biubug6/Face-Detector-1MB-with-landmark
Face Detector 1MB With Landmark
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 system utilizes a lightweight convolutional backbone and an anchor-based detection mechanism to maintain a small memory footprint while performing real-time processing. By employing a multi-task learning head, the model simultaneously predicts bounding box coordinates and landmark positions, ensuring efficient spatial analysis.
The project supports custom training workflows, allowing users to adapt network parameters and evaluate performance on specific datasets. To facilitate deployment, the models can be exported into a standardized format, enabling cross-platform compatibility and optimized execution on edge hardware through quantization-aware inference strategies.
Features
- Edge Face Detection - Provides a compact model for identifying human faces and extracting key facial landmarks on edge devices.
- Anchor Box Detections - Implements an anchor-based mechanism to classify faces and regress spatial coordinates efficiently.
- Face Detection - Identifies the presence and location of faces using a lightweight neural network optimized for speed.
- Facial Landmark Detection - Extracts five specific facial key points to facilitate precise alignment and analysis.
- Multi-Task Learning Models - Employs a multi-task learning head to simultaneously predict bounding boxes and facial landmarks in one pass.
- Edge AI Model Deployment - Optimizes and deploys models to run efficiently on resource-constrained mobile and embedded hardware.
- Detection Model Optimizers - Allows users to adjust network parameters and hyperparameters to refine detection models for specific use cases.
- Custom Vision Training - Supports training and refining detection models on custom datasets for specific environmental requirements.
- Training and Evaluation Pipelines - Provides workflows for training custom detection models and assessing their accuracy using standard evaluation datasets.
- Lightweight Vision Backbones - Utilizes a compact convolutional backbone to minimize parameters while maintaining real-time processing speeds.
