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Merge pull request #1397 from nriviera/yolo

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[FEATURE]: adding yolov11 into face detection portfolio
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Sefik Ilkin Serengil 2024-12-11 11:19:22 +00:00 committed by GitHub
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12 changed files with 198 additions and 131 deletions
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5
README.md
View File

@ -121,7 +121,7 @@ models = [
"ArcFace",
"Dlib",
"SFace",
"GhostFaceNet",
"GhostFaceNet"
]
#face verification
@ -223,6 +223,9 @@ backends = [
'retinaface',
'mediapipe',
'yolov8',
'yolov11s',
'yolov11n',
'yolov11m',
'yunet',
'centerface',
]

166
deepface/DeepFace.py
View File

@ -54,10 +54,10 @@ def build_model(model_name: str, task: str = "facial_recognition") -> Any:
Args:
model_name (str): model identifier
- VGG-Face, Facenet, Facenet512, OpenFace, DeepFace, DeepID, Dlib,
ArcFace, SFace, GhostFaceNet for face recognition
ArcFace, SFace and GhostFaceNet for face recognition
- Age, Gender, Emotion, Race for facial attributes
- opencv, mtcnn, ssd, dlib, retinaface, mediapipe, yolov8, yunet,
fastmtcnn or centerface for face detectors
- opencv, mtcnn, ssd, dlib, retinaface, mediapipe, yolov8, yolov11n,
yolov11s, yolov11m, yunet, fastmtcnn or centerface for face detectors
- Fasnet for spoofing
task (str): facial_recognition, facial_attribute, face_detector, spoofing
default is facial_recognition
@ -68,18 +68,18 @@ def build_model(model_name: str, task: str = "facial_recognition") -> Any:
def verify(
img1_path: Union[str, np.ndarray, List[float]],
img2_path: Union[str, np.ndarray, List[float]],
model_name: str = "VGG-Face",
detector_backend: str = "opencv",
distance_metric: str = "cosine",
enforce_detection: bool = True,
align: bool = True,
expand_percentage: int = 0,
normalization: str = "base",
silent: bool = False,
threshold: Optional[float] = None,
anti_spoofing: bool = False,
img1_path: Union[str, np.ndarray, List[float]],
img2_path: Union[str, np.ndarray, List[float]],
model_name: str = "VGG-Face",
detector_backend: str = "opencv",
distance_metric: str = "cosine",
enforce_detection: bool = True,
align: bool = True,
expand_percentage: int = 0,
normalization: str = "base",
silent: bool = False,
threshold: Optional[float] = None,
anti_spoofing: bool = False,
) -> Dict[str, Any]:
"""
Verify if an image pair represents the same person or different persons.
@ -96,8 +96,8 @@ def verify(
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
(default is opencv).
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m',
'centerface' or 'skip' (default is opencv).
distance_metric (string): Metric for measuring similarity. Options: 'cosine',
'euclidean', 'euclidean_l2' (default is cosine).
@ -164,14 +164,14 @@ def verify(
def analyze(
img_path: Union[str, np.ndarray],
actions: Union[tuple, list] = ("emotion", "age", "gender", "race"),
enforce_detection: bool = True,
detector_backend: str = "opencv",
align: bool = True,
expand_percentage: int = 0,
silent: bool = False,
anti_spoofing: bool = False,
img_path: Union[str, np.ndarray],
actions: Union[tuple, list] = ("emotion", "age", "gender", "race"),
enforce_detection: bool = True,
detector_backend: str = "opencv",
align: bool = True,
expand_percentage: int = 0,
silent: bool = False,
anti_spoofing: bool = False,
) -> List[Dict[str, Any]]:
"""
Analyze facial attributes such as age, gender, emotion, and race in the provided image.
@ -187,8 +187,8 @@ def analyze(
Set to False to avoid the exception for low-resolution images (default is True).
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
(default is opencv).
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m',
'centerface' or 'skip' (default is opencv).
distance_metric (string): Metric for measuring similarity. Options: 'cosine',
'euclidean', 'euclidean_l2' (default is cosine).
@ -263,20 +263,20 @@ def analyze(
def find(
img_path: Union[str, np.ndarray],
db_path: str,
model_name: str = "VGG-Face",
distance_metric: str = "cosine",
enforce_detection: bool = True,
detector_backend: str = "opencv",
align: bool = True,
expand_percentage: int = 0,
threshold: Optional[float] = None,
normalization: str = "base",
silent: bool = False,
refresh_database: bool = True,
anti_spoofing: bool = False,
batched: bool = False,
img_path: Union[str, np.ndarray],
db_path: str,
model_name: str = "VGG-Face",
distance_metric: str = "cosine",
enforce_detection: bool = True,
detector_backend: str = "opencv",
align: bool = True,
expand_percentage: int = 0,
threshold: Optional[float] = None,
normalization: str = "base",
silent: bool = False,
refresh_database: bool = True,
anti_spoofing: bool = False,
batched: bool = False,
) -> Union[List[pd.DataFrame], List[List[Dict[str, Any]]]]:
"""
Identify individuals in a database
@ -298,8 +298,8 @@ def find(
Set to False to avoid the exception for low-resolution images (default is True).
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
(default is opencv).
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m',
'centerface' or 'skip' (default is opencv).
align (boolean): Perform alignment based on the eye positions (default is True).
@ -369,15 +369,15 @@ def find(
def represent(
img_path: Union[str, np.ndarray],
model_name: str = "VGG-Face",
enforce_detection: bool = True,
detector_backend: str = "opencv",
align: bool = True,
expand_percentage: int = 0,
normalization: str = "base",
anti_spoofing: bool = False,
max_faces: Optional[int] = None,
img_path: Union[str, np.ndarray],
model_name: str = "VGG-Face",
enforce_detection: bool = True,
detector_backend: str = "opencv",
align: bool = True,
expand_percentage: int = 0,
normalization: str = "base",
anti_spoofing: bool = False,
max_faces: Optional[int] = None,
) -> List[Dict[str, Any]]:
"""
Represent facial images as multi-dimensional vector embeddings.
@ -396,8 +396,8 @@ def represent(
(default is True).
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
(default is opencv).
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m',
'centerface' or 'skip' (default is opencv).
align (boolean): Perform alignment based on the eye positions (default is True).
@ -441,15 +441,15 @@ def represent(
def stream(
db_path: str = "",
model_name: str = "VGG-Face",
detector_backend: str = "opencv",
distance_metric: str = "cosine",
enable_face_analysis: bool = True,
source: Any = 0,
time_threshold: int = 5,
frame_threshold: int = 5,
anti_spoofing: bool = False,
db_path: str = "",
model_name: str = "VGG-Face",
detector_backend: str = "opencv",
distance_metric: str = "cosine",
enable_face_analysis: bool = True,
source: Any = 0,
time_threshold: int = 5,
frame_threshold: int = 5,
anti_spoofing: bool = False,
) -> None:
"""
Run real time face recognition and facial attribute analysis
@ -462,8 +462,8 @@ def stream(
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
(default is opencv).
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m',
'centerface' or 'skip' (default is opencv).
distance_metric (string): Metric for measuring similarity. Options: 'cosine',
'euclidean', 'euclidean_l2' (default is cosine).
@ -499,15 +499,15 @@ def stream(
def extract_faces(
img_path: Union[str, np.ndarray],
detector_backend: str = "opencv",
enforce_detection: bool = True,
align: bool = True,
expand_percentage: int = 0,
grayscale: bool = False,
color_face: str = "rgb",
normalize_face: bool = True,
anti_spoofing: bool = False,
img_path: Union[str, np.ndarray],
detector_backend: str = "opencv",
enforce_detection: bool = True,
align: bool = True,
expand_percentage: int = 0,
grayscale: bool = False,
color_face: str = "rgb",
normalize_face: bool = True,
anti_spoofing: bool = False,
) -> List[Dict[str, Any]]:
"""
Extract faces from a given image
@ -517,8 +517,8 @@ def extract_faces(
as a string, numpy array (BGR), or base64 encoded images.
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
(default is opencv).
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m',
'centerface' or 'skip' (default is opencv).
enforce_detection (boolean): If no face is detected in an image, raise an exception.
Set to False to avoid the exception for low-resolution images (default is True).
@ -584,11 +584,11 @@ def cli() -> None:
def detectFace(
img_path: Union[str, np.ndarray],
target_size: tuple = (224, 224),
detector_backend: str = "opencv",
enforce_detection: bool = True,
align: bool = True,
img_path: Union[str, np.ndarray],
target_size: tuple = (224, 224),
detector_backend: str = "opencv",
enforce_detection: bool = True,
align: bool = True,
) -> Union[np.ndarray, None]:
"""
Deprecated face detection function. Use extract_faces for same functionality.
@ -601,8 +601,8 @@ def detectFace(
added to resize the image (default is (224, 224)).
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
(default is opencv).
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m',
'centerface' or 'skip' (default is opencv).
enforce_detection (boolean): If no face is detected in an image, raise an exception.
Set to False to avoid the exception for low-resolution images (default is True).

21
deepface/commons/weight_utils.py
View File

@ -128,8 +128,9 @@ def download_all_models_in_one_shot() -> None:
WEIGHTS_URL as SSD_WEIGHTS,
)
from deepface.models.face_detection.Yolo import (
WEIGHT_URL as YOLOV8_WEIGHTS,
WEIGHT_NAME as YOLOV8_WEIGHT_NAME,
WEIGHT_URLS as YOLO_WEIGHTS,
WEIGHT_NAMES as YOLO_WEIGHT_NAMES,
YoloModel
)
from deepface.models.face_detection.YuNet import WEIGHTS_URL as YUNET_WEIGHTS
from deepface.models.face_detection.Dlib import WEIGHTS_URL as DLIB_FD_WEIGHTS
@ -162,8 +163,20 @@ def download_all_models_in_one_shot() -> None:
SSD_MODEL,
SSD_WEIGHTS,
{
"filename": YOLOV8_WEIGHT_NAME,
"url": YOLOV8_WEIGHTS,
"filename": YOLO_WEIGHT_NAMES[YoloModel.V8N.value],
"url": YOLO_WEIGHTS[YoloModel.V8N.value],
},
{
"filename": YOLO_WEIGHT_NAMES[YoloModel.V11N.value],
"url": YOLO_WEIGHTS[YoloModel.V11N.value],
},
{
"filename": YOLO_WEIGHT_NAMES[YoloModel.V11S.value],
"url": YOLO_WEIGHTS[YoloModel.V11S.value],
},
{
"filename": YOLO_WEIGHT_NAMES[YoloModel.V11M.value],
"url": YOLO_WEIGHTS[YoloModel.V11M.value],
},
YUNET_WEIGHTS,
DLIB_FD_WEIGHTS,

76
deepface/models/face_detection/Yolo.py
View File

@ -1,29 +1,45 @@
# built-in dependencies
import os
from typing import Any, List
from typing import List, Any
from enum import Enum
# 3rd party dependencies
import numpy as np
# project dependencies
from deepface.models.Detector import Detector, FacialAreaRegion
from deepface.commons import weight_utils
from deepface.commons.logger import Logger
from deepface.commons import weight_utils
logger = Logger()
class YoloModel(Enum):
V8N = 0
V11N = 1
V11S = 2
V11M = 3
# Model's weights paths
WEIGHT_NAME = "yolov8n-face.pt"
WEIGHT_NAMES = ["yolov8n-face.pt",
"yolov11n-face.pt",
"yolov11s-face.pt",
"yolov11m-face.pt"]
# Google Drive URL from repo (https://github.com/derronqi/yolov8-face) ~6MB
WEIGHT_URL = "https://drive.google.com/uc?id=1qcr9DbgsX3ryrz2uU8w4Xm3cOrRywXqb"
WEIGHT_URLS = ["https://drive.google.com/uc?id=1qcr9DbgsX3ryrz2uU8w4Xm3cOrRywXqb",
"https://github.com/akanametov/yolo-face/releases/download/v0.0.0/yolov11n-face.pt",
"https://github.com/akanametov/yolo-face/releases/download/v0.0.0/yolov11s-face.pt",
"https://github.com/akanametov/yolo-face/releases/download/v0.0.0/yolov11m-face.pt"]
class YoloClient(Detector):
def __init__(self):
self.model = self.build_model()
class YoloDetectorClient(Detector):
def __init__(self, model: YoloModel):
super().__init__()
self.model = self.build_model(model)
def build_model(self) -> Any:
def build_model(self, model: YoloModel) -> Any:
"""
Build a yolo detector model
Returns:
@ -40,7 +56,7 @@ class YoloClient(Detector):
) from e
weight_file = weight_utils.download_weights_if_necessary(
file_name=WEIGHT_NAME, source_url=WEIGHT_URL
file_name=WEIGHT_NAMES[model.value], source_url=WEIGHT_URLS[model.value]
)
# Return face_detector
@ -69,21 +85,27 @@ class YoloClient(Detector):
# For each face, extract the bounding box, the landmarks and confidence
for result in results:
if result.boxes is None or result.keypoints is None:
if result.boxes is None:
continue
# Extract the bounding box and the confidence
x, y, w, h = result.boxes.xywh.tolist()[0]
confidence = result.boxes.conf.tolist()[0]
# right_eye_conf = result.keypoints.conf[0][0]
# left_eye_conf = result.keypoints.conf[0][1]
right_eye = result.keypoints.xy[0][0].tolist()
left_eye = result.keypoints.xy[0][1].tolist()
right_eye = None
left_eye = None
# eyes are list of float, need to cast them tuple of int
left_eye = tuple(int(i) for i in left_eye)
right_eye = tuple(int(i) for i in right_eye)
# yolo-facev8 is detecting eyes through keypoints,
# while for v11 keypoints are always None
if result.keypoints is not None:
# right_eye_conf = result.keypoints.conf[0][0]
# left_eye_conf = result.keypoints.conf[0][1]
right_eye = result.keypoints.xy[0][0].tolist()
left_eye = result.keypoints.xy[0][1].tolist()
# eyes are list of float, need to cast them tuple of int
left_eye = tuple(int(i) for i in left_eye)
right_eye = tuple(int(i) for i in right_eye)
x, y, w, h = int(x - w / 2), int(y - h / 2), int(w), int(h)
facial_area = FacialAreaRegion(
@ -98,3 +120,23 @@ class YoloClient(Detector):
resp.append(facial_area)
return resp
class YoloDetectorClientV8n(YoloDetectorClient):
def __init__(self):
super().__init__(YoloModel.V8N)
class YoloDetectorClientV11n(YoloDetectorClient):
def __init__(self):
super().__init__(YoloModel.V11N)
class YoloDetectorClientV11s(YoloDetectorClient):
def __init__(self):
super().__init__(YoloModel.V11S)
class YoloDetectorClientV11m(YoloDetectorClient):
def __init__(self):
super().__init__(YoloModel.V11M)

4
deepface/modules/demography.py
View File

@ -35,8 +35,8 @@ def analyze(
Set to False to avoid the exception for low-resolution images (default is True).
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
(default is opencv).
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m',
'centerface' or 'skip' (default is opencv).
distance_metric (string): Metric for measuring similarity. Options: 'cosine',
'euclidean', 'euclidean_l2' (default is cosine).

4
deepface/modules/detection.py
View File

@ -38,8 +38,8 @@ def extract_faces(
as a string, numpy array (BGR), or base64 encoded images.
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
(default is opencv)
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m',
'centerface' or 'skip' (default is opencv)
enforce_detection (boolean): If no face is detected in an image, raise an exception.
Default is True. Set to False to avoid the exception for low-resolution images.

17
deepface/modules/modeling.py
View File

@ -11,7 +11,7 @@ from deepface.models.facial_recognition import (
SFace,
Dlib,
Facenet,
GhostFaceNet,
GhostFaceNet
)
from deepface.models.face_detection import (
FastMtCnn,
@ -21,7 +21,7 @@ from deepface.models.face_detection import (
Dlib as DlibDetector,
RetinaFace,
Ssd,
Yolo,
Yolo as YoloFaceDetector,
YuNet,
CenterFace,
)
@ -36,10 +36,10 @@ def build_model(task: str, model_name: str) -> Any:
task (str): facial_recognition, facial_attribute, face_detector, spoofing
model_name (str): model identifier
- VGG-Face, Facenet, Facenet512, OpenFace, DeepFace, DeepID, Dlib,
ArcFace, SFace, GhostFaceNet for face recognition
ArcFace, SFace and GhostFaceNet for face recognition
- Age, Gender, Emotion, Race for facial attributes
- opencv, mtcnn, ssd, dlib, retinaface, mediapipe, yolov8, yunet,
fastmtcnn or centerface for face detectors
- opencv, mtcnn, ssd, dlib, retinaface, mediapipe, yolov8, 'yolov11n',
'yolov11s', 'yolov11m', yunet, fastmtcnn or centerface for face detectors
- Fasnet for spoofing
Returns:
built model class
@ -59,7 +59,7 @@ def build_model(task: str, model_name: str) -> Any:
"Dlib": Dlib.DlibClient,
"ArcFace": ArcFace.ArcFaceClient,
"SFace": SFace.SFaceClient,
"GhostFaceNet": GhostFaceNet.GhostFaceNetClient,
"GhostFaceNet": GhostFaceNet.GhostFaceNetClient
},
"spoofing": {
"Fasnet": FasNet.Fasnet,
@ -77,7 +77,10 @@ def build_model(task: str, model_name: str) -> Any:
"dlib": DlibDetector.DlibClient,
"retinaface": RetinaFace.RetinaFaceClient,
"mediapipe": MediaPipe.MediaPipeClient,
"yolov8": Yolo.YoloClient,
"yolov8": YoloFaceDetector.YoloDetectorClientV8n,
"yolov11n": YoloFaceDetector.YoloDetectorClientV11n,
"yolov11s": YoloFaceDetector.YoloDetectorClientV11s,
"yolov11m": YoloFaceDetector.YoloDetectorClientV11m,
"yunet": YuNet.YuNetClient,
"fastmtcnn": FastMtCnn.FastMtCnnClient,
"centerface": CenterFace.CenterFaceClient,

6
deepface/modules/recognition.py
View File

@ -54,7 +54,8 @@ def find(
Default is True. Set to False to avoid the exception for low-resolution images.
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'.
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8','yolov11n', 'yolov11s',
'yolov11m', 'centerface' or 'skip'.
align (boolean): Perform alignment based on the eye positions.
@ -483,7 +484,8 @@ def find_batched(
Default is True. Set to False to avoid the exception for low-resolution images.
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'.
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s',
'yolov11m', 'centerface' or 'skip'.
align (boolean): Perform alignment based on the eye positions.

3
deepface/modules/representation.py
View File

@ -36,7 +36,8 @@ def represent(
Default is True. Set to False to avoid the exception for low-resolution images.
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'.
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s',
'yolov11m', 'centerface' or 'skip'.
align (boolean): Perform alignment based on the eye positions.

18
deepface/modules/streaming.py
View File

@ -42,11 +42,11 @@ def analysis(
in the database will be considered in the decision-making process.
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face)
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
(default is opencv).
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m',
'centerface' or 'skip' (default is opencv).
distance_metric (string): Metric for measuring similarity. Options: 'cosine',
'euclidean', 'euclidean_l2' (default is cosine).
@ -192,8 +192,8 @@ def search_identity(
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
(default is opencv).
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m',
'centerface' or 'skip' (default is opencv).
distance_metric (string): Metric for measuring similarity. Options: 'cosine',
'euclidean', 'euclidean_l2' (default is cosine).
Returns:
@ -374,8 +374,8 @@ def grab_facial_areas(
Args:
img (np.ndarray): image itself
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
(default is opencv).
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m',
'centerface' or 'skip' (default is opencv).
threshold (int): threshold for facial area, discard smaller ones
Returns
result (list): list of tuple with x, y, w and h coordinates
@ -443,8 +443,8 @@ def perform_facial_recognition(
db_path (string): Path to the folder containing image files. All detected faces
in the database will be considered in the decision-making process.
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
(default is opencv).
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s',
'yolov11m', 'centerface' or 'skip' (default is opencv).
distance_metric (string): Metric for measuring similarity. Options: 'cosine',
'euclidean', 'euclidean_l2' (default is cosine).
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,

4
deepface/modules/verification.py
View File

@ -47,8 +47,8 @@ def verify(
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'
(default is opencv)
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m',
'centerface' or 'skip' (default is opencv)
distance_metric (string): Metric for measuring similarity. Options: 'cosine',
'euclidean', 'euclidean_l2' (default is cosine).

5
tests/visual-test.py
View File

@ -21,7 +21,7 @@ model_names = [
"Dlib",
"ArcFace",
"SFace",
"GhostFaceNet",
"GhostFaceNet"
]
detector_backends = [
@ -34,6 +34,9 @@ detector_backends = [
"retinaface",
"yunet",
"yolov8",
"yolov11n",
"yolov11s",
"yolov11m",
"centerface",
]