Showing 3 open source projects for "nvidia"

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    enhancr

    enhancr

    Video Frame Interpolation & Super Resolution using NVIDIA's TensorRT

    ...The GUI was designed to provide a stunning experience powered by state-of-the-art technologies without feeling clunky and outdated like other alternatives. It features blazing-fast TensorRT inference by NVIDIA, which can speed up AI processes significantly. Pre-packaged, without the need to install Docker or WSL (Windows Subsystem for Linux) - and NCNN inference by Tencent which is lightweight and runs on NVIDIA, AMD and even Apple Silicon - in contrast to the mammoth of an inference PyTorch is, which only runs on NVIDIA GPUs.
    Downloads: 33 This Week
    Last Update:
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  • 2
    Robust Video Matting (RVM)

    Robust Video Matting (RVM)

    Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX

    We introduce a robust, real-time, high-resolution human video matting method that achieves new state-of-the-art performance. Our method is much lighter than previous approaches and can process 4K at 76 FPS and HD at 104 FPS on an Nvidia GTX 1080Ti GPU. Unlike most existing methods that perform video matting frame-by-frame as independent images, our method uses a recurrent architecture to exploit temporal information in videos and achieves significant improvements in temporal coherence and matting quality. Furthermore, we propose a novel training strategy that enforces our network on both matting and segmentation objectives. ...
    Downloads: 6 This Week
    Last Update:
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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 batch and lrto the learning rate. ...
    Downloads: 0 This Week
    Last Update:
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