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Image classification, object detection, segmentation, face recognition, OCR, image generation, video understanding
16,074 datasets
A collection of images generated by a Projected GAN model, likely using the Deep Feature (DF) variant. The dataset is hosted on Kaggle, but its exact size, creation date, and author are unspecified. The content appears to be synthetic visual data intended for machine learning experimentation.
Kaggle hosts a dataset titled 'projected-gan-scc-generated'. The dataset likely contains synthetic images generated by a Projected Generative Adversarial Network (GAN), possibly for the SCC (Semantic Composition Consistency) task. Its specific content, scale, and creator are not detailed in the provided metadata.
A dataset titled 'nok-grain-yolo-dataset' was published on Kaggle. The title suggests it contains images of grains for object detection tasks, likely using the YOLO model framework. No further metadata on size, source, or creation date is available.
YOLO-Net is a dataset for object detection tasks, published on Kaggle. The dataset's specific content, size, and creation details are not provided in the available metadata. Further verification after download is required to confirm its scope and composition.
my-yolo-lite-project is a dataset hosted on Kaggle. Its title suggests a focus on object detection, likely using the YOLO (You Only Look Once) model architecture. The dataset's specific content, size, and origin are not detailed in the provided metadata.
A dataset titled 'ocr_region_crops' is hosted on Kaggle. The dataset's content likely relates to images containing text (OCR) and crop regions, potentially for agricultural or document analysis. Specific details on size, origin, and creation date are unavailable from the provided metadata.
A set of YOLOv8m model weights for object detection, trained on the BDD100K dataset. The dataset was published on Kaggle, but the specific training date and model performance metrics are not provided in the metadata. The original BDD100K dataset is a large-scale driving scene benchmark.
yolobj365finetune2400 is a dataset hosted on Kaggle, likely intended for fine-tuning object detection models. The dataset's title and platform tags suggest it contains images annotated for the YOLO (You Only Look Once) framework. Its specific content, size, and origin require verification after download.
Wajahyolov5 is a dataset published on Kaggle. Its title suggests a focus on object detection, likely using the YOLOv5 architecture. No further metadata is available to confirm its specific contents or scale.
ARPE CNN Training v2.1 is a dataset published on Kaggle. Its title suggests it contains image data for training Convolutional Neural Networks. The dataset's specific content, size, and origin are not detailed in the available metadata.
A pre-trained model file for the EfficientNetB4 architecture, published on Kaggle. The file contains learned weights for image classification tasks, likely trained on a large-scale image dataset. The author, organization, and specific training details are unknown.
AmeriFlux carbon flux data for the US-Wkg Walnut Gulch Kendall Grasslands site. Eddy covariance measurements of energy, water and CO2 fluxes began in spring 2004, with meteorological and hydrological data available further back. The dataset originates from a USDA-ARS experimental watershed.
A high-resolution historical shoreline dataset for Ocracoke Inlet, North Carolina, automated for use as a GIS data layer. The data were derived from shoreline maps produced by the NOAA National Ocean Service and its predecessor agencies, based on imagery interpretation and field surveys. The attribution follows the NGS-developed C-COAST scheme, influenced by the IHO S-57 standard for translation.
NOAA National Ocean Service provides a high-resolution historical shoreline for Coasters Harbor Island and Narragansett Bay, Rhode Island, derived from office interpretation of imagery and field surveys. The data is structured using the NGS-developed C-COAST attribution scheme to facilitate translation into international hydrographic standards. This resource is part of a larger NOAA data collection identified by InPort ID 39808.
A high-resolution vector shoreline dataset compiled from imagery for the Louisiana coast from New Orleans to Morgan City. The data is based on office interpretation of imagery and uses the NOAA-developed Coastal Cartographic Object Attribute Source Table (C-COAST) attribution scheme. This resource is a member of NOAA's InPort catalog item 39808.
A high-resolution historical shoreline dataset for the western end of Lake Erie, spanning Ohio and Michigan. The data were automated from shoreline maps produced by the NOAA National Ocean Service and its predecessor agencies, based on imagery interpretation and field surveys. It is structured using the NGS C-COAST attribution scheme to facilitate translation into international hydrographic standards.
A high-resolution historical shoreline dataset for the Marquette to Au Sable Point region of Lake Superior, Michigan, automated for GIS use. The data are derived from shoreline maps produced by the NOAA National Ocean Service and its predecessor agencies, based on imagery interpretation and field surveys. The attribution follows the NGS-developed C-COAST scheme, influenced by the International Hydrographic Organization's S-57 standard.
NOAA's National Ocean Service provides a high-resolution vector shoreline for Sheboygan, Wisconsin, compiled from aerial or satellite imagery. The data uses the NGS-developed C-COAST attribution scheme to standardize features for potential translation into hydrographic standards. This resource is part of a larger NOAA shoreline mapping program, last updated in March 2026.
ROCR is a software package for visualizing classifier performance, authored by Tobias Sing. It enables the creation of cutoff-parameterized 2D performance curves by combining over 25 different performance measures. The tool supports averaging curves from cross-validation or bootstrapping runs and visualizing variability with standard deviations or box plots.
SemEval-2013 Task 13 data originally provided by organizers David Jurgens and Sapienza University of Rome. The dataset is designed for the Word Sense Induction for Graded and Non-Graded Senses task. The specific number of rows, columns, and file size are unknown from the provided metadata.