KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) is one of the most popular datasets for use in mobile robotics and autonomous driving. It consists of hours of traffic scenarios recorded with a variety of sensor modalities, including high-resolution RGB, grayscale stereo cameras, and a 3D laser scanner. Despite its popularity, the dataset itself does not contain ground truth for semantic segmentation. However, various researchers have manually annotated parts of the dataset to fit their necessities. Álvarez et al. generated ground truth for 323 images from the road detection challenge with three classes: road, vertical, and sky. Zhang et al. annotated 252 (140 for training and 112 for testing) acquisitions – RGB and Velodyne scans – from the tracking challenge for ten object categories: building, sky, road, vegetation, sidewalk, car, pedestrian, cyclist, sign/pole, and fence. Ros et al. labeled 170 training images and 46 testing images (from the visual odome
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Office-Home is a benchmark dataset for domain adaptation which contains 4 domains where each domain consists of 65 categories. The four domains are: Art – artistic images in the form of sketches, paintings, ornamentation, etc.; Clipart – collection of clipart images; Product – images of objects without a background and Real-World – images of objects captured with a regular camera. It contains 15,500 images, with an average of around 70 images per class and a maximum of 99 images in a class.
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The Sims4Action Dataset: a videogame-based dataset for Synthetic→Real domain adaptation for human activity recognition.
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This dataset contains Bangla handwritten numerals, basic characters and compound characters. This dataset was collected from multiple geographical location within Bangladesh and includes sample collected from a variety of aged groups. This dataset can also be used for other classification problems i.e: gender, age, district.
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Moral Foundations Reddit Corpus (MFRC) is a collection of 16,123 Reddit comments that have been curated from 12 distinct subreddits, hand-annotated by at least three trained annotators for 8 categories of moral sentiment (i.e., Care, Proportionality, Equality, Purity, Authority, Loyalty, Thin Morality, Implicit/Explicit Morality) based on the updated Moral Foundations Theory (MFT) framework.
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By releasing this dataset, we aim at providing a new testbed for computer vision techniques using Deep Learning. The main peculiarity is the shift from the domain of "natural images" proper of common benchmark dataset to biological imaging. We anticipate that the advantages of doing so could be two-fold: i) fostering research in biomedical-related fields - for which popular pre-trained models perform typically poorly - and ii) promoting methodological research in deep learning by addressing peculiar requirements of these images. Possible applications include but are not limited to semantic segmentation, object detection and object counting. The data consist of 283 high-resolution pictures (1600x1200 pixels) of mice brain slices acquired through a fluorescence microscope. The final goal is to individuate and count neurons highlighted in the pictures by means of a marker, so to assess the result of a biological experiment. The corresponding ground-truth labels were generated through a hy
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Unsupervised Domain Adaptation demonstrates great potential to mitigate domain shifts by transferring models from labeled source domains to unlabeled target domains. While Unsupervised Domain Adaptation has been applied to a wide variety of complex vision tasks, only few works focus on lane detection for autonomous driving. This can be attributed to the lack of publicly available datasets. To facilitate research in these directions, we propose CARLANE, a 3-way sim-to-real domain adaptation benchmark for 2D lane detection. CARLANE encompasses the single-target datasets MoLane and TuLane and the multi-target dataset MuLane. These datasets are built from three different domains, which cover diverse scenes and contain a total of 163K unique images, 118K of which are annotated. In addition we evaluate and report systematic baselines, including our own method, which builds upon Prototypical Cross-domain Self-supervised Learning. We find that false positive and false negative rates of the eva
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There was no predefined dataset of party symbols to be usedas a benchmark. We curated a dataset from various nationaland regional websites owned by the ECI. The dataset consists of symbols (image files) of 49 National and State registered parties approved by the ECI. For each image of theoriginal party symbol, 18 different distortions and transformations were created as variations to the training data. Each image is of the dimension 180 x 180. The final labeled dataset consists of 931 images of party symbols with their corresponding party names as the labels.
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According to the WHO, World report on vision 2019, the number of visually impaired people worldwide is estimated to be 2.2 billion, of whom at least 1 billion have a vision impairment that could have been prevented or is yet to be addressed. The world faces considerable challenges in terms of eye care, including inequalities in the coverage and quality of prevention, treatment, and rehabilitation services. Early detection and diagnosis of ocular pathologies would enable forestall of visual impairment. One challenge that limits the adoption of a computer-aided diagnosis tool by the ophthalmologist is, the sight-threatening rare pathologies such as central retinal artery occlusion or anterior ischemic optic neuropathy and others are usually ignored. In the past two decades, many publicly available datasets of color fundus images have been collected with a primary focus on diabetic retinopathy, glaucoma, and age-related macular degeneration, and few other frequent pathologies. The challe
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SpaRTUN a dataset synthesized for transfer learning on spatial question answering (SQA) and spatial role labeling (SpRL).
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The dataset contains more than 100k code patch pairs extracted from open source projects on GitHub. Each pair comes with the erroneous and the fixed version of the corresponding code snippet. Instead of the whole file, the code snippets are extracted to focus on the problematic region (error line + other lines around it). For each sample, the repository name, the commit id, and the file names are provided so that one can access the complete files in case of interest.
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Dataset contains images with apples infected by scab. The images are grouped in two folders: "Healthy" and "Scab". The collection of digital images were carried out in different locations of Latvia. Digital images with characteristic scab symptoms on fruits were collected by the Institute of Horticulture (LatHort) under project "lzp-2019/1-0094 Application of deep learning and datamining for the study of plant-pathogen interaction: the case of apple and pear scab" with a goal to create mobile application for apple scab detection using convolution neural networks. Devices: smartphone cameras (12 MP, 13 MP, 48 MP) and a digital compact camera (10 MP). The collection of images was carried out in field conditions, in orchards. The images were taken at three different stages of the day - in the morning (9:00-10:00), around noon (12:00-14:00), as well as in the evening (16:00-17:00) to provide a variety of natural light conditions. The images were also taken on both sunny days and overcast d
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Dataset contains images with apple leaves infected by scab. The images are grouped in two folders: "Healthy" and "Scab". The collection of digital images were carried out in different locations of Latvia. Digital images with characteristic scab symptoms on leaves were collected by the Institute of Horticulture (LatHort) under project "lzp-2019/1-0094 Application of deep learning and datamining for the study of plant-pathogen interaction: the case of apple and pear scab" with a goal to create mobile application for apple scab detection using convolution neural networks. Devices: smartphone cameras (12 MP, 13 MP, 48 MP) and a digital compact camera (10 MP). The collection of images was carried out in field conditions, in orchards. The images were taken at three different stages of the day - in the morning (9:00-10:00), around noon (12:00-14:00), as well as in the evening (16:00-17:00) to provide a variety of natural light conditions. The images were also taken on both sunny days and over
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