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PyTorch implementation of neural style transfer algorithm
The main features of progamergov/neural-style-pt are: Computer Vision, Computer Vision Models, Style Transfer.
Projects with overlapping indexed features include: zhanghang1989/pytorch-style-transfer — Neural Style and MSG-Net. cmu-perceptual-computing-lab/openpose — OpenPose is a real-time pose estimation engine designed to detect and track human body, face, hand, and foot… 1adrianb/face-alignment — This is a PyTorch-based computer vision library for detecting 2D and 3D facial landmark coordinates. It functions as a… alexis-jacq/pytorch-tutorials. bodokaiser/piwise — Pixel-wise segmentation on the [VOC2012][dataset] dataset using [pytorch][pytorch]. amdegroot/ssd.pytorch — This is a PyTorch object detection framework that implements the Single Shot MultiBox Detector for identifying and…
Neural Style and MSG-Net
This is a PyTorch object detection framework that implements the Single Shot MultiBox Detector for identifying and localizing multiple objects within images and video. The project provides a neural network architecture designed for single-shot object detection, which predicts bounding boxes and class labels in one pass. The implementation includes a real-time object detector capable of processing live video streams to track and label objects across sequential frames. It also features a complete computer vision training pipeline for preparing image datasets and training model weights. The fra
This is a PyTorch-based computer vision library for detecting 2D and 3D facial landmark coordinates. It functions as a facial landmark detector and reconstruction tool, utilizing deep learning to identify precise geometric points on human faces from image datasets. The library allows for the selection of specific detection backends to balance accuracy and processing speed. It supports the integration of precomputed bounding box files, which enables the system to bypass the initial detection phase and proceed directly to landmark extraction. The toolkit includes capabilities for batch image p