W600k-r50.onnx Now

This model is primarily used for , where it converts a face image into a 512-dimensional vector (embedding).

def get_face_embedding(face_image: np.ndarray) -> np.ndarray: """ face_image: BGR image from OpenCV, must be 112x112 pixels already cropped and aligned. Returns: 512-dim embedding vector. """ # Convert BGR to RGB rgb = cv2.cvtColor(face_image, cv2.COLOR_BGR2RGB)

According to the InsightFace model zoo documentation, the w600k_r50 model from the buffalo_l pack achieves impressive metrics on challenging datasets:

This model is primarily used for , where it converts a face image into a 512-dimensional vector (embedding).

def get_face_embedding(face_image: np.ndarray) -> np.ndarray: """ face_image: BGR image from OpenCV, must be 112x112 pixels already cropped and aligned. Returns: 512-dim embedding vector. """ # Convert BGR to RGB rgb = cv2.cvtColor(face_image, cv2.COLOR_BGR2RGB)

According to the InsightFace model zoo documentation, the w600k_r50 model from the buffalo_l pack achieves impressive metrics on challenging datasets: