REFUGE Challenge provides a data set of 1200 fundus images with ground truth segmentations and clinical glaucoma labels, currently the largest existing one.
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Partial iLIDS is a dataset for occluded person person re-identification. It contains a total of 476 images of 119 people captured by 4 non-overlapping cameras. Some images contain people occluded by other individuals or luggage.
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CryoNuSeg is a fully annotated FS-derived cryosectioned and H&E-stained nuclei instance segmentation dataset. The dataset contains images from 10 human organs that were not exploited in other publicly available datasets, and is provided with three manual mark-ups to allow measuring intra-observer and inter-observer variability.
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So2Sat LCZ42 consists of local climate zone (LCZ) labels of about half a million Sentinel-1 and Sentinel-2 image patches in 42 urban agglomerations (plus 10 additional smaller areas) across the globe. This dataset was labeled by 15 domain experts following a carefully designed labeling work flow and evaluation process over a period of six months.
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Multimodal material segmentation (MCubeS) dataset contains 500 sets of images from 42 street scenes. Each scene has images for four modalities: RGB, angle of linear polarization (AoLP), degree of linear polarization (DoLP), and near-infrared (NIR). The dataset provides annotated ground truth labels for both material and semantic segmentation for every pixel. The dataset is divided training set with 302 image sets, validation set with 96 image sets, and test set with 102 image sets. Each image has 1224 x 1024 pixels and a total of 20 class labels per pixel.
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We release expert-made scribble annotations for the medical ACDC dataset 1. The released data must be considered as extending the original ACDC dataset. The ACDC dataset contains cardiac MRI images, paired with hand-made segmentation masks. It is possible to use the segmentation masks provided in the ACDC dataset to evaluate the performance of methods trained using only scribble supervision.
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Detecting vehicles and representing their position and orientation in the three dimensional space is a key technology for autonomous driving. Recently, methods for 3D vehicle detection solely based on monocular RGB images gained popularity. In order to facilitate this task as well as to compare and drive state-of-the-art methods, several new datasets and benchmarks have been published. Ground truth annotations of vehicles are usually obtained using lidar point clouds, which often induces errors due to imperfect calibration or synchronization between both sensors. To this end, we propose Cityscapes 3D, extending the original Cityscapes dataset with 3D bounding box annotations for all types of vehicles. In contrast to existing datasets, our 3D annotations were labeled using stereo RGB images only and capture all nine degrees of freedom. This leads to a pixel-accurate reprojection in the RGB image and a higher range of annotations compared to lidar-based approaches. In order to ease multi
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DeepFish as a benchmark suite with a large-scale dataset to train and test methods for several computer vision tasks. The dataset consists of approximately 40 thousand images collected underwater from 20 habitats in the marine environments of tropical Australia. It contains classification labels as well as point-level and segmentation labels to have a more comprehensive fish analysis benchmark. These labels enable models to learn to automatically monitor fish count, identify their locations, and estimate their sizes.
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