added buffalo_l model to deepface

deepface/DeepFace.py

@@ -54,7 +54,7 @@ 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 and GhostFaceNet for face recognition
+                ArcFace, SFace GhostFaceNet and Buffalo_L for face recognition
             - Age, Gender, Emotion, Race for facial attributes
             - opencv, mtcnn, ssd, dlib, retinaface, mediapipe, yolov8, yolov11n,
               yolov11s, yolov11m, yunet, fastmtcnn or centerface for face detectors

deepface/models/facial_recognition/Buffalo_L.py

@@ -0,0 +1,96 @@
+import os
+from typing import List, Union
+import numpy as np
+
+from deepface.commons import weight_utils, folder_utils
+from deepface.commons.logger import Logger
+from deepface.models.FacialRecognition import FacialRecognition
+
+logger = Logger()
+
+class Buffalo_L(FacialRecognition):
+    def __init__(self):
+        self.model = None
+        self.input_shape = (112, 112)
+        self.output_shape = 512
+        self.load_model()
+
+    def load_model(self):
+        """Load the InsightFace Buffalo_L recognition model."""
+        try:
+            from insightface.model_zoo import get_model
+        except Exception as err:
+            raise ModuleNotFoundError(
+                "InsightFace and its dependencies are optional for the Buffalo_L model. "
+                "Please install them with: "
+                "pip install insightface>=0.7.3 onnxruntime>=1.9.0 typing-extensions pydantic"
+                "albumentations"
+            ) from err
+
+        sub_dir = "buffalo_l"
+        model_file = "webface_r50.onnx"
+        model_rel_path = os.path.join(sub_dir, model_file)
+        home = folder_utils.get_deepface_home()
+        weights_dir = os.path.join(home, ".deepface", "weights")
+        buffalo_l_dir = os.path.join(weights_dir, sub_dir)
+
+        if not os.path.exists(buffalo_l_dir):
+            os.makedirs(buffalo_l_dir, exist_ok=True)
+            logger.info(f"Created directory: {buffalo_l_dir}")
+
+        weights_path = weight_utils.download_weights_if_necessary(
+        file_name=model_rel_path,
+        source_url="https://drive.google.com/uc?export=download&confirm=pbef&id=1N0GL-8ehw_bz2eZQWz2b0A5XBdXdxZhg" #pylint: disable=line-too-long
+        )
+
+        if not os.path.exists(weights_path):
+            raise FileNotFoundError(f"Model file not found at: {weights_path}")
+        logger.debug(f"Model file found at: {weights_path}")
+
+        self.model = get_model(weights_path)
+        self.model.prepare(ctx_id=-1, input_size=self.input_shape)
+
+    def preprocess(self, img: np.ndarray) -> np.ndarray:
+        """
+        Preprocess the input image or batch of images.
+
+        Args:
+            img: Input image or batch with shape (112, 112, 3) 
+            or (batch_size, 112, 112, 3).
+
+        Returns:
+            Preprocessed image(s) with RGB converted to BGR.
+        """
+        if len(img.shape) == 3:
+            img = np.expand_dims(img, axis=0)  # Convert single image to batch of 1
+        elif len(img.shape) != 4:
+            raise ValueError(f"Input must be (112, 112, 3) or (X, 112, 112, 3). Got {img.shape}")
+        # Convert RGB to BGR for the entire batch
+        img = img[:, :, :, ::-1]
+        return img
+
+    def forward(self, img: np.ndarray) -> Union[List[float], List[List[float]]]:
+        """
+        Extract facial embedding(s) from the input image or batch of images.
+
+        Args:
+            img: Input image or batch with shape (112, 112, 3) 
+            or (batch_size, 112, 112, 3).
+
+        Returns:
+            Embedding as a list of floats (single image) 
+            or list of lists of floats (batch).
+        """
+        # Preprocess the input (single image or batch)
+        img = self.preprocess(img)
+        batch_size = img.shape[0]
+
+        # Handle both single images and batches
+        embeddings = []
+        for i in range(batch_size):
+            embedding = self.model.get_feat(img[i])
+            embeddings.append(embedding.flatten().tolist())
+
+        # Return single embedding if batch_size is 1, otherwise return list of embeddings
+        return embeddings[0] if batch_size == 1 else embeddings
+

deepface/modules/modeling.py

@@ -11,7 +11,8 @@
     SFace,
     Dlib,
     Facenet,
-    GhostFaceNet
+    GhostFaceNet,
+    Buffalo_L
 )
 from deepface.models.face_detection import (
     FastMtCnn,
@@ -59,7 +60,8 @@ def build_model(task: str, model_name: str) -> Any:
             "Dlib": Dlib.DlibClient,
             "ArcFace": ArcFace.ArcFaceClient,
             "SFace": SFace.SFaceClient,
-            "GhostFaceNet": GhostFaceNet.GhostFaceNetClient
+            "GhostFaceNet": GhostFaceNet.GhostFaceNetClient,
+            "Buffalo_L": Buffalo_L.Buffalo_L
         },
         "spoofing": {
             "Fasnet": FasNet.Fasnet,

deepface/modules/verification.py

@@ -423,6 +423,7 @@ def find_threshold(model_name: str, distance_metric: str) -> float:
         "DeepFace": {"cosine": 0.23, "euclidean": 64, "euclidean_l2": 0.64},
         "DeepID": {"cosine": 0.015, "euclidean": 45, "euclidean_l2": 0.17},
         "GhostFaceNet": {"cosine": 0.65, "euclidean": 35.71, "euclidean_l2": 1.10},
+        "Buffalo_L": {"cosine": 0.65, "euclidean": 11.13, "euclidean_l2": 1.1},
     }
 
     threshold = thresholds.get(model_name, base_threshold).get(distance_metric, 0.4)

requirements.txt

@@ -12,4 +12,4 @@ flask_cors>=4.0.1
 mtcnn>=0.1.0
 retina-face>=0.0.14
 fire>=0.4.0
-gunicorn>=20.1.0
+gunicorn>=20.1.0

requirements_additional.txt

@@ -3,4 +3,10 @@ mediapipe>=0.8.7.3
 dlib>=19.20.0
 ultralytics>=8.0.122
 facenet-pytorch>=2.5.3
-torch>=2.1.2
+torch>=2.1.2
+insightface>=0.7.3
+onnxruntime>=1.9.0
+tf-keras
+typing-extensions
+pydantic
+albumentations