Showing 4 open source projects for "image tools"

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  • 1

    openSkyMatch

    Matches OpenScience Observatories images with astronomical catalogs

    openSkyMatch is a collection of Linux shell and Python scripts designed for the OpenScience Observatories program. It automates the identification and matching of detected celestial objects in locally captured FITS images with entries in large-scale sky catalogs, notably Pan-STARRS1 DR2 (II/389/ps1_dr2). The toolkit supports data preprocessing, coordinate correlation, and catalog-based validation of astronomical detections. All tools are open-source and optimized for reproducibility and...
    Downloads: 0 This Week
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  • 2
    PyTorch GAN Zoo

    PyTorch GAN Zoo

    A mix of GAN implementations including progressive growing

    PyTorch GAN Zoo is a comprehensive open research toolbox designed for experimenting with and developing Generative Adversarial Networks (GANs) using PyTorch. The project provides modular implementations of popular GAN architectures, including Progressive Growing of GANs (PGAN), DCGAN, and an experimental StyleGAN version. It is built to support both researchers and developers who want to train, evaluate, and extend GANs efficiently across diverse datasets such as CelebA-HQ, FashionGen, DTD,...
    Downloads: 4 This Week
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  • 3
    NiftyNet

    NiftyNet

    An open-source convolutional neural networks platform for research

    An open-source convolutional neural networks platform for medical image analysis and image-guided therapy. NiftyNet is a TensorFlow-based open-source convolutional neural networks (CNNs) platform for research in medical image analysis and image-guided therapy. NiftyNet’s modular structure is designed for sharing networks and pre-trained models. Using this modular structure you can get started with established pre-trained networks using built-in tools. ...
    Downloads: 0 This Week
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  • 4
    Learning to Learn in TensorFlow

    Learning to Learn in TensorFlow

    Learning to Learn in TensorFlow

    ...The design supports both single-variable and high-dimensional problems, and includes tools for evaluating how well a learned optimizer performs on unseen tasks.
    Downloads: 7 This Week
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