Showing 2 open source projects for "hex-view"

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  • Eptura Workplace Software Icon
    Eptura Workplace Software

    From desk booking and visitor management, to space planning and office utilization data, Eptura Workplace helps your entire organization work smarter.

    With the world of work changed forever, it’s essential to manage your workplace and assets together to effectively create a high-performing environment. The Eptura experience combines the power of workplace management software with asset management, enabling you to effectively operate your building and facilitate hybrid work.
  • An All-in-One EMR Exclusively for Therapy and Rehab. Icon
    An All-in-One EMR Exclusively for Therapy and Rehab.

    Electronic Medical Records Software

    Managing your therapy and rehab practice is a time-consuming process. You spend hours on paperwork, billing, scheduling, and more. Raintree’s Therapy & Rehab EHR is here to help you manage your practice more efficiently. With our all-in-one solution, you’ll get the tools you need to streamline your therapy and rehab practice, improve patient care, and get back to doing what you love.
  • 1
    pdf-extractor

    pdf-extractor

    Node.js module for rendering pdf pages to images, svgs and HTML files

    Pdf-extractor is a wrapper around pdf.js to generate images, svgs, html files, text files and json files from a pdf on node.js. A DOM Canvas is used to render and export the graphical layer of the pdf. Canvas exports *.png as a default but can be extended to export to other file types like .jpg. Pdf objects are converted to svg using the SVGGraphics parser of pdf.js. Pdf text is converted to HTML. This can be used as a (transparent) layer over the image to enable text selection. Pdf text is...
    Downloads: 3 This Week
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  • 2
    Lightweight' GAN

    Lightweight' GAN

    Implementation of 'lightweight' GAN, proposed in ICLR 2021

    Implementation of 'lightweight' GAN proposed in ICLR 2021, in Pytorch. The main contribution of the paper is a skip-layer excitation in the generator, paired with autoencoding self-supervised learning in the discriminator. Quoting the one-line summary "converge on single gpu with few hours' training, on 1024 resolution sub-hundred images". Augmentation is essential for Lightweight GAN to work effectively in a low data setting. You can test and see how your images will be augmented before...
    Downloads: 0 This Week
    Last Update:
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