This repository contains the JPEG version of Mini-DDBS breast cancer dataset from Kaggle. Sinced the whole dataset is 50GB only the JPEG version is extracted and uploaded here for faster retrieving.

In freshwater bodies of New Hampshire, the most problematic aquatic invasive plant species is Myriophyllum heterophyllum or variable leaf water-milfoil. Once established, variable leaf water-milfoil forms dense beds that can alter the limnologic characteristics of a waterbody, impacting natural lacustrine communities and their habitats. Variable leaf water-milfoil infestations also disrupt recreational uses of waterbodies and have negatively affected swimming, boating, fishing, and property values in and around several lakes and ponds in New Hampshire. In 1965, Moultonborough Bay, Lake Winnipesaukee became the first waterbody in New Hampshire where variable leaf water-milfoil was observed. Variable leaf water-milfoil is native to the Southeastern and Midwestern areas of the United States where more alkaline waters appear to limit the growth of this plant. Outside its native range, however, it adapts well to the relatively acidic, low-alkalinity, and nutrient-poor conditions of oligotrophic lakes and bays similar to Moultonborough Bay. In 2005, the New Hampshire Department of Environmental Services (NHDES) collaborated with the U.S. Geological Survey to investigate the distribution (presence and density) of variable leaf water-milfoil in Moultonborough Bay. This study utilized geophysical systems and conventional water-quality measurements to identify lake-floor environments that may provide suitable habitat for the establishment and growth of variable leaf water-milfoil. The results of the study are intended to assist resource managers in federal and state agencies by providing methods for detecting variable leaf water-milfoil and for identifying areas susceptible to infestation. Ultimately, this information may lead to early detection, prevention, and more effective mitigation strategies. Field activity information for this cruise is available on-line through the U.S. Geological Survey Coastal and Marine Geoscience Data System https://cmgds.marine.usgs.gov/fan_info.php?fa=2005-004-FA.

geoguess-ai/panorama-div9-1024-jpeg dataset hosted on Hugging Face and contributed by the HF Datasets community

Lake Mead is a large interstate reservoir located in the Mojave Desert of southeastern Nevada and northwestern Arizona. It was impounded in 1935 by the construction of Hoover Dam and is one of a series of multi-purpose reservoirs on the Colorado River. The lake extends 183 km from the mouth of the Grand Canyon to Black Canyon, the site of Hoover Dam, and provides water for residential, commercial, industrial, recreational, and other non-agricultural users in communities across the southwestern United States. Extensive research has been conducted on Lake Mead, but a majority of the studies have involved determining levels of anthropogenic contaminants such as synthetic organic compounds, heavy metals and dissolved ions, furans/dioxins, and nutrient loading in lake water, sediment, and biota (Preissler, et al., 1998; Bevans et al, 1996; Bevans et al., 1998; Covay and Leiker, 1998; LaBounty and Horn, 1997; Paulson, 1981). By contrast, little work has focused on the sediments in the lake and the processes of deposition (Gould, 1951). To address these questions, sidescan-sonar imagery and high-resolution seismic-reflection profiles were collected throughout Lake Mead by the USGS in cooperation with researchers from University of Nevada Las Vegas (UNLV). These data allow a detailed mapping of the surficial geology and the distribution and thickness of sediment that has accumulated in the lake since the completion of Hoover Dam. Results indicate that the accumulation of post-impoundment sediment is primarily restricted to former river and stream beds that are now submerged below the lake while the margins of the lake appear to be devoid of post-impoundment sediment. The sediment cover along the original Colorado River bed is continuous and is typically greater than 10 m thick through much of its length. Sediment thickness in some areas exceeds 35 m while the smaller tributary valleys typically are filled with less than 4 m of sediment. Away from the river beds that are now covered with post-impoundment sediment, pre-impoundment alluvial deposits and rock outcrops are still exposed on the lake floor.

Files from the Site Survey Data Bank (ssdb.iodp.org) projection_information.jpeg from proposal 618-Full2 is Reprints/Reports data from site VN-1A - Jpeg image showing projection and datum information used to create maps.

Dataset Card for "dreambooth-hackathon-images-sbob-jpeg"

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Dataset

This dataset was created by Shamail Saeed

Released under Database: Open Database, Contents: © Original Authors

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Dataset

This dataset was created by Umer Abbasi658

Released under CC0: Public Domain

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No description is available. Visit https://dataone.org/datasets/9edefff5ba10dbe1a563ef09faccbc5a for complete metadata about this dataset.

This collection contains the trained models and object detection results of 2 architectures found in the Detectron2 library, on the MS COCO val2017 dataset, under different JPEG compresion level Q = {5, 12, 19, 26, 33, 40, 47, 54, 61, 68, 75, 82, 89, 96} (14 levels per trained model). Architectures: F50 – Faster R-CNN on ResNet-50 with FPN R50 – RetinaNet on ResNet-50 with FPN Training type: D2 – Detectron2 Model ZOO pre-trained 1x model (90.000 iterations, batch 16) STD – standard 1x training (90.000 iterations) on original train2017 dataset Q20 – 1x training (90.000 iterations) on train2017 dataset degraded to Q=20 Q40 – 1x training (90.000 iterations) on train2017 dataset degraded to Q=40 T20 – extra 1x training on top of D2 on train2017 dataset degraded to Q=20 T40 – extra 1x training on top of D2 on train2017 dataset degraded to Q=40 Model and metrics files models_FasterRCNN.tar.gz (F50-STD, F50-Q20, …) models_RetinaNet.tar.gz (R50-STD, R50-Q20, …) For every model there are 3 files: config.yaml – the Detectron2 config of the model. model_final.pth – the weights (training snapshot) in PyTorch format. metrics.json – training metrics (like time, total loss, etc.) every 20 iterations. The D2 models were not included, because they are available from the Detectron2 Model ZOO, as faster_rcnn_R_50_FPN_1x (F50-D2) and retinanet_R_50_FPN_1x (R50-D2). Result files F50-results.tar.gz – results for Faster R-CNN models (inluding D2). R50-results.tar.gz – results for RetinaNet models (inluding D2). For every model there are 14 subdirectories, e.g. evaluator_dump_R50x1_005 through evaluator_dump_R50x1_096, for each of the JPEG Q values. Each such folder contains: coco_instances_results.json – all detected objects (image id, bounding box, class index and confidence). results.json – AP metrics as computed by COCO API. Source code for processing the data The data can be processed using our code, published at: https://github.com/tgandor/urban_oculus. Additional dependencies for the source code: COCO API Detectron2

These data were collected under a cooperative agreement with the Massachusetts Office of Coastal Zone Management (CZM) and the U.S. Geological Survey (USGS), Coastal and Marine Geology Program, Woods Hole Science Center (WHSC). Initiated in 2003, the primary objective of this program is to develop regional geologic framework information for the management of coastal and marine resources. Accurate data and maps of sea-floor geology are important first steps toward protecting fish habitat, delineating marine resources, and assessing environmental changes due to natural or human impacts. The project is focused on the inshore waters (5-30 m deep) of Massachusetts between the New Hampshire border and Cape Cod Bay. Data collected for the mapping cooperative have been released in a series of USGS Open-File Reports (https://woodshole.er.usgs.gov/project-pages/coastal_mass/). This spatial dataset is from the study area located between Duxbury and Hull Massachusetts, and consists of high-resolution geophysics (bathymetry, backscatter intensity, and seismic reflection) and ground validation (sediment samples, video tracklines and bottom photographs). The data were collected during four separate surveys conducted between 2003 and 2007 (NOAA survey H10993 in 2003, USGS-WHSC survey 06012 in 2006, and USGS-WHSC surveys 07001 and 07003 in 2007) and cover more than 200 square kilometers of the inner continental shelf.

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JPEG images of each seismic line were generated in order to incorporate images of the seismic data into Geographic Information System (GIS) projects and data archives utilizing HTML. The JPG format is universal and enables hassle-free transfer of data. These data yield a pictorial view of the seismic data acquired.In 1999, the USGS, in partnership with the South Carolina Sea Grant Consortium, began a study to investigate processes affecting shoreline change along the northern coast of South Carolina, focusing on the Grand Strand region. Previous work along the U.S. Atlantic coast shows that the structure and composition of older geologic strata located seaward of the coast heavily influences the coastal behavior of areas with limited sediment supply, such as the Grand Strand. By defining this geologic framework and identifying the transport pathways and sinks of sediment, geoscientists are developing conceptual models of the present-day physical processes shaping the South Carolina coast. The primary objectives of this research effort are: 1) to provide a regional synthesis of the shallow geologic framework underlying the coastal upland, shoreface and inner continental shelf, and define its role in coastal evolution and modern beach behavior; 2) to identify and model the physical processes affecting coastal ocean circulation and sediment transport, and to define their role in shaping the modern shoreline; and 3) to identify sediment sources and transport pathways; leading to construction of a regional sediment budget.

These seismic data were collected to infer the paleodepositional environment of Pulley Ridge through seismic facies analysis. Without actual rock cores, remote sensing is the next best tool. It was uncertain if Pulley Ridge represented a drowned reef or paleoshoreline. Through seismic imaging, it was determined from the high-amplitude, level-bedded nature of material in the sub-surface that Pulley Ridge represents several stages of barrier-island development.

These data were collected under a cooperative agreement with the Massachusetts Office of Coastal Zone Management (CZM) and the U.S. Geological Survey (USGS), Coastal and Marine Geology Program, Woods Hole Coastal and Marine Science Center (WHCMSC). Initiated in 2003, the primary objective of this program is to develop regional geologic framework information for the management of coastal and marine resources. Accurate data and maps of sea-floor geology are important first steps toward protecting fish habitat, delineating marine resources, and assessing environmental changes due to natural or human impacts. The project is focused on the inshore waters of coastal Massachusetts, primarily in water depths of 3-30 meters deep. Data collected for the mapping cooperative have been released in a series of USGS Open-File Reports (https://woodshole.er.usgs.gov/project-pages/coastal_mass/). The data collected in the study area located in Northern Cape Cod Bay Massachusetts includes high-resolution geophysics (bathymetry, backscatter intensity, and seismic reflection), and ground validation (sediment samples, video tracklines, and bottom photographs). The data were collected during five separate surveys conducted between 2006 and 2008 and cover 480 square kilometers of the inner continental shelf. More information about the individual USGS surveys that are were conducted as part of the northern Cape Cod Bay project can be found on the Woods Hole Coastal and Marine Science Center Field Activity webpage: 06012: https://cmgds.marine.usgs.gov/fan_info.php?fa=2006-012-FA 07001: https://cmgds.marine.usgs.gov/fan_info.php?fa=2007-001-FA 07002: https://cmgds.marine.usgs.gov/fan_info.php?fa=2007-002-FA 07003: https://cmgds.marine.usgs.gov/fan_info.php?fa=2007-003-FA 08002: https://cmgds.marine.usgs.gov/fan_info.php?fa=2008-002-FA

Files from the Site Survey Data Bank (ssdb.iodp.org) Undefined

Variability of bitrate improvements of Photo, No-photo (a) and No-photo (b) images for SS-DWT variants presented in Figs 3 and 5.

Dataset

This dataset was created by Ahmed Fouad

Contents

A complete list of live websites using the jpeg-js technology, compiled through global website indexing conducted by WebTechSurvey.

Execution times of unmodified JPEG 2000 elements and of entropy calculation.