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Image classification, object detection, segmentation, face recognition, OCR, image generation, video understanding
16,127 datasets
Loss values likely recorded during the training of a Deep Convolutional Generative Adversarial Network (DCGAN). The dataset is hosted on Kaggle, but its specific creation date, author, and the exact number of records are unknown. Columns suggest it contains metrics for monitoring the generator and discriminator performance over training iterations.
A collection of real-world images for computer vision tasks. The dataset appears to be designed for training YOLO-based segmentation models, specifically for detecting crosswalks. It was published on Kaggle, but details on its size, origin, and creation date are not provided.
State of Oregon's Division of Financial Regulation maintains a catalog of established local rates submitted by ground ambulance service organizations, as mandated by HB 3243. The data supports transparency in healthcare billing between insurers and service providers. The dataset is available in multiple formats including CSV, JSON, RDF, and XML.
yolo-efficentnet-weights is a dataset published on Kaggle. The title suggests it contains pre-trained model weights for a YOLO-EfficientNet hybrid architecture. The dataset's specific content, size, and origin are not detailed in the provided metadata.
A collection of pre-trained model weights for the YOLO (You Only Look Once) object detection architecture, published on Kaggle. The dataset likely contains the parameter files necessary to initialize or fine-tune YOLO models for various computer vision tasks. Specific details on the model versions, training data, and performance metrics are not provided in the available metadata.
A custom YOLO model designed for detecting motorcycle helmet usage in real-time. The dataset was sourced from Kaggle, but specific details about its size, creation date, and author are not provided. It is intended for computer vision tasks related to safety compliance and traffic monitoring.
DECEganAUG is a dataset published on Kaggle. Its title suggests a focus on data augmentation using Generative Adversarial Networks. The dataset's specific content, size, and origin are not detailed in the available metadata.
Object detection data likely intended for training YOLOv8 models. The dataset is hosted on Kaggle, but its specific contents, scale, and origin are not detailed in the provided metadata. Columns and sample data are unknown, requiring verification after download.
A dataset from Kaggle likely containing electrocardiogram (ECG) signals and associated Generative Adversarial Network (GAN) components. The title suggests it includes generated ECG data and discriminator outputs, potentially derived from the MIT-BIH Arrhythmia Database. Its exact size, scope, and creation details are unspecified.
A large-scale synthetic dataset for Urdu Optical Character Recognition (OCR) created by PuristanLabs1. It contains 1.5 million samples, including 499,845 rendered with authentic Jameel Noori Nastaliq ligatures and 1,000,160 in standard Urdu Naskh fonts. The dataset was last updated on February 1,ๆไปฌๅ็ฐไบไธไธช้ฎ้ขใ
Organochlorine resistance reported for at least one site, indicating locations where disease-carrying insects show resistance to this class of insecticides. The dataset is provided by the World Health Organization (WHO) as part of its Global Health Observatory (GHO) on vector control and pesticide resistance.
Data from the World Health Organization (WHO) on the role of non-governmental organizations in dementia policy development. The dataset covers global health perspectives and is categorized under Dementia Care and Policy Analysis. Specific temporal coverage, row count, and column details are not provided.
National-level data enumerates non-governmental organization office branches dedicated to dementia care. The dataset is provided by the World Health Organization (WHO) through its Global Health Observatory platform. Temporal coverage and record count are unspecified.
Local office branch locations for nongovernmental organizations (NGOs) focused on dementia care. The dataset is provided by the World Health Organization (WHO) and is part of their Global Health Observatory data. Specific temporal coverage and record counts are not detailed in the provided metadata.
Dementia nongovernmental organization office branches (Sub-national) provides a listing of NGO facilities focused on dementia care. The dataset is compiled by the World Health Organization (WHO) and includes subnational location data. The specific number of records and last update date are not provided.
Dementia-focused non-governmental organizations equipped with office facilities are documented by the World Health Organization. The dataset provides a structured view of health infrastructure supporting dementia care globally. Specific row counts and update frequency are not provided.
World Health Organization data on governmental financial allocations to non-governmental organizations supporting dementia-related activities and services. The dataset covers funding amounts, recipient organizations, and the specific services funded. It is compiled by the WHO from national health accounts and expenditure reports.
Staffing data for nongovernmental organizations involved in dementia care, collected by the World Health Organization. The dataset categorizes staff primarily as salaried or volunteer. It is part of the WHO's Global Health Observatory data on the health workforce.
Objectron contains 15,000 object-centric video clips and 4 million annotated images across nine categories, produced by Google Research and updated in 2026. Each video includes AR session metadata such as camera poses, sparse point-clouds, and planes, with objects annotated via 3D bounding boxes.
ARM-Thinker-Data is a multimodal dataset for training agentic reward models in evidence-grounded reasoning. The data is annotated by Qwen3-VL-235B-A22B-Instruct, Qwen3-VL-235B-A22B-Thinking, and GPT-4o models. It supports tasks including image-text-to-text, question answering, and tool use.