Showing 3 open source projects for "encoder"

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  • 1
    ImageInTerminal.jl

    ImageInTerminal.jl

    Julia package for displaying images in the terminal using ANSI colors

    ImageInTerminal is a drop-in package that once imported changes how a single Colorant and whole Colorant arrays (regular images) are displayed in the interactive REPL. The displayed images will be downscaled to fit into the size of your active terminal session. By default, this package will detect if your running terminal supports 24-bit colors (true colors). If it does, the image will be displayed in 24-bit colors, otherwise, it falls back to 8-bit (256 colors).
    Downloads: 4 This Week
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  • 2
    SafeUtils

    SafeUtils

    110+ developer tools as native MacOS, Linux & Windows desktop apps.

    Tools: https://safeutils.com/barcode-generator https://safeutils.com/color-picker https://safeutils.com/qr-code-generator https://safeutils.com/qr-code-scanner https://safeutils.com/word-counter https://safeutils.com/base-64-decoder https://safeutils.com/diff-checker https://safeutils.com/hex-to-ascii https://safeutils.com/json-formatter https://safeutils.com/lorem-ipsum-generator https://safeutils.com/random-generator https://safeutils.com/time-converter https://safeutils.com/xml-formatter https://safeutils.com/ascii-to-binary https://safeutils.com/ascii-to-hex https://safeutils.com/base-64-encoder https://safeutils.com/binary-to-ascii https://safeutils.com/case-converter https://safeutils.com/csv-to-json https://safeutils.com/decimal-to-ascii https://safeutils.com/html-formatter https://safeutils.com/html-preview https://safeutils.com/html-to-markdown https://safeutils.com/id-generator https://safeutils.com/json-to-csv https://safeutils.com/json-to-xml
    Downloads: 0 This Week
    Last Update:
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  • 3
    TransPose

    TransPose

    PyTorch Implementation for "TransPose, Keypoint localization

    TransPose is a human pose estimation model based on a CNN feature extractor, a Transformer Encoder, and a prediction head. Given an image, the attention layers built in Transformer can efficiently capture long-range spatial relationships between keypoints and explain what dependencies the predicted keypoints locations highly rely on.
    Downloads: 2 This Week
    Last Update:
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