How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
This project is a modular PyTorch framework for training and evaluating object detection and instance segmentation models. It serves as a computer vision research tool and a deep learning inference engine designed to identify object locations, classes, and pixel-level masks within images. The framework implements a two-stage inference pipeline that utilizes region proposal networks and a symmetric mask-head architecture. It provides specialized capabilities for instance segmentation, object bounding box detection, and human pose estimation via anatomical keypoint detection. The system includ
Pixel-wise segmentation on the VOC2012dataset dataset using pytorchpytorch.
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
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 repository contains some models for semantic segmentation and the pipeline of training and testing models, implemented in PyTorch
The main features of zijundeng/pytorch-semantic-segmentation are: Computer Vision, Computer Vision Models, Segmentation Architectures.
Projects with overlapping indexed features include: facebookresearch/maskrcnn-benchmark — This project is a modular PyTorch framework for training and evaluating object detection and instance segmentation… dbolya/yolact — Yolact is a computer vision framework and real-time instance segmentation model. It utilizes a fully convolutional… 1adrianb/face-alignment — This is a PyTorch-based computer vision library for detecting 2D and 3D facial landmark coordinates. It functions as a… amdegroot/ssd.pytorch — This is a PyTorch object detection framework that implements the Single Shot MultiBox Detector for identifying and… cmu-perceptual-computing-lab/openpose — OpenPose is a real-time pose estimation engine designed to detect and track human body, face, hand, and foot… bodokaiser/piwise — Pixel-wise segmentation on the [VOC2012][dataset] dataset using [pytorch][pytorch].