Face-Body Detection in Python

Python, Software Architecture, CUDA, Machine Learning (ML), Image Processing, OpenCV, Computer Vision, Deep Learning, Object Detection, YOLO · I’m building a computer-vision pipeline focused on reliable detection and recognition of human faces and full-body figures. The core of the job is a clean, well-documented Python implementation that can take still images or short video clips and return bounding boxes, class labels, and confidence scores for each detected person.

I already have test media and the computing environment; what’s missing is the detection logic itself—ideally leveraging familiar libraries such as OpenCV, TensorFlow, PyTorch, or a proven YOLO/SSD variant. Accuracy on varied lighting and crowded scenes is more important to me than sheer speed, but the code should still run in real time on a modern GPU.

Deliverables • Python source code with clear inline comments • Pre-trained weights (or training notebook) and instructions for further fine-tuning • Short report outlining model choice, evaluation metrics, and sample results • Simple CLI or notebook demo that shows the system working on my test set

Acceptance criteria • ≥90 % precision and recall on the provided images • No external dependencies beyond standard Python CV/ML stacks • Reproducible setup: a requirements.txt or environment.yml t