The PNG dataset consists of image-text pairs. Unlike datasets such as RefCOCO, PNG dataset is characterized by lengthy descriptions of all the objects and their relationships within the complete image.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
## Overview
PnG V2 is a dataset for object detection tasks - it contains Product 5huv annotations for 212 images.
## Getting Started
You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.
## License
This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).
The U.S. Geological Survey (USGS) mapped approximately 336 square kilometers of the lower shoreface and inner-continental shelf offshore of Fire Island, New York in 2011 using interferometric sonar and high-resolution chirp seismic-reflection systems. These spatial data support research on the Quaternary evolution of the Fire Island coastal system and provide baseline information for research on coastal processes along southern Long Island. For more information about the WHCMSC Field Activity, see https://cmgds.marine.usgs.gov/fan_info.php?fan=2011-005-FA.
MNIST WebDataset PNG
The MNIST dataset with samples stored as PNG images and compiled into the WebDataset format.
DALI/JAX Example
The following code shows how this dataset can be loaded into JAX arrays by DALI. from nvidia.dali import pipeline_def import nvidia.dali.fn as fn import nvidia.dali.types as types from nvidia.dali.plugin.jax import DALIGenericIterator from nvidia.dali.plugin.base_iterator import LastBatchPolicy
def get_data_iterator(batch_size, dataset_path):… See the full description on the dataset page: https://huggingface.co/datasets/hayden-donnelly/mnist-webdataset-png.
Public Domain Mark 1.0https://creativecommons.org/publicdomain/mark/1.0/
License information was derived automatically
The PNGDSP embodies the principles of the Constitution of PNG and reinforces the fundamental directives required to advance PNG into a middle-income country by 2030.
FIGR-SVG-png-caption Dataset
Overview
The FIGR-SVG-png-caption dataset is an extension of the FIGR-8 and FIGR-8-SVG datasets, introduced in the FIGR-8 research paper and its subsequent SVG version (FIGR-8-SVG GitHub repository). This dataset has been enhanced with captions generated by Large Language Models (LLMs) as used in the IconShop project (IconShop paper). Originally in SVG format, the images have been converted to 200 x 200 PNG files for this dataset. This… See the full description on the dataset page: https://huggingface.co/datasets/yxxshin/FIGR-SVG-png-caption.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
## Overview
Dental PNG is a dataset for object detection tasks - it contains Pathologies annotations for 339 images.
## Getting Started
You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.
## License
This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).
Public Domain Mark 1.0https://creativecommons.org/publicdomain/mark/1.0/
License information was derived automatically
Download from IUCN www.iucnredlist.org on 2nd of April 2019
A geophysical and geological survey was conducted at the mouth of the Connecticut River from Old Saybrook to Essex, Connecticut, in September 2012. Approximately 230 linear kilometers of digital Chirp subbottom (seismic-reflection) and 234-kilohertz interferometric sonar (bathymetric and backscatter) data were collected along with sediment samples, riverbed photographs, and (or) video at 88 sites within the geophysical survey area. Sediment grab samples were collected at 72 of the 88 sampling sites, video was acquired at 68 sites, and photographs of the river bottom were taken at 38 sites. These survey data are used to characterize the riverbed by identifying sediment-texture and riverbed morphology. More information can be found on the web page for the Woods Hole Coastal and Marine Science Center field activity: https://cmgds.marine.usgs.gov/fan_info.php?fan=2012-024-FA. Data collected during the 2012 survey can be obtained here: https://doi.org/10.5066/F7PG1Q7V.
https://pacific-data.sprep.org/dataset/data-portal-license-agreements/resource/de2a56f5-a565-481a-8589-406dc40b5588https://pacific-data.sprep.org/dataset/data-portal-license-agreements/resource/de2a56f5-a565-481a-8589-406dc40b5588
Fifth National Report on the Species richness of PNG and world higher vertebrates
https://pacific-data.sprep.org/dataset/data-portal-license-agreements/resource/de2a56f5-a565-481a-8589-406dc40b5588https://pacific-data.sprep.org/dataset/data-portal-license-agreements/resource/de2a56f5-a565-481a-8589-406dc40b5588
Maximum Flow, minimum flow and discharge (flow) datasets in cubic feet and the latter in acre feet. Data collected by the Department of Works (Commonwealth of Australia) from 1954 to 1964 (10 year period). Data extracted from PNG State of the Environment (SOE) Report 2020 (page 198)
https://pacific-data.sprep.org/dataset/data-portal-license-agreements/resource/de2a56f5-a565-481a-8589-406dc40b5588https://pacific-data.sprep.org/dataset/data-portal-license-agreements/resource/de2a56f5-a565-481a-8589-406dc40b5588
Link to the PNG Mineral Resources Authority portal. Available layers:
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
PNG Invasive Species list as of April, 2018 - The List is a collective compilation discussed in collaboration by groups, organisations, institutions in the PNG agriculture industry during three one day regional workshops in April 2018. The List was then submitted to the GBIF Project following verification from NAQIA. *Thank you to GBIF and the BID programme for their support in mobilizing this dataset ** Publication of this dataset was funded by the European Union
http://opendatacommons.org/licenses/dbcl/1.0/http://opendatacommons.org/licenses/dbcl/1.0/
This dataset was created by Mohan
Released under Database: Open Database, Contents: Database Contents
The U.S. Geological Survey (USGS) conducted a geophysical and sampling survey in October 2014 that focused on a series of shoreface-attached ridges offshore of western Fire Island, NY. Seismic-reflection data, surficial grab samples and bottom photographs and video were collected along the lower shoreface and inner continental shelf. The purpose of this survey was to assess the impact of Hurricane Sandy on this coastal region. These data were compared to seismic-reflection and surficial sediment data collected by the USGS in the same area in 2011 to evaluate any post-storm changes in seabed morphology and modern sediment thickness on the inner continental shelf. For more information about the WHCMSC Field Activity, see: https://cmgds.marine.usgs.gov/fan_info.php?fan=2014-009-FA.
This dataset was created by Awsaf
This dataset was created by Manoj Prabhakar
skungk/composition-png dataset hosted on Hugging Face and contributed by the HF Datasets community
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
This dataset was created by lucaskg
Released under CC0: Public Domain
During 1975, the U.S. Geological Survey (USGS) conducted a seismic-reflection survey utilizing Uniboom seismics in eastern Rhode Island Sound aboard the Research Vessel Asterias. This cruise totalled 8 survey days. Data from this survey were recorded in analog form and archived at the USGS. Due to recent interest in the geology of Rhode Island Sound and in an effort to make the data more readily accessible while preserving the original paper records, the seismic data from this cruise were scanned and converted to TIFF images and SEG-Y data files. Navigation data were converted from LORAN-C time delays to latitudes and longitudes, which are available in ESRI shapefile format and as eastings and northings in space-delimited text format.
The PNG dataset consists of image-text pairs. Unlike datasets such as RefCOCO, PNG dataset is characterized by lengthy descriptions of all the objects and their relationships within the complete image.