Open-source alternatives to HyperLPR

This project is a generative adversarial network designed for image animation and motion transfer. It functions as a computer vision framework that synthesizes video sequences by applying motion patterns extracted from a driving video onto a static source image. The model distinguishes itself by using a keypoint-based representation to decouple object appearance from temporal movement. By tracking structural deformations through learned latent coordinates, it performs motion retargeting and synthetic media production without requiring manual annotations or object-specific training data. The

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A PyTorch implementation of the YOLO v3 object detection algorithm

Convolutional Recurrent Neural Network

Bullet3 is a professional physics simulation engine designed for calculating rigid body, soft body, and collision dynamics within 3D environments and robotics applications. It functions as a computational framework for determining complex geometric intersections and contact manifolds between objects in simulated space. The library distinguishes itself through a distributed rendering framework that scales heavy graphical workloads and scene generation tasks across large clusters of machines. This capability enables the production of massive datasets by distributing complex scene generation acr

A pytorch implementation of Paper "Improved Training of Wasserstein GANs"

CARLA is an autonomous driving simulator and research environment designed for developing and validating self-driving software. It functions as an urban traffic simulator that generates realistic vehicle and pedestrian behavior and as a synthetic sensor data generator producing LiDAR, Radar, and camera data. The platform distinguishes itself through its deep integration with robotics frameworks, specifically providing native connectivity to ROS2 nodes for robotic control and data processing. It supports the training of driving models via imitation and reinforcement learning within a controlle

This is a TensorFlow implementation of the Deep Convolutional Generative Adversarial Network (DCGAN) architecture, providing a framework for training generative models that produce synthetic images from random noise vectors. The project implements the core DCGAN design, using transposed convolutions for upsampling, batch normalization for training stability, and leaky ReLU activations in the discriminator, all executed as static TensorFlow computation graphs. The implementation supports training on custom image datasets by accepting user-supplied image folders without requiring a predefined f

PyTorch implementation of "Efficient Neural Architecture Search via Parameters Sharing"

PointNet++ is a deep learning framework designed for processing and classifying 3D point cloud data. It utilizes a hierarchical feature learning architecture to extract geometric patterns from sampled 3D point sets. The framework implements a variety of 3D analysis tools, including a point cloud classifier for categorizing objects based on spatial coordinates and surface normals, a semantic scene segmenter for labeling surfaces in large-scale environments, and a tool for 3D object part segmentation. The system covers a broad range of capabilities including geometric feature extraction, 3D da

Mask RCNN in TensorFlow

Official Pytorch implementation of CRAFT text detector | Paper | Pretrained Model | Supplementary

StarGAN v2 - Official PyTorch Implementation (CVPR 2020)

OpenPose is a real-time pose estimation engine designed to detect and track human body, face, hand, and foot landmarks. It functions as a multi-person motion tracker, identifying the spatial coordinates of multiple individuals simultaneously within video streams or static images. Beyond two-dimensional detection, the software acts as a three-dimensional kinematics processor, reconstructing spatial movement data from single or multiple synchronized camera perspectives. The system distinguishes itself through a bottom-up approach that utilizes part-affinity fields to associate body parts across

Openface is a deep learning toolkit designed for facial recognition and identity verification. It provides a comprehensive pipeline for detecting faces, aligning landmarks, and transforming facial images into compact numerical vectors. By utilizing these embeddings, the system enables identity classification and similarity comparison through geometric distance calculations. The project distinguishes itself by integrating research-oriented diagnostic tools alongside its core recognition capabilities. It includes utilities for visualizing high-dimensional feature clusters, inspecting internal c

Open source person re-identification library in python

FaceNet is a facial recognition framework designed to transform facial images into high-dimensional numerical embeddings for identity verification and recognition. It provides a deep learning face embedder that maps facial features into a Euclidean space where distance corresponds to facial similarity. The system includes tools for both supervised and unsupervised identity management. It features a face identity classifier for categorizing images into known identity classes and an unsupervised clustering tool to group similar facial embeddings together without predefined labels. The framewor

Yolact is a computer vision framework and real-time instance segmentation model. It utilizes a fully convolutional neural network to detect objects and generate pixel-level masks for images and video feeds. The system employs prototypical mask generation to create global mask prototypes that are linearly combined for instance-specific results. It incorporates deformable convolutional layers and deformable region-of-interest pooling to adapt spatial sampling to the irregular shapes of objects. The framework covers the full model development lifecycle, including training on custom datasets, ac

Faceswap is a comprehensive framework for automated media manipulation and neural face synthesis. It provides a modular pipeline that manages the entire lifecycle of facial feature extraction, deep learning model training, and image conversion. By coordinating complex computer vision workflows, the system enables users to map facial identities between source and destination datasets while maintaining structural alignment and lighting consistency across video frames. The project distinguishes itself through a highly extensible plugin-based architecture that handles hardware-accelerated process

DeepLab-ResNet rebuilt in TensorFlow

Screenshot-to-code is an AI visual frontend generator that translates images, design mockups, and screenshots into structured HTML, CSS, and Tailwind markup. It functions as a design-to-code converter that uses large language models and neural networks to transform visual elements into functional web page layouts. The tool automates the frontend design process by converting static mockups into website code, facilitating rapid UI prototyping and the generation of static website structures. It bridges the gap between visual design assets and frontend development by translating image-based layou

text detection mainly based on ctpn model in tensorflow, id card detect, connectionist text proposal network

Keras-GAN is a collection of generative adversarial network implementations built with Keras for synthetic data generation and image manipulation. It provides frameworks for image-to-image translation, image inpainting, and neural image super-resolution. The library includes tools for learning disentangled latent space representations to control specific attributes of synthetic outputs. It also features capabilities for image domain translation using paired or unpaired data and the ability to fill corrupted or missing image parts by analyzing surrounding visual context. The project covers ge

PyTorch-GAN is a research-oriented framework providing a collection of modular implementations for generative adversarial network architectures. It serves as a toolkit for training and evaluating models that utilize adversarial minimax optimization to produce synthetic data, offering a structured environment for exploring complex generative tasks within the PyTorch ecosystem. The library distinguishes itself through a comprehensive suite of image synthesis and manipulation capabilities, including super-resolution, inpainting, and cross-domain style translation. It supports advanced training m

开源人脸口罩检测模型和数据 Detect faces and determine whether people are wearing mask.