How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
This project provides a compact neural network architecture designed for human face detection and facial landmark localization. It functions as a specialized computer vision tool that identifies faces and extracts five specific facial key points within a single inference pass, making it suitable for integration into resource-constrained environments.
The main features of biubug6/face-detector-1mb-with-landmark are: Edge Face Detection, Anchor Box Detections, Face Detection, Facial Landmark Detection, Multi-Task Learning Models, Edge AI Model Deployment, Detection Model Optimizers, Custom Vision Training.
Projects with overlapping indexed features include: paddlepaddle/paddledetection — PaddleDetection is an object detection framework designed for the end-to-end development, training, and deployment of… linzaer/ultra-light-fast-generic-face-detector-1mb — This project provides a suite of lightweight face detection models designed for high-speed inference on edge computing… xlite-dev/lite.ai.toolkit — lite.ai.toolkit is a C++ computer vision toolkit designed for edge AI deployment. It enables the execution of… kpzhang93/mtcnn_face_detection_alignment — This library provides a deep learning framework for identifying human faces and extracting facial landmarks within… thu-mig/yolov10 — YOLOv10 is a PyTorch computer vision library and real-time vision framework designed for locating and identifying… tingsongyu/pytorch-tutorial-2nd — This project is a comprehensive instructional resource and course for building neural networks using PyTorch. It…
PaddleDetection is an object detection framework designed for the end-to-end development, training, and deployment of computer vision models. It provides a comprehensive library of modular neural network architectures and pipelines that support object detection, instance segmentation, and multi-object tracking tasks. The project distinguishes itself through a configuration-driven approach that decouples model components like backbones and heads, allowing for the flexible assembly of custom vision workflows. It incorporates advanced techniques such as anchor-free detection logic, joint detecti
This project provides a suite of lightweight face detection models designed for high-speed inference on edge computing devices. It centers on a compact neural network architecture that enables human face detection within environments characterized by limited compute resources and power constraints. The system features quantized face detectors available in multiple formats to ensure compatibility across diverse hardware architectures. It includes utilities for model export and quantization, allowing trained weights to be converted into standardized formats for hardware-agnostic deployment. Th
lite.ai.toolkit is a C++ computer vision toolkit designed for edge AI deployment. It enables the execution of pre-trained models for object detection, image classification, and segmentation on resource-constrained devices. The project features a multi-backend inference engine that supports the ONNX model runtime, allowing AI models to run across different hardware targets. It includes a GPU-accelerated pipeline specifically for NVIDIA hardware to reduce latency and increase processing speed. The toolkit covers a broad range of facial analysis capabilities, including emotion detection, gender
This library provides a deep learning framework for identifying human faces and extracting facial landmarks within digital images. It utilizes a multi-task convolutional neural network architecture to simultaneously perform face classification, bounding box regression, and landmark localization. The system processes images through three sequential stages of neural networks, incorporating image pyramid resizing to detect faces of varying scales. To ensure accuracy, it employs bounding box regression to refine coordinate predictions and non-maximum suppression to filter out redundant overlappin