timesler/facenet-pytorch

Implement alternative detection algorithms

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#74 opened on Apr 16, 2020

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Description

As highlighted in #10, the current MTCNN design is appropriate for the project while a single detection method is implemented, but with multiple, it makes more sense to separate the return of bounding boxes and confidences from the cropping of images.

Should also think about returning facial landmarks to implement face alignment following detection.

Retinaface is probably the best option to start with.

Contributor guide

Research direction
Study RetinaFace paper and existing PyTorch implementations, then integrate into the existing MTCNN pipeline, separating detection from cropping.
Tech stack
pythonpytorch
Domain
ai
Issue type
Feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
3-5 days
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Fresh
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Mostly clear
Prerequisites
PythonPyTorch
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
30