diff --git a/happypose/pose_estimators/cosypose/cosypose/utils/cosypose_wrapper.py b/happypose/pose_estimators/cosypose/cosypose/utils/cosypose_wrapper.py
index 2e6f447e..13688c1b 100644
--- a/happypose/pose_estimators/cosypose/cosypose/utils/cosypose_wrapper.py
+++ b/happypose/pose_estimators/cosypose/cosypose/utils/cosypose_wrapper.py
@@ -11,7 +11,7 @@
 - Deprecate this class when possible
 """
 
-from typing import Union
+from typing import Tuple, Union
 
 import torch
 
@@ -24,6 +24,7 @@
 from happypose.pose_estimators.cosypose.cosypose.training.pose_models_cfg import (
     load_model_cosypose,
 )
+from happypose.pose_estimators.megapose.inference.detector import Detector
 from happypose.toolbox.datasets.datasets_cfg import make_object_dataset
 from happypose.toolbox.datasets.object_dataset import RigidObjectDataset
 from happypose.toolbox.inference.utils import load_detector
@@ -77,34 +78,37 @@ class CosyPoseWrapper:
     def __init__(
         self,
         dataset_name: str,
+        model_type: str = "pbr",
         object_dataset: Union[None, RigidObjectDataset] = None,
-        n_workers=8,
-        gpu_renderer=False,
         renderer_type: str = "panda3d",
+        n_workers: int = 8,
     ) -> None:
         """
-        inputs:
-        - dataset_name: hope|tless|ycbv
-        - object_dataset: None or already existing rigid object dataset. If None, will use dataset_name
-        to build one
-        - n_workers: how many processes will be spun in the batch renderer
-        - renderer_type: 'panda3d'|'bullet'
+        :param dataset_name: str, name of the dataset on which model was trained, hope|tless|ycbv
+        :param model_type: str, type of NN model (depends on training data), hope|tless|ycbv
+        :param object_dataset: None or already existing rigid object dataset. If None, will use dataset_name to build one
+        :param n_workers: how many processes will be spun in the batch renderer
+        :param renderer_type: 'panda3d'|'bullet'
         """
 
         self.dataset_name = dataset_name
         self.object_dataset = object_dataset
         self.detector, self.pose_predictor = self.get_model(
-            dataset_name, n_workers, renderer_type
+            dataset_name, model_type, n_workers, renderer_type
         )
 
-    def get_model(self, dataset_name, n_workers, renderer_type, model_type="pbr"):
+    def get_model(
+        self, dataset_name, model_type, n_workers, renderer_type
+    ) -> Tuple[Detector, PoseEstimator]:
         """
+        Return CosyPose detector and pose estimator objects for a given dataset.
 
-        :param dataset_name: str, name of the dataset on which model was trained
-        :param n_workers: int, number of workers used in the renderer
+        :param dataset_name: str, name of the dataset on which model was trained, hope|tless|ycbv
+        :param model_type: str, type of NN model (depends on training data), hope|tless|ycbv
         :param renderer_type: str, which renderer to use, "panda3d" and "bullet" supported
+        :param n_workers: int, number of workers used in the renderer
         :param model_type: str, what training data was used, "pbr" and "synth+real" supported
-        :return: tuple (detector)
+        :return: tuple (Detector,PoseEstimator)
         """
         try:
             mids = AVAILABLE_MODELS[dataset_name][model_type]