19,997 machine learning datasets
19,997 dataset results
A list of all proceedings retrieved from the two-stage keyword (key first, key second in the csv file) filtering approach and the list of all evaluated and reviewed papers by four authors to identify the relevant papers.
The dataset contains 256x256 tiles extracted from Whole Slide Images (WSI) of mouse liver tissue stained with H&E and Masson's Trichrome. WSIs were acquired with a Zeiss AxioScan scanner with a 20× objective at a resolution of 0.221 µm/pix and subsequently subsampled with a factor of 1:2, which resulted in a 0.442 µm/pixel resolution.
Car crash dataset RUSSIA 2022-2023 is a big driving video dataset that contains over 500 high-resolution videos of various driving scenarios. The dataset was created to aid the development and testing of autonomous driving systems and other related technologies. It includes videos from Russia, captured from a diverse set of locations, weather conditions, and lighting conditions, each video lasting about 10 seconds. The videos are annotated with bounding boxes around objects such as different types of cars, pedestrians, and cyclists, as well as traffic signs, and traffic lights. Additionally, the dataset includes metadata information for each video.Car crash dataset RUSSIA 2022-2023 is considered to be one of the few datasets from Russia on this topic. Created by 7 students from Moscow, MIEM HSE. First version published on 4th May, 2023.
MINDS-14 is a dataset designed for the intent detection task with spoken data. It encompasses 14 distinct intents extracted from a commercial system in the e-banking domain. These intents are associated with spoken examples in 14 diverse language varieties. The dataset serves as a valuable resource for training and evaluating intent detection models.
About the MNAD Dataset The MNAD corpus is a collection of over 1 million Moroccan news articles written in modern Arabic language. These news articles have been gathered from 11 prominent electronic news sources. The dataset is made available to the academic community for research purposes, such as data mining (clustering, classification, etc.), information retrieval (ranking, search, etc.), and other non-commercial activities.
New open access dataset of handwritten texts images in Russian
X-Wines is a consistent wine dataset containing 100,646 instances and 21 million real evaluations carried out by users. Data were collected on the open Web in 2022 and pre-processed for wider free use. They refer to the scale 1–5 ratings carried out over a period of 10 years (2012–2021) for wines produced in 62 different countries.
The dataset comprises patches of size 512x512 pixels collected from Sentinel-2 L2A satellite mission. All reported forest fires are located in California. For each area of interest, two images are provided: pre-fire acquisition and post-fire acquisition. Each image is composed of 12 different channels, collecting information from the visible spectrum, infrared and ultrablue.
The dataset contains 105,811 information-seeking conversations converted from MS MARCO. This dataset is constructed to relieve the data scarcity problem of conversational search to an extent. Considering the multi-intent problem and contextual information, this large-scale intent-oriented and context-aware dataset is automatically constructed based on the web search session data in MS MARCO. This dataset can be used to train and evaluate conversational search systems.
Abstract: We introduce the multi-spectral stereo (MS2) outdoor dataset, including stereo RGB, stereo NIR, stereo thermal, stereo LiDAR data, and GPS/IMU information. Our dataset provides rectified and synchronized 184K data pairs taken from city, residential, road, campus, and suburban areas in the morning, daytime, and nighttime under clear-sky, cloudy, and rainy conditions. We designed the dataset to explore various computer vision algorithms from multi-spectral sensor data to achieve high-level performance, reliability, and robustness against challenging environments.
The MICCAI iSEG dataset was described in "https://iseg2017.web.unc.edu/how-to-cite/", that has a total of 10 images, including T1-1 through T1-10, T2-1 through T2-10, and a ground truth for the training set. And there are 13 images, T-11 through T-23, including T-11 through T-23 for test set.
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The dataset is organized into 24 typical scenarios, showcasing the richness of real-world environments, conditions, and objects. It is carefully curated to reflect diverse and realistic situations, allowing models to be tested and refined under a wide range of conditions.
Multi-grained Vehicle Parsing (MVP) is a large-scale dataset for semantic analysis of vehicles in the wild, which has several featured properties. 1. The MVP contains 24,000 vehicle images captured in read-world surveillance scenes, which makes it more scalable for real applications. 2. For different requirements, we annotate the vehicle images with pixel-level part masks in two granularities, i.e., the coarse annotations of ten classes and the fine annotations of 59 classes. The former can be applied to object-level applications such as vehicle Re-Id, fine-grained classification, and pose estimation, while the latter can be explored for high-quality image generation and content manipulation. 3. The images reflect the complexity of real surveillance scenes, such as different viewpoints, illumination conditions, backgrounds, and etc. In addition, the vehicles have diverse countries, types, brands, models, and colors, which makes the dataset more diverse and challenging.
Im-Promptu Visual Analogy Suite is a meta-learning framework. Each visual analogy suite is divided into two broad kind of analogies depending on the underlying relation - Primitive and Composite tasks
The free Face dataset made for students and teachers. It contains 10,000 photos with equal distribution of race and gender parameters, along with metadata and facial landmarks. Free to use for research with citation Photos by Generated.Photos.
The University of Padova Body Pose Estimation dataset (UNIPD-BPE) is an extensive dataset for multi-sensor body pose estimation containing both single-person and multi-person sequences with up to 4 interacting people A network with 5 Microsoft Azure Kinect RGB-D cameras is exploited to record synchronized high-definition RGB and depth data of the scene from multiple viewpoints, as well as to estimate the subjects’ poses using the Azure Kinect Body Tracking SDK. Simultaneously, full-body Xsens MVN Awinda inertial suits allow obtaining accurate poses and anatomical joint angles, while also providing raw data from the 17 IMUs required by each suit. All the cameras and inertial suits are hardware synchronized, while the relative poses of each camera with respect to the inertial reference frame are calibrated before each sequence to ensure maximum overlap of the two sensing systems outputs.
UT Zappos50K (UT-Zap50K) is a large shoe dataset consisting of 50,025 catalog images collected from Zappos.com. The images are divided into 4 major categories — shoes, sandals, slippers, and boots — followed by functional types and individual brands. The shoes are centered on a white background and pictured in the same orientation for convenient analysis. This dataset is created in the context of an online shopping task, where users pay special attentions to fine-grained visual differences. For instance, it is more likely that a shopper is deciding between two pairs of similar men's running shoes instead of between a woman's high heel and a man's slipper. GIST and LAB color features are provided. In addition, each image has 8 associated meta-data (gender, materials, etc.) labels that are used to filter the shoes on Zappos.com. We introduced this dataset in the context of a pairwise comparison task, where the goal is to predict which of two images more strongly exhibits a visual attribu
This is a machine-learning-ready glaucoma dataset using a balanced subset of standardized fundus images from the Rotterdam EyePACS AIROGS train set. This dataset is split into training, validation, and test folders which contain 2500, 270, and 500 fundus images in each class respectively. Each training set has a folder for each class: referable glaucoma (RG) and non-referable glaucoma (NRG).
Standardized Multi-Channel Dataset for Glaucoma (SMDG-19) is a collection and standardization of 19 public datasets, comprised of full-fundus glaucoma images, associated image metadata like, optic disc segmentation, optic cup segmentation, blood vessel segmentation, and any provided per-instance text metadata like sex and age. This dataset is the largest public repository of fundus images with glaucoma.