3,275 machine learning datasets
3,275 dataset results
A large-scale dataset of ~29.5K rain/rain-free image pairs that covers a wide range of natural rain scenes.
Real-World Masked Face Dataset (RMFD) is a large dataset for masked face detection.
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.
TACO is a growing image dataset of waste in the wild. It contains images of litter taken under diverse environments: woods, roads and beaches. These images are manually labelled and segmented according to a hierarchical taxonomy to train and evaluate object detection algorithms. The annotations are provided in COCO format.
Talk The Walk is a large-scale dialogue dataset grounded in action and perception. The task involves two agents (a “guide” and a “tourist”) that communicate via natural language in order to achieve a common goal: having the tourist navigate to a given target location.
The Breast Cancer Histopathological Image Classification (BreakHis) is composed of 9,109 microscopic images of breast tumor tissue collected from 82 patients using different magnifying factors (40X, 100X, 200X, and 400X). It contains 2,480 benign and 5,429 malignant samples (700X460 pixels, 3-channel RGB, 8-bit depth in each channel, PNG format). This database has been built in collaboration with the P&D Laboratory - Pathological Anatomy and Cytopathology, Parana, Brazil.
Synbols is a dataset generator designed for probing the behavior of learning algorithms. By defining the distribution over latent factors one can craft a dataset specifically tailored to answer specific questions about a given algorithm.
Five classic grayscale images commonly used for image quality assessment tasks.
Diabetic retinopathy is the leading cause of blindness in the working-age population of the developed world. It is estimated to affect over 93 million people.
RGB-D-D is a large-scale dataset for depth map super-resolution (SR). It consists of real-world paired low-resolution (LR) and high-resolution (HR) depth maps. The paired LR and HR depth maps are captured from mobile phone and Lucid Helios respectively ranging from indoor scenes to challenging outdoor scenes.
Place Pulse is a crowdsourcing effort that aims to map which areas of a city are perceived as safer, livelier, wealthier, more active, beautiful and friendly. By asking users to select images from a pair, Place Pulse collected more than 1.5 million reports that evaluate more than 100,000 images from 56 cities.
UDIS-D is a large image dataset for image stitching or image registration. It contains different overlap rates, varying degrees of parallax, and variable scenes such as indoor, outdoor, night, dark, snow, and zooming.
RailSem19 offers 8500 unique images taken from a the ego-perspective of a rail vehicle (trains and trams). Extensive semantic annotations are provided, both geometry-based (rail-relevant polygons, all rails as polylines) and dense label maps with many Cityscapes-compatible road labels. Many frames show areas of intersection between road and rail vehicles (railway crossings, trams driving on city streets). RailSem19 is usefull for rail applications and road applications alike.
The first large demoire dataset. The dataset contains 135,000 image pairs, each containing an image contaminated with moire patterns and its corresponding uncontaminated reference image.
Crello dataset consists of design templates obtained from online design service, crello.com. The dataset contains designs for various display formats, such as social media posts, banner ads, blog headers, or printed posters, all in a vector format. In dataset construction, design templates and associated resources (e.g., linked images) from crello.com were first downloaded. After the initial data acquisition, the data structure was inspected and identified useful vector graphic information in each template. Next, mal-formed templates or those having more than 50 elements were eliminated, resulting in 23,182 templates. The data was paritioned to 18,768 / 2,315 / 2,278 examples for train, validation, and test splits.
Depth in the Wild is a dataset for single-image depth perception in the wild, i.e., recovering depth from a single image taken in unconstrained settings. It consists of images in the wild annotated with relative depth between pairs of random points.
Hilti SLAM Challenge is a dataset for Simultaneous Localization and Mapping (SLAM) algorithms due to sparsity, varying illumination conditions, and dynamic objects. The sensor platform used to collect this dataset contains a number of visual, lidar and inertial sensors which have all been rigorously calibrated. All data is temporally aligned to support precise multi-sensor fusion. Each dataset includes accurate ground truth to allow direct testing of SLAM results. Raw data as well as intrinsic and extrinsic sensor calibration data from twelve datasets in various environments is provided. Each environment represents common scenarios found in building construction sites in various stages of completion.
Breast cancer (BC) has become the greatest threat to women’s health worldwide. Clinically, identification of axillary lymph node (ALN) metastasis and other tumor clinical characteristics such as ER, PR, and so on, are important for evaluating the prognosis and guiding the treatment for BC patients.
nvBench is a large-scale NL2VIS (natural languagge to visualisations) benchmark, containing 25,750 (NL, VIS) pairs from 750 tables over 105 domains, synthesized from (NL, SQL) benchmarks to support cross-domain NLPVIS (Natural Language Query to Visualization) task.
VinDr-CXR is an open large-scale dataset of chest X-rays with radiologist’s annotations. It's bult from more than 100,000 raw images in DICOM format that were retrospectively collected from the Hospital 108 and the Hanoi Medical University Hospital, two of the largest hospitals in Vietnam. The published dataset consists of 18,000 postero-anterior (PA) view CXR scans that come with both the localization of critical findings and the classification of common thoracic diseases. These images were annotated by a group of 17 radiologists with at least 8 years of experience for the presence of 22 critical findings (local labels) and 6 diagnoses (global labels); each finding is localized with a bounding box. The local and global labels correspond to the “Findings” and “Impressions” sections, respectively, of a standard radiology report.