Showing 8 open source projects for "deep learning with python"

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  • 1
    satellite-image-deep-learning

    satellite-image-deep-learning

    Resources for deep learning with satellite & aerial imagery

    This page lists resources for performing deep learning on satellite imagery. To a lesser extent classical Machine learning (e.g. random forests) are also discussed, as are classical image processing techniques. Note there is a huge volume of academic literature published on these topics, and this repository does not seek to index them all but rather list approachable resources with published code that will benefit both the research and developer communities.
    Downloads: 2 This Week
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  • 2
    MMDeploy

    MMDeploy

    OpenMMLab Model Deployment Framework

    MMDeploy is an open-source deep learning model deployment toolset. It is a part of the OpenMMLab project. Models can be exported and run in several backends, and more will be compatible. All kinds of modules in the SDK can be extended, such as Transform for image processing, Net for Neural Network inference, Module for postprocessing and so on. Install and build your target backend.
    Downloads: 1 This Week
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  • 3
    SMILI

    SMILI

    Scientific Visualisation Made Easy

    The Simple Medical Imaging Library Interface (SMILI), pronounced 'smilie', is an open-source, light-weight and easy-to-use medical imaging viewer and library for all major operating systems. The main sMILX application features for viewing n-D images, vector images, DICOMs, anonymizing, shape analysis and models/surfaces with easy drag and drop functions. It also features a number of standard processing algorithms for smoothing, thresholding, masking etc. images and models, both with...
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    Downloads: 6 This Week
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  • 4
    Surface Defect Detection Dataset Papers

    Surface Defect Detection Dataset Papers

    Constantly summarizing open source dataset and critical papers

    ...A reasonable imaging scheme helps to obtain images with uniform illumination and clearly reflect the surface defects of the object. In recent years, many defect detection methods based on deep learning have also been widely used in various industrial scenarios.
    Downloads: 0 This Week
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  • 5
    GIMP ML

    GIMP ML

    AI for GNU Image Manipulation Program

    This repository introduces GIMP3-ML, a set of Python plugins for the widely popular GNU Image Manipulation Program (GIMP). It enables the use of recent advances in computer vision to the conventional image editing pipeline. Applications from deep learning such as monocular depth estimation, semantic segmentation, mask generative adversarial networks, image super-resolution, de-noising and coloring have been incorporated with GIMP through Python-based plugins. ...
    Downloads: 11 This Week
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  • 6

    Spectral Python

    A python module for hyperspectral image processing

    Spectral Python (SPy) is a python package for reading, viewing, manipulating, and classifying hyperspectral image (HSI) data. SPy includes functions for clustering, dimensionality reduction, supervised classification, and more.
    Downloads: 0 This Week
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  • 7
    Exposure

    Exposure

    Learning infinite-resolution image processing with GAN and RL

    Learning infinite-resolution image processing with GAN and RL from unpaired image datasets, using a differentiable photo editing model. ACM Transactions on Graphics (presented at SIGGRAPH 2018) Exposure is originally designed for RAW photos, which assumes 12+ bit color depth and linear "RGB" color space (or whatever we get after demosaicing). jpg and png images typically have only 8-bit color depth (except 16-bit pngs) and the lack of information (dynamic range/activation resolution) may...
    Downloads: 0 This Week
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  • 8

    Training Image Operators from Samples

    Tools to train Image Operators automatically from a set of samples.

    TRIOS - Training Image Operators from Samples is a set of tools to bring Image Processing closer to scientists in general. It is capable of estimating an operator between two images using only pairs of samples that contain an input image and the desired output. The operator is saved to a file and can be applied to any image.
    Downloads: 0 This Week
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