JiageWang / face-recognition-system
It is a face recognition system using mtcnn and arcface in pytorch
☆17Updated 5 years ago
Alternatives and similar repositories for face-recognition-system:
Users that are interested in face-recognition-system are comparing it to the libraries listed below
- Retinaface pytorch face-pose-detect face-key-point-detect☆36Updated 4 years ago
- PyTorch implementation of MobileFaceNets☆19Updated 5 years ago
- 使用opencv的dnn模块做yolov4目标检测☆14Updated 4 years ago
- Ultra Light Weight Face Detection with Landmark☆37Updated 3 years ago
- 基于RetinaFace的目标检测方法，适用于人脸、缺陷、小目标、行人等☆110Updated 4 years ago
- 用opencv的dnn模块实现Yolo-Fastest的目标检测☆50Updated 4 years ago
- yolov5_ncnn in ununtu16.04☆10Updated 4 years ago
- InsightFaceRecognition_Demo_AndroidNCNN☆62Updated 5 years ago
- 【口罩佩戴检测数据训练 | 开源口罩检测数据集和预训练模型】Train D/CIoU_YOLO_V3 by darknet for object detection☆58Updated 4 years ago
- Pose Estimation using OpenPose and TensorRT (only c++)☆18Updated 5 years ago
- MTCNN Pytorch implementation☆33Updated 5 years ago
- 纯YOLO系列的人脸检测+106个关键点检测☆31Updated 4 years ago
- a tensorflow implement faceboxes☆47Updated 2 years ago
- 基于yolo3的口罩佩戴检测项目☆20Updated 5 years ago
- 使用opencv部署DBNet文字检测，包含C++和Python两种版本的实现☆33Updated 3 years ago
- yolov5检测人脸和关键点，只依赖opencv库就可以运行，程序包含C++和Python两个版本的☆63Updated 3 years ago
- Deep Head(face) Pose Estimation☆43Updated 5 years ago
- 基于ncnn的手机端轻量级人脸检测和关键点定位模型☆52Updated 3 years ago
- 基于人造车牌和caffe多标签分类的端到端中 文车牌识别☆36Updated 6 years ago
- ☆38Updated 4 years ago
- End to End Chinese License Plate Recognition☆81Updated 5 years ago
- 基于mobilev2和YOLOv3的目标检测的android端的ncnn的实现☆9Updated 5 years ago
- Face Recognization System By MTCNN & Insightface☆48Updated 5 years ago
- 人员佩戴口罩 检测数据集☆82Updated 4 years ago
- ☆12Updated 4 years ago
- yolov5模型训练后量化代码☆19Updated 4 years ago
- Intelligent monitoring of escalator.Function including traffic statistics,passenger retention detection and large object retention detect…☆24Updated 10 months ago
- arcface and retinaface model convert mxnet to onnx.☆60Updated 4 years ago
- 数钢筋demo，IOU 0.7 下，AP 90.6。训练只要不到十分钟，可以非常愉快的 玩耍☆34Updated 5 years ago
- ☆17Updated 3 years ago