          super().__init__(git_model_id=git_model_id, model_filename=model_filename, **kwargs)
          self.name = "cv2-DNN Detector"
          self.input_size = 300
          self.vram = 0  # CPU Only. Doesn't use VRAM
          self.batchsize = 1
          self.confidence = self.config["confidence"] / 100
  
      def init_model(self):
          """ Initialize CV2 DNN Detector Model"""
 
</s> remove         to_landmarks = self._training_opts["warp_to_landmarks"]
 </s> add  </s> remove         retval = 1 if self._is_parallel else 2
 </s> add         retval = 1 if self._is_parallel else 3 </s> remove         batch["feed"] = np.array(faces, dtype="float32").transpose((0, 3, 1, 2)) / 255.0
 </s> add         batch["feed"] = np.array(faces, dtype="float32")[..., :3].transpose((0, 3, 1, 2)) / 255.0
        self.vram_per_batch = 0
          super().__init__(git_model_id=git_model_id, model_filename=model_filename, **kwargs)
          self.name = "cv2-DNN Detector"
          self.input_size = 300
          self.vram = 0  # CPU Only. Doesn't use VRAM
        self.vram_per_batch = 0
          self.batchsize = 1
          self.confidence = self.config["confidence"] / 100
  
      def init_model(self):
          """ Initialize CV2 DNN Detector Model"""
keep keep keep add keep keep keep keep keep
 Smart Mask Exposure for Extraction & Training (#831)

Smart Masks - Initial Commit 
plugins/extract/detect/cv2_dnn.py