Search Results for "nvidia"

...Using A2M is straightforward: Select an audio file, click Convert, and the application generates a MIDI file automatically in your Downloads/A2M folder. All processing is done locally on your device, no uploads, no accounts, and no telemetry. The app runs on CPU by default, with optional NVIDIA GPU acceleration for faster conversions. Project links: Website: justagwas.com/projects/a2m GitHub: github.com/Justagwas/a2m Documentation: https://github.com/Justagwas/a2m/wiki A2M is fully open source and operates only on the files you choose. VirusTotal scan result: https://www.virustotal.com/gui/file/cc2a961baaaac2f8932c2e9ed04f0c27a55309cc03ed0825e44c8af18e263ce6

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. ...

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. ...

...Docker scripts and Google Colab notebooks are available to carry training and prediction. Also, we provide scripts to facilitate training on the cloud with AWS and Nvidia-docker with only a few commands. When training your own model, start with only PSNR loss (50+ epochs, depending on the dataset) and only then introduce GANS and feature loss. This can be controlled by the loss weights argument. The weights used to produce these images are available directly when creating the model object. ISR is compatible with Python 3.6 and is distributed under the Apache 2.0 license.

XBMC Complete installer is a Linux automated installer for XBMC on top of minimal Ubuntu install. It aims to provide quality and complete support for installing & configuring for ION platforms/others. It automates otherwise complex tasks.