retinaface
Face Recognition
retinaface | Face Recognition | |
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2 | 34 | |
958 | 51,816 | |
- | - | |
7.7 | 0.0 | |
3 days ago | 2 months ago | |
Python | Python | |
MIT License | MIT License |
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retinaface
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what is the best and most optimized model for face detection/face alignment. best for cuda
I tried this implementation https://github.com/serengil/retinaface
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Using Edge Biometrics For Better AI Security System Development
For face detection, we used the RetinaFace model with a MobileNet backbone from the InsightFace project. This model outputs four coordinates for each detected face on an image as well as 5 facial landmarks. The fact that images captured at different angles or with different optics can change the proportions of the face due to distortion. This may cause the model to struggle identifying the person.
Face Recognition
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Security Image Recognition
Camera connected to a PI? Something like this could run locally: https://github.com/ageitgey/face_recognition
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Facial recognition software/API for face-blind teacher?
Have you tried this repo: github
- GitHub - ageitgey/face_recognition: The world's simplest facial recognition api for Python and the command line
- The simplest facial recognition API for Python
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Every thing you need to know about Machine Learning Pipeline
One of the most common challenges is the black-box problem, when the pipeline becomes too complex to understand it would happen. This can make it difficult to identify issues with the system or to understand why it isn't working as we expected or make accurate predictions that saiwa company find out the solution for Face Recognition. Another challenge is the time required for organizations to deploy a machine learning model, which is increasing and make real-time computing difficult . To overcome these challenges, it's important to have an efficient and rigorous ML pipeline . ML level 0 involves a manual process with its own set of challenges, while ML level 1 involves ML pipeline automation and additional components . A well-defined machine learning pipeline can help to abstract the complex process into a series of steps, allowing each team to work independently on specific tasks such as data collection, data preparation, model training, model evaluation, and model deployment.
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Reverse image search / facial recognition
Second link is an easy to implement python library is you want to build it yourself https://github.com/ageitgey/face_recognition
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Made a easy to use face recognition library
It is similar to https://github.com/ageitgey/face_recognition, except that Ageitgey's cli only compares the first face found in the image to the first one found the the second.
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Salisbury council meeting minutes addressing conspiracy theorist councillors
You'd have alot more luck with something like DLIB or an open source implementation such as: https://github.com/ageitgey/face_recognition
- Face comparison in Stable Diffusion
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Understanding different Algorithms for Facial Recognition
To know more about face_recognition module https://github.com/ageitgey/face_recognition
What are some alternatives?
yolov8-face - yolov8 face detection with landmark
insightface - State-of-the-art 2D and 3D Face Analysis Project
NeMo - A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech)
CompreFace - Leading free and open-source face recognition system
facenet - Face recognition using Tensorflow
Milvus - A cloud-native vector database, storage for next generation AI applications
OpenCV - Open Source Computer Vision Library
ECAPA-TDNN - Unofficial reimplementation of ECAPA-TDNN for speaker recognition (EER=0.86 for Vox1_O when train only in Vox2)
tesseract-ocr - Tesseract Open Source OCR Engine (main repository)
tiny - Tiny Face Detector, CVPR 2017
Kornia - Geometric Computer Vision Library for Spatial AI