Voice & SpeechRetinaFace
Deep learning facial detector with landmarks for Python
- Status
- Generally available
- Last checked
- Aging · Sep 3, 2026
- License
- Open source
- Pricing
- Free tier · from $0
What it is
RetinaFace is a deep learning-based tool that detects faces and their landmarks in images, providing high accuracy and precision. It's ideal for applications requiring robust face detection capabilities.
RetinaFace stands out for its precision in facial landmark localization and robustness across different image conditions, making it a preferred choice for developers needing high accuracy in face detection tasks.
When it fits — and when it doesn’t
A good fit for
Developers building real-time facial detection systems who need high accuracy and speed
Projects that require precise localization of facial landmarks for augmented reality or security purposes
Look elsewhere if
Applications requiring face recognition beyond just detection (RetinaFace does not perform identity verification)
Real-time applications with extremely low latency requirements, as it may have higher processing times compared to simpler models
Known weaknesses
Each one links to the public issue, changelog or docs page it came from, where we have one.
- 01
Performance degradation with very high-resolution images
Processing times increase significantly with higher resolution inputs, impacting real-time applications
- 02
Dependency on specific hardware configurations
Optimal performance requires GPUs; CPU fallback is much slower and less efficient
What it does well
High accuracy in face detection and landmark localization
Supports various image resolutions and formats
Efficient processing for real-time applications
Pricing
- Free tier
- YesOpen source — free to use
- Paid from
- $0
- Model
- Flat rate
- Enterprise plan
- No
Pricing not independently confirmed — check the vendor’s page.
Performance benchmarks
How Fast Is It?
Set up RetinaFace
Setup steps, common gotchas and what to check after 30 days.