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StarGAN is a PyTorch image-to-image translation framework designed to synthesize visual styles and attributes across multiple domains. It implements a generative adversarial network that serves as a deep learning image translator for modifying specific visual characteristics within an image dataset. The framework uses a single unified model to handle translations between multiple image domains rather than requiring separate pairs of models. It is a research implementation that learns mappings between different image attributes without the need for paired training data. The project covers the
pix2pixHD is a conditional generative adversarial network designed to transform semantic label maps into high-resolution photorealistic images. It functions as a high-resolution image synthesizer and an image-to-image translation model capable of producing synthetic images at 2048x1024 resolution. The system includes a semantic image editor that allows for the modification of high-resolution visuals by updating the underlying semantic label maps. This enables interactive image editing and the generation of photorealistic images based on source images or discrete label maps. The framework pro
This project is a deep learning framework designed for training and deploying image-to-image translation models. It serves as a research platform for experimenting with neural network architectures that transform visual content between distinct stylistic domains, supporting both paired and unpaired training data. The framework distinguishes itself through its support for cycle-consistency constraints, which allow for image translation between domains without requiring corresponding paired examples. It provides a structured pipeline that utilizes adversarial loss optimization, where generator
This project is a PyTorch object detection framework that implements the Faster R-CNN architecture. It serves as a vision model for predicting precise bounding boxes around multiple objects within images and live video feeds. The system is optimized for multi-GPU training to reduce the time required for model convergence. It utilizes a GPU-accelerated design to handle the training and inference of complex detection networks. The framework covers the full object detection lifecycle, including custom network training and inference for static images and real-time video streams. It includes capa
Image Deblurring using Generative Adversarial Networks
The main features of kupynorest/deblurgan are: Computer Vision Libraries, Computer Vision Models, Deep Learning Deblurring, Image Editing and Manipulation, Image Synthesis, Model Implementations, Image manipulation.
Projects with overlapping indexed features include: yunjey/stargan — StarGAN is a PyTorch image-to-image translation framework designed to synthesize visual styles and attributes across… nvidia/pix2pixhd — pix2pixHD is a conditional generative adversarial network designed to transform semantic label maps into… wkentaro/pytorch-fcn — PyTorch Implementation of Fully Convolutional Networks. (Training code to reproduce the original result is available.). jwyang/faster-rcnn.pytorch — This project is a PyTorch object detection framework that implements the Faster R-CNN architecture. It serves as a… junyanz/pytorch-cyclegan-and-pix2pix — This project is a deep learning framework designed for training and deploying image-to-image translation models. It… 1adrianb/face-alignment — This is a PyTorch-based computer vision library for detecting 2D and 3D facial landmark coordinates. It functions as a…