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refactor: Standardize naming conventions (#47)

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* refactor: Standardize naming conventions

* chore: Update the version and re-run experiments

* chore: Improve code quality tooling and documentation

- Add pre-commit job to CI workflow for automated linting on PRs
- Update uniface/__init__.py with copyright header, module docstring,
  and logically grouped exports
- Revise CONTRIBUTING.md to reflect pre-commit handles all formatting
- Remove redundant ruff check from CI (now handled by pre-commit)
- Update build job Python version to 3.11 (matches requires-python)
This commit is contained in:
Yakhyokhuja Valikhujaev
2025-12-30 00:20:34 +09:00
committed by GitHub
parent 64ad0d2f53
commit 50226041c9
72 changed files with 1200 additions and 774 deletions
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20
MODELS.md
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@@ -34,7 +34,7 @@ detector = RetinaFace() # Uses MNET_V2
# Specific model
detector = RetinaFace(
model_name=RetinaFaceWeights.MNET_025, # Fastest
conf_thresh=0.5,
confidence_threshold=0.5,
nms_thresh=0.4,
input_size=(640, 640)
)
@@ -63,14 +63,14 @@ from uniface.constants import SCRFDWeights
# Fast real-time detection
detector = SCRFD(
model_name=SCRFDWeights.SCRFD_500M_KPS,
conf_thresh=0.5,
confidence_threshold=0.5,
input_size=(640, 640)
)
# High accuracy
detector = SCRFD(
model_name=SCRFDWeights.SCRFD_10G_KPS,
conf_thresh=0.5
confidence_threshold=0.5
)
```
@@ -99,29 +99,29 @@ from uniface.constants import YOLOv5FaceWeights
# Lightweight/Mobile
detector = YOLOv5Face(
model_name=YOLOv5FaceWeights.YOLOV5N,
conf_thresh=0.6,
confidence_threshold=0.6,
nms_thresh=0.5
)
# Real-time detection (recommended)
detector = YOLOv5Face(
model_name=YOLOv5FaceWeights.YOLOV5S,
conf_thresh=0.6,
confidence_threshold=0.6,
nms_thresh=0.5
)
# High accuracy
detector = YOLOv5Face(
model_name=YOLOv5FaceWeights.YOLOV5M,
conf_thresh=0.6
confidence_threshold=0.6
)
# Detect faces with landmarks
faces = detector.detect(image)
for face in faces:
bbox = face['bbox'] # [x1, y1, x2, y2]
confidence = face['confidence']
landmarks = face['landmarks'] # 5-point landmarks (5, 2)
bbox = face.bbox # [x1, y1, x2, y2]
confidence = face.confidence
landmarks = face.landmarks # 5-point landmarks (5, 2)
```
---
@@ -466,7 +466,7 @@ spoofer = MiniFASNet(model_name=MiniFASNetWeights.V1SE)
# Detect and check liveness
faces = detector.detect(image)
for face in faces:
label_idx, score = spoofer.predict(image, face['bbox'])
label_idx, score = spoofer.predict(image, face.bbox)
# label_idx: 0 = Fake, 1 = Real
label = 'Real' if label_idx == 1 else 'Fake'
print(f"{label}: {score:.1%}")