facenet-pytorch
pytorch2keras
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facenet-pytorch | pytorch2keras | |
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4 | 2 | |
4,129 | 846 | |
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3.8 | 0.0 | |
14 days ago | over 1 year ago | |
Python | Python | |
MIT License | MIT License |
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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.
pytorch2keras
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Help Needed: Converting PlantNet-300k Pretrained Model Weights from Tar to h5 Format Help
It's almost certainly a pickled pytorch model so you will first need to load it using pytorch and then write it out to h5 (legacy keras format) with https://github.com/gmalivenko/pytorch2keras.
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Is Pytorch good deployment wise?
But of course, you can always train a model with one framework and run inference with another. For instance, pytorch2keras does exactly this.
What are some alternatives?
anime-face-detector - Anime Face Detector using mmdet and mmpose
NudeNet - Neural Nets for Nudity Detection and Censoring
CompreFace - Leading free and open-source face recognition system
InvoiceNet - Deep neural network to extract intelligent information from invoice documents.
OpenCV - Open Source Computer Vision Library
efficientnet-lite-keras - Keras reimplementation of EfficientNet Lite.
facenet - Face recognition using Tensorflow
onnx-tensorflow - Tensorflow Backend for ONNX
DeepFake-Detection - Towards deepfake detection that actually works
cnn-watermark-removal - Fully convolutional deep neural network to remove transparent overlays from images
OpenSeeFace - Robust realtime face and facial landmark tracking on CPU with Unity integration
TensorFlow-object-detection-tutorial - The purpose of this tutorial is to learn how to install and prepare TensorFlow framework to train your own convolutional neural network object detection classifier for multiple objects, starting from scratch