yolov8-face
facenet-pytorch
yolov8-face | facenet-pytorch | |
---|---|---|
2 | 4 | |
386 | 4,167 | |
- | - | |
4.7 | 4.3 | |
about 1 month ago | 7 days ago | |
Python | Python | |
GNU General Public License v3.0 only | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
yolov8-face
-
[D] Face encoding and face clustering
Hello. Q1: I have a large collection of files from which i have to encode faces. Until now, the fastest way i've found is to use a yolov8 finetune model (https://github.com/derronqi/yolov8-face) for face detection and the face_recognition library for encoding in a 128. I've tried using deepface but it's much slower.
-
[D] Fast face recognition over video
Hijacking this comment because i've been working nonstop on my project thanks to your suggestion. I'm now using this https://github.com/derronqi/yolov8-face for face detection and still the old face_recognition for encodings. I'm clustering with dbscan and extracting frames with ffmpeg with -hwaccel on. I'm planning to try this: https://github.com/timesler/facenet-pytorch as it looks like it would be the fastest thing avaiable to process videos? Keep in mind i need to perform encoding other than just detection because i want to use DBscan (and later also facial recognition, but this might be done separately just by saving the encodings). let me know if you have any other suggestions, and thanks again for your help
facenet-pytorch
-
[D] Fast face recognition over video
Hijacking this comment because i've been working nonstop on my project thanks to your suggestion. I'm now using this https://github.com/derronqi/yolov8-face for face detection and still the old face_recognition for encodings. I'm clustering with dbscan and extracting frames with ffmpeg with -hwaccel on. I'm planning to try this: https://github.com/timesler/facenet-pytorch as it looks like it would be the fastest thing avaiable to process videos? Keep in mind i need to perform encoding other than just detection because i want to use DBscan (and later also facial recognition, but this might be done separately just by saving the encodings). let me know if you have any other suggestions, and thanks again for your help
-
Random but unrepeated combinations?
For now, I am trying to evaluate and get the accuracy of the FaceNet module. Like this example on facenet-pytorch, getting the accuracy relies on this file (pairs.txt) provided by the official site. Format description below:
-
Need to watch through 100s of hours of surveylance footage - AI solution?
with some python knowledge you can try a two step procedure: 1) extract a number of frames per second, for example five frames (images, i.e. still frames) per second using opencv or ffmpeg 2) Using facenet: detect faces in frames and then classify them by comparing each image to a known image of the person you are looking for.
-
Query regarding Multiple face recognization system
It's generally better to split the task into a multiple tasks. First I'd want to detect and extract faces. There are a number of pretrained models that you could use for that, e.g. https://github.com/timesler/facenet-pytorch, https://github.com/opencv/opencv/tree/master/data/haarcascades. Once you've extracted faces, you can train a facial recognition using something like a siamese network as you normally would.
What are some alternatives?
retinaface - RetinaFace: Deep Face Detection Library for Python
anime-face-detector - Anime Face Detector using mmdet and mmpose
deepface - A Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python
CompreFace - Leading free and open-source face recognition system
yolov5-face - YOLO5Face: Why Reinventing a Face Detector (https://arxiv.org/abs/2105.12931) ECCV Workshops 2022)
OpenCV - Open Source Computer Vision Library
Adaptive-Face-Recognition
pytorch2keras - PyTorch to Keras model convertor
inference - A fast, easy-to-use, production-ready inference server for computer vision supporting deployment of many popular model architectures and fine-tuned models.
facenet - Face recognition using Tensorflow
anylabeling - Effortless AI-assisted data labeling with AI support from YOLO, Segment Anything, MobileSAM!!
DeepFake-Detection - Towards deepfake detection that actually works