The TimberSeg 1.0 dataset is composed of 220 images showing wood logs in various environments and conditions in Canada. The images are densely annotated with segmentation masks for each log instance, as well as the corresponding bounding box and class label. This dataset aim towards enabling autonomous forestry forwarders, therefore it contains nearly 2500 instances of wood logs from an operators' point-of-view. Images were taken in the forest, near the roadside, in lumberyards and above timber-filled trailers. The logs were annotated considering a grasping perspective, meaning that only the logs above the piles and accessible are segmented.
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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.
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MOTFront provides photo-realistic RGB-D images with their corresponding instance segmentation masks, class labels, 2D & 3D bounding boxes, 3D geometry, 3D poses and camera parameters. The MOTFront dataset comprises 2,381 unique indoor sequences with a total of 60,000 images and is based on the 3D-FRONT dataset.
MVTec D2S is a benchmark for instance-aware semantic segmentation in an industrial domain. It contains 21,000 high-resolution images with pixel-wise labels of all object instances. The objects comprise groceries and everyday products from 60 categories. The benchmark is designed such that it resembles the real-world setting of an automatic checkout, inventory, or warehouse system. The training images only contain objects of a single class on a homogeneous background, while the validation and test sets are much more complex and diverse.
Occluded COCO is 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 mask is connected.
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Synthetic dataset of over 13,000 images of damaged and intact parcels with full 2D and 3D annotations in the COCO format. For details see our paper and for visual samples our project page.
A set of 221 stereo videos captured by the SOCRATES stereo camera trap in a wildlife park in Bonn, Germany between February and July of 2022. A subset of frames is labeled with instance annotations in the COCO format.
Video sequences captured at a field on Campus Kleinaltendorf (CKA), University of Bonn, captured by BonBot-I, an autonomous weeding robot. The data was captured by mounting an Intel RealSense D435i sensor with a nadir view of the ground.
The dataset of Thermal Bridges on Building Rooftops (TBBR dataset) consists of annotated combined RGB and thermal drone images with a height map. All images were converted to a uniform format of 3000$\times$4000 pixels, aligned, and cropped to 2400$\times$3400 to remove empty borders.
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We learn high fidelity human depths by leveraging a collection of social media dance videos scraped from the TikTok mobile social networking application. It is by far one of the most popular video sharing applications across generations, which include short videos (10-15 seconds) of diverse dance challenges as shown above. We manually find more than 300 dance videos that capture a single person performing dance moves from TikTok dance challenge compilations for each month, variety, type of dances, which are moderate movements that do not generate excessive motion blur. For each video, we extract RGB images at 30 frame per second, resulting in more than 100K images. We segmented these images using Removebg application, and computed the UV coordinates from DensePose.
iShape is an irregular shape dataset for instance segmentation. iShape contains six sub-datasets with one real and five synthetics, each represents a scene of a typical irregular shape.
BPCIS is collection of 364 bacterial phase contrast images and corresponding label matrices for instance segmentation. Labels were made according to fluorescence channels where possible. Prior to manual annotation, images were automatically cropped into microcolonies and tiled into ensemble images to reduce the empty (non-cell) image regions for training and testing. Subsequent to annotation, we performed non-rigid registration of phase contrast to cell masks.
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Chinese Character Stroke Extraction (CCSE) is a benchmark containing two large-scale datasets: Kaiti CCSE (CCSE-Kai) and Handwritten CCSE (CCSE-HW). It is designed for stroke extraction problems.
The 'Me 163' was a Second World War fighter airplane and a result of the German air force secret developments. One of these airplanes is currently owned and displayed in the historic aircraft exhibition of the 'Deutsches Museum' in Munich, Germany. To gain insights with respect to its history, design and state of preservation, a complete CT scan was obtained using an industrial XXL-computer tomography scanner at Fraunhofer EZRT .
We provide all the expected data inputs to GUISS such as meshes, texture images, and blend files. Generated datasets used in our experiments along with the stereo depth estimations can be downloaded. We have defined seven dataset types: scene_reconstructions, texture_variation, gaea_texture_variation, generative_texture, terrain_variation, rocks, and generative_texture_snow. Each dataset type contains renderings with varying values of different parameters such as lighting angle, texture imgs, albedo, etc. Position each dataset type folder under data/dataset/.
Human fibrosarcoma HT1080WT (ATCC) cells at low cell densities embedded in 3D collagen type I matrices [1]. The time-lapse videos were recorded every 2 minutes for 16.7 hours and covered a field of view of 1002 pixels × 1004 pixels with a pixel size of 0.802 μm/pixel The videos were pre-processed to correct frame-to-frame drift artifacts, resulting in a final size of 983 pixels × 985 pixels pixels.
Revision: v1.0.0-full-20210527a DOI: 10.5281/zenodo.4817662 Authors: J. Chazalon, E. Carlinet, Y. Chen, J. Perret, C. Mallet, B. Duménieu and T. Géraud Official competition website: https://icdar21-mapseg.github.io/
This publicly available dataset contains 1613 RGB-D images of field-grown broccoli plants. The dataset also includes the polygon and circle annotations of the broccoli heads.
The Instance Segmentation task, an extension of the well-known Object Detection task, is of great help in many areas, such as precision agriculture: being able to automatically identify plant organs and the possible diseases associated with them, allows to effectively scale and automate crop monitoring and its diseases control.
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This data set contains 775 video sequences, captured in the wildlife park Lindenthal (Cologne, Germany) as part of the AMMOD project, using an Intel RealSense D435 stereo camera. In addition to color and infrared images, the D435 is able to infer the distance (or “depth”) to objects in the scene using stereo vision. Observed animals include various birds (at daytime) and mammals such as deer, goats, sheep, donkeys, and foxes (primarily at nighttime). A subset of 412 images is annotated with a total of 1038 individual animal annotations, including instance masks, bounding boxes, class labels, and corresponding track IDs to identify the same individual over the entire video.
Minor Irrigation Structures Check-Dam Dataset is a public dataset annotated by domain experts using images from Google static map for instance segmentation and object detection tasks.
Microscopy images of shrub cross sections for instance segmentation of tree rings.
An object-centric version of Stylized COCO to benchmark texture bias and out-of-distribution robustness of vision models. See the ECCV 22 paper and supplementary material for details.
OmniCity is a dataset for omnipotent city understanding from multi-level and multi-view images. It contains multi-view satellite images as well as street-level panorama and mono-view images, constituting over 100K pixel-wise annotated images that are well-aligned and collected from 25K geo-locations in New York City. This dataset introduces a new task of fine-grained building instance segmentation on street-level panorama images. It also provides new problem settings for existing tasks, such as cross-view image matching, synthesis, segmentation, detection, etc., and facilitates the developing of new methods for large-scale city understanding, reconstruction, and simulation.
Overview The Surgical Instruments Recognition Dataset is a groundbreaking collection of high-resolution images (1280x960 pixels) specifically designed for the recognition and categorization of surgical instruments. This dataset captures the intricate details and complexity of surgical tools, particularly when arranged in scenarios reminiscent of an operating room.
A multimodal dataset of radio galaxies and their corresponding infrared hosts.
Automating the creation of catalogues for radio galaxies in next-generation deep surveys necessitates the identification of components within extended sources and their respective infrared hosts. We present RadioGalaxyNET, a multimodal dataset, tailored for machine learning tasks to streamline the automated detection and localization of multi-component extended radio galaxies and their associated infrared hosts. The dataset encompasses 4,155 instances of galaxies across 2,800 images, incorporating both radio and infrared channels. Each instance furnishes details about the extended radio galaxy class, a bounding box covering all components, a pixel-level segmentation mask, and the keypoint position of the corresponding infrared host galaxy. RadioGalaxyNET is the first dataset to include images from the highly sensitive Australian Square Kilometre Array Pathfinder (ASKAP) radio telescope, corresponding infrared images, and instance-level annotations for galaxy detection.
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SEmantic Salient Instance Video (SESIV) dataset is obtained by augmenting the DAVIS-2017 benchmark dataset by assigning semantic ground-truth for salient instance labels. The SESIV dataset consists of 84 high-quality video sequences with pixel-wisely per-frame ground-truth labels.
TAMPAR is a real-world dataset of parcel photos for tampering detection with annotations in COCO format. For details see the paper and for visual samples the project page. Features are:
We introduce the trapped yeast cell (TYC) dataset, a novel dataset for understanding instance-level semantics and motions of cells in microstructures. We release $105$ dense annotated high-resolution brightfield microscopy images, including about $19$k instance masks. We also release $261$ curated video clips composed of $1293$ high-resolution microscopy images to facilitate unsupervised understanding of cell motions and morphology.
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
VizWiz-FewShot is a a few-shot localization dataset originating from photographers who authentically were trying to learn about the visual content in the images they took. It includes nearly 10,000 segmentations of 100 categories in over 4,500 images that were taken by people with visual impairments.
An instance segmentation dataset of yeast cells in microstructures. The dataset includes 493 densely annotated microscopy images. For more information see the paper "An Instance Segmentation Dataset of Yeast Cells in Microstructures".
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)
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ShipRSImageNet is a large-scale fine-grainted dataset for ship detection in high-resolution optical remote sensing images. The dataset contains 3,435 images from various sensors, satellite platforms, locations, and seasons. Each image is around 930×930 pixels and contains ships with different scales, orientations, and aspect ratios. The images are annotated by experts in satellite image interpretation, categorized into 50 object categories images. The fully annotated ShipRSImageNet contains 17,573 ship instances. There are five critical contributions of the proposed ShipRSImageNet dataset compared with other existing remote sensing image datasets. Images are collected from various remote sensors cover- ing multiple ports worldwide and have large variations in size, spatial resolution, image quality, orientation, and environment. Ships are hierarchically classified into four levels and 50 ship categories. The number of images, ship instances, and ship cate- gories is larger than that in