Open-source alternatives to Mahyarnajibi SSH

Faceai is a computer vision toolkit designed for facial analysis, identity recognition, and image processing. It provides integrated engines for detecting human faces in static images and live video streams, matching facial encodings against identity databases, and mapping facial landmarks to understand geometric structure and alignment. The project enables real-time augmented reality applications, such as applying virtual makeup and digital accessories by scaling assets to detected facial coordinates. It also includes a suite for digital image restoration capable of removing noise, erasing w

This code is distributed under the BSD 2-Clause license.

This repository contains the code (in PyTorch) for SlimConv: Reducing Channel Redundancy in Convolutional Neural Networks by Features Recombining paper (TIP 2021)

This is an unofficial PyTorch implementation of DeepLab v2 1 with a ResNet-101 backbone. COCO-Stuff dataset 2 and PASCAL VOC dataset 3 are supported. The official Caffe weights provided by the authors can be used without building the Caffe APIs. DeepLab v3/v3+ models with the identical…

This is a PyTorch implementation of EfficientNet convolutional neural networks. It serves as a computer vision model library providing architectures for image classification and high-level feature extraction, including pre-trained weights for immediate image categorization. The library supports transfer learning by allowing the modification of model architectures and output layers to accommodate a custom number of classes for new datasets. It also includes a model exporter to convert trained PyTorch weights into the ONNX format for production inference. The system covers broader computer vis

faceBoxes: a cpu real-time face detector with hight accuracy

A TensorFlow implementation of the following paper by Rajeev Ranjan, Vishal M. Patel and Rama Chellappa published in TPAMI: HyperFace: A Deep Multi-task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition

Face detection using deep learning.

A minimalistic framework for real-time object detection (with a pre-trained face detector)

SeetaFaceEngine is a C++ face recognition engine designed to detect, align, and identify human faces. It functions as a native library that performs facial analysis without relying on external third-party software libraries. The system utilizes a convolutional neural network framework for facial feature extraction and identity matching, representing identities as numerical feature-vector embeddings. It employs a funnel-structured cascade schema for real-time face localization and stacked auto-encoder networks to normalize facial orientation through landmark alignment. The toolkit integrates

By Shifeng Zhang

This repository includes the training and evaluating codes for SFA face detector, implemented in pycaffe. The SFA codes is an extension of the SSH repository. Our method is trained on the training set of the WIDER FACE dataset. If it is useful, please light up a star for this repository.

libfacedetection is a C++ face detection library and computer vision tool. It utilizes a neural network face detector to identify human faces in images and return bounding box coordinates. The library is designed for low latency and high throughput processing, enabling real-time face detection in image and video streams. It supports automated image analysis for identifying coordinates of human faces across large batches of photos and high-performance video processing.

see details at https://github.com/Tencent/FaceDetection-DSFD

This repository provides a collection of reference implementations, toolkits, and orchestration tools for training and deploying large-scale AI models on Cloud TPU hardware. It serves as a framework for managing the lifecycle of accelerator clusters, including hardware orchestration and the provisioning of high-performance compute infrastructure for machine learning workloads. The project specifically enables the pre-training of foundation models, large language models, and complex reasoning architectures through distributed training toolkits and multi-host scaling recipes. It further provide

A new method to fuse high-level features and low-level features in human pose estimation

The C++ implementation of A Fast and Accurate Unconstrained Face Detector.

Created by Jinming Cao , Yangyan Li , Mingchao Sun, Ying Chen , Dani Lischinski , Daniel Cohen-Or , Baoquan Chen , and Changhe Tu . Transactions on Image Processing (TIP) 2022.

This repository is a PyTorch implementation for our CVPR2020 paper (non-commercial use only).

ECCV 2020 PSConv: Squeezing Feature Pyramid into One Compact Poly-Scale Convolutional Layer

Update (Aug 17, 2021): refactored the code of ACB. The readability has been greatly improved. You may call switchtodeploy of an ACB to convert it to the inference-time structure. If you use ACB in your own model, the conversion is as easy as `` for m in yourmodel.modules(): if hasattr(m,…

By Songtao Liu, Di Huang, Yunhong Wang