PyTorch module to use OpenFace's nn4.small2.v1.t7 model
Las características principales de thnkim/openfacepytorch son: Visión artificial, Computer Vision Models, Face Manipulation, Face Recognition, Model Implementations, Facial, Action and Pose Recognition.
Las alternativas de código abierto para thnkim/openfacepytorch incluyen: nvidia/flownet2-pytorch — Pytorch implementation of FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks. 1adrianb/face-alignment — This is a PyTorch-based computer vision library for detecting 2D and 3D facial landmark coordinates. It functions as a… deepinsight/insightface — InsightFace is a comprehensive deep learning framework designed for face recognition, biometric identity verification,… amdegroot/ssd.pytorch — This is a PyTorch object detection framework that implements the Single Shot MultiBox Detector for identifying and… davexpro/pytorch-pose-estimation — PyTorch Implementation of Realtime Multi-Person Pose Estimation project. bodokaiser/piwise — Pixel-wise segmentation on the [VOC2012][dataset] dataset using [pytorch][pytorch].
Pytorch implementation of FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
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
Pixel-wise segmentation on the VOC2012dataset dataset using pytorchpytorch.
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