Showing 3 open source projects for "deep learning with python"

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  • AestheticsPro Medical Spa Software Icon
    AestheticsPro Medical Spa Software

    Our new software release will dramatically improve your medspa business performance while enhancing the customer experience

    AestheticsPro is the most complete Aesthetics Software on the market today. HIPAA Cloud Compliant with electronic charting, integrated POS, targeted marketing and results driven reporting; AestheticsPro delivers the tools you need to manage your medical spa business. It is our mission To Provide an All-in-One Cutting Edge Software to the Aesthetics Industry.
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  • The Most Powerful Software Platform for EHSQ and ESG Management Icon
    The Most Powerful Software Platform for EHSQ and ESG Management

    Addresses the needs of small businesses and large global organizations with thousands of users in multiple locations.

    Choose from a complete set of software solutions across EHSQ that address all aspects of top performing Environmental, Health and Safety, and Quality management programs.
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  • 1
    AI Upscaler for Blender

    AI Upscaler for Blender

    AI Upscaler for Blender using Real-ESRGAN

    ...Upscaling is done entirely on the CPU. Blender renders a low-resolution image. The Real-ESRGAN Upscaler upscales the low-resolution image to a higher-resolution image. Real-ESRGAN is a deep learning upscaler that uses neural networks to achieve excellent results by adding in detail when it upscales.
    Downloads: 0 This Week
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  • 2
    G2SConverter

    G2SConverter

    Convert models from GoldSource engine to Source engine with AI

    Convert models from GoldSource engine to the Source engine with AI. This utility converts GoldSource engine models to Source engine models. A feature of this utility is the ability to improve the quality of textures of models using Upscaling, deblurring, and normal map generating. All operations to improve the quality of textures are performed by neural networks. To improve the quality of the texture, it is first Upscaled using RealESRGAN. The user can select scaling factor: x2, x4 or x8....
    Downloads: 0 This Week
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  • 3
    Super-résolution via CNN

    Super-résolution via CNN

    Super resolution using a CNN, based on the work of the DGtal team

    Super-resolution using a CNN, based on the work of the DGtal team. First of all, an Nvidia graphics card (neither AMD nor Intel integrated) is highly recommended to parallelize the CNN. You will then need to install CUDA. No CUDA = dozens of times slower. This program will generate "model_epoch_ .pth" files corresponding to the model at epoch n, in a folder saved_model_u t_bs bs_tbs tbs_lr lr, where corresponds to the scale factor, bsthe size of the training batch, tbsthe size of the test...
    Downloads: 0 This Week
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