Docker and Flask based API layer + data ingestion pipeline for the Facenet-PyTorch facial recognition library. I.e. simple ML deployment for matching pairs of photos
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Updated
Oct 17, 2025 - Python
Docker and Flask based API layer + data ingestion pipeline for the Facenet-PyTorch facial recognition library. I.e. simple ML deployment for matching pairs of photos
This model implements an automated pipeline for face detection, embedding generation, and clustering using deep learning and unsupervised machine learning techniques. The model utilizes MTCNN (Multi-task Cascaded Convolutional Neural Networks) for face detection, and InceptionResNetV1
🌟 Detect objects and recognize faces in real-time with our YOLOv8 and MTCNN-powered system, featuring a user-friendly Streamlit UI for seamless interaction.
Real-Time Object Detection and Face Recognition system built using YOLOv8, MTCNN, and FaceNet. The project performs live object detection and face recognition through a webcam. It includes a Streamlit-based interactive UI for uploading images, generating face embeddings, and running real-time recognition with a modular, privacy-aware design.
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