The TrashCan dataset is an instance-segmentation dataset of underwater trash. It is comprised of annotated images (7,212 images) which contain observations of trash, ROVs, and a wide variety of undersea flora and fauna. The annotations in this dataset take the format of instance segmentation annotations: bitmaps containing a mask marking which pixels in the image contain each object. The imagery in TrashCan is sourced from the J-EDI (JAMSTEC E-Library of Deep-sea Images) dataset, curated by the Japan Agency of Marine Earth Science and Technology (JAMSTEC).
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A Multi-Task 4D Radar-Camera Fusion Dataset for Autonomous Driving on Water Surfaces description of the dataset
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Bamboo Dataset is a mega-scale and information-dense dataset for both classification and detection pre-training. It is built upon integrating 24 public datasets (e.g. ImagenNet, Places365, Object365, OpenImages) and added new annotations through active learning. Bamboo has 69M image classification annotations and 32M object bounding boxes.
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BigDetection is a new large-scale benchmark to build more general and powerful object detection systems. It leverages the training data from existing datasets (LVIS, OpenImages and Object365) with carefully designed principles, and curate a larger dataset for improved detector pre-training. BigDetection dataset has 600 object categories and contains 3.4M training images with 36M object bounding boxes.
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Cops-Ref is a dataset for visual reasoning in context of referring expression comprehension with two main features.
This dataset is a collection of spoken language instructions for a robotic system to pick and place common objects. Text instructions and corresponding object images are provided. The dataset consists of situations where the robot is instructed by the operator to pick up a specific object and move it to another location: for example, Move the blue and white tissue box to the top right bin. This dataset consists of RGBD images, bounding box annotations, destination box annotations, and text instructions.
Aiming Detect small obstacles, like lost and found.
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SpaceNet 2: Building Detection v2 - is a dataset for building footprint detection in geographically diverse settings from very high resolution satellite images. It contains over 302,701 building footprints, 3/8-band Worldview-3 satellite imagery at 0.3m pixel res., across 5 cities (Rio de Janeiro, Las Vegas, Paris, Shanghai, Khartoum), and covers areas that are both urban and suburban in nature. The dataset was split using 60%/20%/20% for train/test/validation.
TTPLA is a public dataset which is a collection of aerial images on Transmission Towers (TTs) and Power Lines (PLs). It can be used for detection and segmentation of transmission towers and power lines. It consists of 1,100 images with the resolution of 3,840×2,160 pixels, as well as manually labelled 8,987 instances of TTs and PLs.
YouTube-BoundingBoxes (YT-BB) is a large-scale data set of video URLs with densely-sampled object bounding box annotations. The data set consists of approximately 380,000 video segments about 19s long, automatically selected to feature objects in natural settings without editing or post-processing, with a recording quality often akin to that of a hand-held cell phone camera. The objects represent a subset of the MS COCO label set. All video segments were human-annotated with high-precision classification labels and bounding boxes at 1 frame per second.
The Annotated Germs for Automated Recognition (AGAR) dataset is an image database of microbial colonies cultured on an agar plate. It contains 18000 photos of five different microorganisms, taken under diverse lighting conditions with two different cameras. All images are classified into countable, uncountable, and empty, with the former being labeled by microbiologists with colony location and 5 species identification (336 442 colonies).
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BAAI-VANJEE is a dataset for benchmarking and training various computer vision tasks such as 2D/3D object detection and multi-sensor fusion. The BAAI-VANJEE roadside dataset consists of LiDAR data and RGB images collected by VANJEE smart base station placed on the roadside about 4.5m high. This dataset contains 2500 frames of LiDAR data, 5000 frames of RGB images, including 20% collected at the same time. It also contains 12 classes of objects, 74K 3D object annotations and 105K 2D object annotations.
The EuroCity Persons dataset provides a large number of highly diverse, accurate and detailed annotations of pedestrians, cyclists and other riders in urban traffic scenes. The images for this dataset were collected on-board a moving vehicle in 31 cities of 12 European countries. With over 238,200 person instances manually labeled in over 47,300 images, EuroCity Persons is nearly one order of magnitude larger than person datasets used previously for benchmarking. The dataset furthermore contains a large number of person orientation annotations (over 211,200).
Falling Things (FAT) is a dataset for advancing the state-of-the-art in object detection and 3D pose estimation in the context of robotics. It consists of generated photorealistic images with accurate 3D pose annotations for all objects in 60k images.
Freiburg Groceries is a groceries classification dataset consisting of 5000 images of size 256x256, divided into 25 categories. It has imbalanced class sizes ranging from 97 to 370 images per class. Images were taken in various aspect ratios and padded to squares.
IIIT-AR-13K is created by manually annotating the bounding boxes of graphical or page objects in publicly available annual reports. This dataset contains a total of 13k annotated page images with objects in five different popular categories - table, figure, natural image, logo, and signature. It is the largest manually annotated dataset for graphical object detection.
Lytro Illum is a new light field dataset using a Lytro Illum camera. 640 light fields are collected with significant variations in terms of size, textureness, background clutter and illumination, etc. Micro-lens image arrays and central viewing images are generated, and corresponding ground-truth maps are produced.
A large-scale video dataset for MOR in aerial videos.
MobilityAids is a dataset for perception of people and their mobility aids. The annotated dataset contains five classes: pedestrian, person in wheelchair, pedestrian pushing a person in a wheelchair, person using crutches and person using a walking frame. In total the hospital dataset has over 17, 000 annotated RGB-D images, containing people categorized according to the mobility aids they use. The images were collected in the facilities of the Faculty of Engineering of the University of Freiburg and in a hospital in Frankfurt.
The PS-Battles dataset is gathered from a large community of image manipulation enthusiasts and provides a basis for media derivation and manipulation detection in the visual domain. The dataset consists of 102'028 images grouped into 11'142 subsets, each containing the original image as well as a varying number of manipulated derivatives.
APRICOT is a collection of over 1,000 annotated photographs of printed adversarial patches in public locations. The patches target several object categories for three COCO-trained detection models, and the photos represent natural variation in position, distance, lighting conditions, and viewing angle.
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The complete blood count (CBC) dataset contains 360 blood smear images along with their annotation files splitting into Training, Testing, and Validation sets. The training folder contains 300 images with annotations. The testing and validation folder both contain 60 images with annotations. We have done some modifications over the original dataset to prepare this CBC dataset where some of the image annotation files contain very low red blood cells (RBCs) than actual and one annotation file does not include any RBC at all although the cell smear image contains RBCs. So, we clear up all the fallacious files and split the dataset into three parts. Among the 360 smear images, 300 blood cell images with annotations are used as the training set first, and then the rest of the 60 images with annotations are used as the testing set. Due to the shortage of data, a subset of the training set is used to prepare the validation set which contains 60 images with annotations.
Comprises about 40,000 images where the most suitable objects for 14 tasks have been annotated.
DUO is a dataset for Underwater object detection for robot picking. The dataset contains a collection of diverse underwater images with more rational annotations.
Breast MRI scans of 922 cancer patients from Duke University, with tumor bounding box annotations, clinical, imaging, and many other features, and more.
HJDataset is a large dataset of Historical Japanese Documents with Complex Layouts. It contains over 250,000 layout element annotations of seven types. In addition to bounding boxes and masks of the content regions, it also includes the hierarchical structures and reading orders for layout elements. The dataset is constructed using a combination of human and machine efforts.
HS-SOD is a hyperspectral salient object detection dataset with a collection of 60 hyperspectral images with their respective ground-truth binary images and representative rendered colour images (sRGB).
Kitchen Scenes is a multi-view RGB-D dataset of nine kitchen scenes, each containing several objects in realistic cluttered environments including a subset of objects from the BigBird dataset. The viewpoints of the scenes are densely sampled and objects in the scenes are annotated with bounding boxes and in the 3D point cloud.
5 PAPERS • 1 BENCHMARK
The dataset is split between train, test and val folders.
Satlas is a remote sensing dataset and benchmark that is large in both breadth, featuring all of the aforementioned applications and more, as well as scale, comprising 290M labels under 137 categories and 7 label modalities.
Comprises of 171,191 video segments from 346 high-quality soccer games. The database contains 702,096 bounding boxes, 37,709 essential event labels with time boundary and 17,115 highlight annotations for object detection, action recognition, temporal action localization, and highlight detection tasks.
TICaM is a Time-of-flight In-car Cabin Monitoring dataset for vehicle interior monitoring using a single wide-angle depth camera. This dataset addresses the deficiencies of other available in-car cabin datasets in terms of the ambit of labeled classes, recorded scenarios and provided annotations; all at the same time. It consists of an exhaustive list of actions performed while driving and multi-modal labeled images (depth, RGB and IR), with complete annotations for 2D and 3D object detection, instance and semantic segmentation as well as activity annotations for RGB frames. Additional to real recordings, it also contains a synthetic dataset of in-car cabin images with same multi-modality of images and annotations, providing a unique and extremely beneficial combination of synthetic and real data for effectively training cabin monitoring systems and evaluating domain adaptation approaches.
The CropAndWeed dataset is focused on the fine-grained identification of 74 relevant crop and weed species with a strong emphasis on data variability. Annotations of labeled bounding boxes, semantic masks and stem positions are provided for about 112k instances in more than 8k high-resolution images of both real-world agricultural sites and specifically cultivated outdoor plots of rare weed types. Additionally, each sample is enriched with meta-annotations regarding environmental conditions.
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Digital radiography is widely available and the standard modality in trauma imaging, often enabling to diagnose pediatric wrist fractures. However, image interpretation requires time-consuming specialized training. Due to astonishing progress in computer vision algorithms, automated fracture detection has become a topic of research interest. This paper presents the GRAZPEDWRI-DX dataset containing annotated pediatric trauma wrist radiographs of 6,091 patients, treated at the Department for Pediatric Surgery of the University Hospital Graz between 2008 and 2018. A total number of 10,643 studies (20,327 images) are made available, typically covering posteroanterior and lateral projections. The dataset is annotated with 74,459 image tags and features 67,771 labeled objects. We de-identified all radiographs and converted the DICOM pixel data to 16-Bit grayscale PNG images. The filenames and the accompanying text files provide basic patient information (age, sex). Several pediatric radiolog
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InsPLAD is a Dataset for Power Line Asset Inspection containing 10,607 high-resolution Unmanned Aerial Vehicles colour images. It contains 17 unique power line assets captured from real-world operating power lines. Some of those assets (five, to be precise) are also annotated regarding their conditions. They present the following defects: corrosion (4 of them), broken/missing cap (1 of them), and bird's nest presence (1 of them).
PIDray is a large-scale dataset which covers various cases in real-world scenarios for prohibited item detection, especially for deliberately hidden items. The dataset contains 12 categories of prohibited items in 47, 677 X-ray images with high-quality annotated segmentation masks and bounding boxes.
The evaluation of object detection models is usually performed by optimizing a single metric, e.g. mAP, on a fixed set of datasets, e.g. Microsoft COCO and Pascal VOC. Due to image retrieval and annotation costs, these datasets consist largely of images found on the web and do not represent many real-life domains that are being modelled in practice, e.g. satellite, microscopic and gaming, making it difficult to assert the degree of generalization learned by the model.
The Small Object Detection for Spotting Birds (SOD4SB) dataset is a dataset consisting of 39,070 images including 137,121 bird instances. The SOD4SD dataset contains a wide variety of small bird types and a variety of scenes.
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Specially designed to evaluate active learning for video object detection in road scenes.
SpaceNet 1: Building Detection v1 is a dataset for building footprint detection. The data is comprised of 382,534 building footprints, covering an area of 2,544 sq. km of 3/8 band WorldView-2 imagery (0.5 m pixel res.) across the city of Rio de Janeiro, Brazil. The images are processed as 200m×200m tiles with associated building footprint vectors for training.
VEDAI is a dataset for Vehicle Detection in Aerial Imagery, provided as a tool to benchmark automatic target recognition algorithms in unconstrained environments. The vehicles contained in the database, in addition of being small, exhibit different variabilities such as multiple orientations, lighting/shadowing changes, specularities or occlusions. Furthermore, each image is available in several spectral bands and resolutions. A precise experimental protocol is also given, ensuring that the experimental results obtained by different people can be properly reproduced and compared. We also give the performance of some baseline algorithms on this dataset, for different settings of these algorithms, to illustrate the difficulties of the task and provide baseline comparisons.
The Aircraft Context Dataset, a composition of two inter-compatible large-scale and versatile image datasets focusing on manned aircraft and UAVs, is intended for training and evaluating classification, detection and segmentation models in aerial domains. Additionally, a set of relevant meta-parameters can be used to quantify dataset variability as well as the impact of environmental conditions on model performance.
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Based on simulated challenging conditions that correspond to adversaries that can occur in real-world environments and systems.
FOD in Airports (FOD-A) is an image dataset of FOD, Foreign Object Degris, which consists of 31 object categories and over 30,000 annotation instances. The object categories have been selected based on guidance from prior documentation and related research by the Federal Aviation Administration (FAA).
We introduce an object detection dataset in challenging adverse weather conditions covering 12000 samples in real-world driving scenes and 1500 samples in controlled weather conditions within a fog chamber. The dataset includes different weather conditions like fog, snow, and rain and was acquired by over 10,000 km of driving in northern Europe. The driven route with cities along the road is shown on the right. In total, 100k Objekts were labeled with accurate 2D and 3D bounding boxes. The main contributions of this dataset are: - We provide a proving ground for a broad range of algorithms covering signal enhancement, domain adaptation, object detection, or multi-modal sensor fusion, focusing on the learning of robust redundancies between sensors, especially if they fail asymmetrically in different weather conditions. - The dataset was created with the initial intention to showcase methods, which learn of robust redundancies between the sensor and enable a raw data sensor fusion in cas
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A large-scale logo image database for logo detection and brand recognition from real-world product images.
MJU-Waste is an RGBD waste object segmentation dataset that is made public to facilitate future research in this area.
The MUAD dataset (Multiple Uncertainties for Autonomous Driving), consisting of 10,413 realistic synthetic images with diverse adverse weather conditions (night, fog, rain, snow), out-of-distribution objects, and annotations for semantic segmentation, depth estimation, object, and instance detection. Predictive uncertainty estimation is essential for the safe deployment of Deep Neural Networks in real-world autonomous systems and MUAD allows to a better assess the impact of different sources of uncertainty on model performance.