SeetaPsych Face Hub

Community open-source face modules for SeetaPsych

Usage

This project is already included in the seetapsych-lib default configuration. Download and use it via seetapsych-manager download.

For usage, refer to SeetaPsych.

You can additionally add this algorithm module using the following methods.

WebUI

Run seetapsych-webui with the --dirs argument to use it.

seetapsych-webui --files seetapsych_face_hub/modules/insightface/retinaface.yml

Programmatic Usage

Add the following code in your program to use this algorithm module.

from seetapsych_lib.runtime.factory import Factory
from seetapsych_lib.runtime.pipeline import Pipeline

factory = Factory()
factory.load_file_modules("seetapsych_face_hub/modules/insightface/retinaface.yml")

pipeline = Pipeline(factory, ...)

pipeline.add_attributes("face/detection")

Module Catalog

Module YML Packages
insightface/retinaface.yml FaceDetection-RetinaFace(InsightFace)
insightface/arcface.yml FaceFeature-ArcFace(InsightFace)
mediapipe.yml FaceDetection-MediaPipe, FaceMesh-MediaPipe
retinaface.yml FaceDetection-RetinaFace(PyTorch)

InsightFace (RetinaFace)

InsightFace Buffalo RetinaFace detector: face bounding box + 5-point landmark detection with 500M / 2.5G / 10G FLOPs model variants.

Module config: insightface/retinaface.yml

Package Name Provides Attributes Requires Attributes
FaceDetection-RetinaFace(InsightFace) face/detection, face/landmarks (none)

Description: InsightFace RetinaFace detecting face boxes + 5-point landmarks with configurable input size; 3 accuracy/speed model tiers

Parameters

Name Type Default Description & Tuning
input_size number[] [640, 640] Network input [width, height] in pixels. Larger sizes improve small-face recall but slow inference. Square inputs are recommended.

Models

Name Recommended Notes
det_500m.onnx ✓
det_2.5g.onnx
det_10g.onnx

InsightFace (ArcFace)

InsightFace Buffalo face feature extraction via ArcFace models producing L2-normalized 512-dim embeddings.

Module config: insightface/arcface.yml

Package Name Provides Attributes Requires Attributes
FaceFeature-ArcFace(InsightFace) face/feature face/landmarks

Description: Extract L2-normalized 512-dim face embeddings for recognition, clustering, or similarity search from 5-point aligned faces

Parameters: (none)

Models

Name Recommended Notes
w600k_r50.onnx ✓
w600k_mbf.onnx

MediaPipe

MediaPipe Tasks face detection and 468-point 3D face mesh landmarking via BlazeFace short/full-range models.

Module config: mediapipe.yml

Package Name Provides Attributes Requires Attributes
FaceDetection-MediaPipe face/detection (none)
FaceMesh-MediaPipe face/mesh face/detection

Package: FaceDetection-MediaPipe

Description: Face bounding box detection via MediaPipe BlazeFace detector with short/full-range/sparse model variants

Parameters

Name Type Default Description & Tuning
running_mode selection (IMAGE, VIDEO, LIVE_STREAM) IMAGE Task execution mode. IMAGE for single static frames; VIDEO/LIVE_STREAM for time-series smoothing across frames.
min_detection_confidence number 0.5 Minimum confidence score for a detection box to be returned. Lower values catch more faces at the cost of false positives.
min_suppression_threshold number 0.3 IoU threshold for non-maximum suppression. Higher values keep more overlapping boxes; lower values prune aggressively.

Models

Name Recommended Notes
blaze_face_full_range.tflite ✓
blaze_face_full_range_sparse.tflite
blaze_face_short_range.tflite

Package: FaceMesh-MediaPipe

Description: 468-point normalized 3D face mesh landmark extraction from detected face boxes with optional blendshapes and pose matrices

Parameters

Name Type Default Description & Tuning
running_mode selection (IMAGE, VIDEO, LIVE_STREAM) IMAGE Task execution mode. VIDEO/LIVE_STREAM enable temporal landmark smoothing when num_faces = 1.
num_faces integer 1 Maximum number of faces to landmark simultaneously. Smoothing is only active when num_faces is 1.
min_face_detection_confidence number 0.5 Minimum confidence for the built-in face detector to succeed when initializing landmark placement.
min_face_presence_confidence number 0.5 Minimum confidence that a face is present in the frame before landmark output; filters intermittent dropouts in video.
min_tracking_confidence number 0.5 Minimum tracking confidence to reuse the last known face position vs. re-running full detection. Higher values reduce drift but trigger re-detection more often.
output_face_blendshapes boolean false Whether to output 52 face blendshape coefficients for 3D avatar/AR rendering. Adds minor compute overhead.
output_facial_transformation_matrixes boolean false Whether to output 4x4 facial pose transformation matrices for rigid head pose estimation.

Models

Name Recommended Notes
face_landmarker.task ✓

References

Project links

Data verified by PyPI on Sep 2, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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  • Released:
Newer release available (0.0.3.post1)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for Levalup from gravatar.com Levalup

GitHub Statistics

Data verified by PyPI on Sep 2, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

BSD 3-Clause License

Requires

Python >=3.10

Provides Extra

all dev

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