Dockerface
A Docker container for face detection using Faster R-CNN deep learning, processing videos and images with bounding box outputs.
What is Dockerface?
Dockerface is a deep learning face detection system that uses a Faster R-CNN network packaged in a Docker container. It processes videos and images to detect faces, outputting annotated media with bounding boxes and structured annotation files containing coordinates and confidence scores. The project solves the problem of deploying complex face detection models by providing a containerized, ready-to-use solution.
Computer vision researchers, developers, and practitioners who need to perform face detection on videos or images without setting up deep learning frameworks from scratch. It's particularly useful for those working with media analysis, annotation pipelines, or prototyping face detection applications.
Developers choose Dockerface because it packages a state-of-the-art face detector in a Docker container, eliminating complex environment setup and dependency management. The containerized approach ensures consistent performance across systems while providing GPU acceleration through CUDA/cuDNN integration for faster processing.
Overview
Face detection using deep learning.
Use Cases
Best For
- Batch processing of video files for face detection
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