faceswitch 0.2.2

Simple multi-model face detection library

FaceSwitch

Detect faces in any image with one line of Python — swap the detector without changing your code.

FaceSwitch is a lightweight Python library that wraps multiple face detection engines behind a single, consistent interface. Pick the detector that suits your needs, swap it later without rewriting anything, and only install what you actually use.

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What does it do?

You give it an image. It tells you where the faces are.

from faceswitch.detectors.yolo import YoloDetector
import cv2

image = cv2.imread("photo.jpg")
detector = YoloDetector()
faces = detector.detect(image)

for face in faces:
    print(f"Face at ({face.x1}, {face.y1}) → ({face.x2}, {face.y2})")

Every detector returns the same thing — a list of face boxes — so switching from YOLO to HOG (or any other detector) is just changing one import line.

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Installation

You need Python 3.10 or newer.

Step 1 — install the base library:

pip install faceswitch

Step 2 — install the detector you want to use:

Detector	What it is	Install command
HOG	Classic CPU-based detector (fast, lightweight, no GPU needed)	pip install "faceswitch[hog]"
YOLO	Deep learning detector (more accurate, works best with GPU)	pip install "faceswitch[yolo]"
RetinaFace	High-accuracy ResNet detector (best for difficult angles and small faces)	pip install "faceswitch[retinaface]"

| RetinaFace | Deep learning face detector using ResNet+FPN with multi-scale anchors for high-accuracy detection. | pip install "faceswitch[retinaface]" |

Or install everything at once:

pip install "faceswitch[all]"

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Quick start — 3 lines to detect faces

import cv2
from faceswitch.detectors.hog import HogDetector   # swap this line to switch detectors

image = cv2.imread("photo.jpg")
faces = HogDetector().detect(image)

print(f"Found {len(faces)} face(s)")

To use a different detector, change only the import:

from faceswitch.detectors.yolo import YoloDetector         # YOLO
from faceswitch.detectors.retinaface import RetinaFaceDetector  # RetinaFace

Everything else stays exactly the same.

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Understanding the results

detector.detect(image) always returns a list of FaceBox objects. Each one looks like this:

FaceBox(x1=120, y1=45, x2=210, y2=160, confidence=0.97)

Field	Meaning
x1, y1	Top-left corner of the face box (pixels)
x2, y2	Bottom-right corner of the face box (pixels)
confidence	How sure the detector is (0.0 to 1.0). Some detectors don't provide this (None).

Draw the boxes on your image:

import cv2
from faceswitch.detectors.yolo import YoloDetector

image = cv2.imread("photo.jpg")
faces = YoloDetector().detect(image)

for face in faces:
    cv2.rectangle(image, (face.x1, face.y1), (face.x2, face.y2), (0, 255, 0), 2)

cv2.imwrite("result.jpg", image)
print(f"Saved result.jpg with {len(faces)} face(s) highlighted")

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Choosing the right detector

	HOG	YOLO	RetinaFace
Speed	Fast	Medium	Slower
Accuracy	Basic	High	Very high
GPU needed?	No	Optional	No
Best for	Quick scripts, low-power machines	General use, real-time video	Difficult angles, small faces, production use
Install size	Small (~80MB)	Large (~500MB with torch)	Large (~200MB with TensorFlow)

Not sure? Start with HOG. If it misses faces, switch to YOLO or RetinaFace — your code won't change.

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Available detectors

HOG — pip install "faceswitch[hog]"

Uses dlib's Histogram of Oriented Gradients detector. Works entirely on CPU, very fast, good for frontal faces.

from faceswitch.detectors.hog import HogDetector
faces = HogDetector().detect(image)

YOLO — pip install "faceswitch[yolo]"

Uses Ultralytics YOLOv8. Deep learning-based, excellent accuracy on varied poses and lighting. Downloads a model on first use (~6MB).

from faceswitch.detectors.yolo import YoloDetector
faces = YoloDetector().detect(image)

RetinaFace — pip install "faceswitch[retinaface]"

Uses the serengil/retinaface ResNet+FPN model. Best accuracy on small faces, side profiles, and crowded images. Downloads model weights on first use (~120MB).

from faceswitch.detectors.retinaface import RetinaFaceDetector
faces = RetinaFaceDetector().detect(image)

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Common questions

Do I need a GPU? No. All detectors run on CPU. YOLO and RetinaFace are faster with a GPU but don't require one.

I get "ImportError: ... install faceswitch[hog]" You installed the base library but not the detector dependency. Run the install command from the table above.

The detector downloads a model on first use — is that normal? Yes. YOLO (~6MB) and RetinaFace (~120MB) download their model weights automatically the first time you use them. After that, they're cached locally.

Can I use my own image loading library instead of OpenCV? Yes, as long as the image is a NumPy array with shape (height, width, 3) and uint8 dtype (standard BGR or RGB image). cv2.imread() gives you this automatically.

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More detectors coming

FaceSwitch is actively maintained. New detectors are added regularly — run pip install --upgrade faceswitch to get the latest.

Current version: 0.2.2 | GitHub | PyPI