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facenet-pytorch
FROM | facenet-pytorch | |
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2 | 4 | |
37 | 4,232 | |
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10.0 | 3.8 | |
over 1 year ago | 4 days ago | |
Python | Python | |
- | MIT License |
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- ‘Robust’ face detection.
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Need help running this code, my professor will fail me if I don't by next week
I tried to run this https://github.com/haibo-qiu/FROM , on anaconda but I am the following error raise RuntimeError('Attempting to deserialize object on a CUDA '
facenet-pytorch
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[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
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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:
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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.
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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?
anime-face-detector - Anime Face Detector using mmdet and mmpose
CompreFace - Leading free and open-source face recognition system
OpenCV - Open Source Computer Vision Library
pytorch2keras - PyTorch to Keras model convertor
facenet - Face recognition using Tensorflow
DeepFake-Detection - Towards deepfake detection that actually works
OpenSeeFace - Robust realtime face and facial landmark tracking on CPU with Unity integration
deface - Video anonymization by face detection
pytorch_resnet_cifar10 - Proper implementation of ResNet-s for CIFAR10/100 in pytorch that matches description of the original paper.
Real-time-GesRec - Real-time Hand Gesture Recognition with PyTorch on EgoGesture, NvGesture, Jester, Kinetics and UCF101
yolov8-face - yolov8 face detection with landmark
FreeFaceMoCap - Free Face Tracking Module for facial motion capture in Blender