Search Results for "binary"

PyAV is a Python library that provides direct bindings to FFmpeg, offering fine-grained control over multimedia processing through a Pythonic interface. It enables developers to work with media containers, streams, packets, codecs, and frames, exposing low-level operations while simplifying some of the complexity of FFmpeg. The library is designed for precise media manipulation, making it suitable for applications that require frame-level access or custom processing pipelines. PyAV...

...The library provides both server and client implementations, allowing Python applications to serve and communicate with browser clients or other Socket.IO clients in real time. It supports key Socket.IO features like event handling, message broadcasting, binary data transmission, rooms, and namespaces, giving developers the building blocks to create chat applications, live dashboards, multiplayer games, and collaborative tools.

Segmentation models with pre trained backbones. High-level API (just two lines to create a neural network) 9 models architectures for binary and multi class segmentation (including legendary Unet) 124 available encoders (and 500+ encoders from timm) All encoders have pre-trained weights for faster and better convergence. Popular metrics and losses for training routines. All encoders have pretrained weights. Preparing your data the same way as during weights pre-training may give you better results (higher metric score and faster convergence). ...

You-Get is a small command-line utility for downloading media (video, audio and images) from the Web when there are no other means to do so. It can download video and audio files from such popular web sites as YouTube, Twitter, Niconico, Vimeo, Flickr, Instagram and a whole lot more. You-Get is a great option for when you want to enjoy your favorite videos, audio or images from the internet without having to open any web browsers or get interrupted by ads. It’s also a good choice for...

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...