diff --git a/easycv/predictors/pose_predictor.py b/easycv/predictors/pose_predictor.py
index 0cd18bed..295306d6 100644
--- a/easycv/predictors/pose_predictor.py
+++ b/easycv/predictors/pose_predictor.py
@@ -15,7 +15,8 @@ from easycv.file import io
 from easycv.framework.errors import ModuleNotFoundError, TypeError, ValueError
 from easycv.models import build_model
 from easycv.predictors.builder import PREDICTORS
-from easycv.predictors.detector import TorchYoloXPredictor
+from easycv.predictors.detector import (DetectionPredictor,
+                                        TorchYoloXPredictor, YoloXPredictor)
 from easycv.utils.checkpoint import load_checkpoint
 from easycv.utils.config_tools import mmcv_config_fromfile
 from easycv.utils.registry import build_from_cfg
@@ -390,26 +391,54 @@ class TorchPoseTopDownPredictor(PredictorInterface):
 
         return all_pose_results
 
+    def show_result(self,
+                    image_path,
+                    keypoints,
+                    radius=4,
+                    thickness=1,
+                    kpt_score_thr=0.3,
+                    bbox_color='green',
+                    show=False,
+                    save_path=None):
+        vis_result = vis_pose_result(
+            self.model,
+            image_path,
+            keypoints['pose_results'],
+            dataset_info=self.dataset_info,
+            radius=radius,
+            thickness=thickness,
+            kpt_score_thr=kpt_score_thr,
+            bbox_color=bbox_color,
+            show=show,
+            out_file=save_path)
+
+        return vis_result
+
 
 @PREDICTORS.register_module()
 class TorchPoseTopDownPredictorWithDetector(PredictorInterface):
 
-    SUPPORT_DETECTION_PREDICTORS = {'TorchYoloXPredictor': TorchYoloXPredictor}
+    SUPPORT_DETECTION_PREDICTORS = {
+        'TorchYoloXPredictor': TorchYoloXPredictor,
+        'YoloXPredictor': YoloXPredictor,
+        'DetectionPredictor': DetectionPredictor
+    }
 
     def __init__(
-        self,
-        model_path,
-        model_config={
-            'pose': {
-                'bbox_thr': 0.3,
-                'format': 'xywh'
+            self,
+            model_path,
+            model_config={
+                'pose': {
+                    'bbox_thr': 0.3,
+                    'format': 'xywh'
+                },
+                'detection': {
+                    'model_type': None,
+                    'reserved_classes': [],
+                    'score_thresh': 0.0,
+                }
             },
-            'detection': {
-                'model_type': None,
-                'reserved_classes': [],
-                'score_thresh': 0.0,
-            }
-        }):
+            return_vis_data=False):
         """
         init model
 
@@ -424,7 +453,7 @@ class TorchPoseTopDownPredictorWithDetector(PredictorInterface):
         detection_model_type = model_config['detection'].pop('model_type')
         assert detection_model_type in self.SUPPORT_DETECTION_PREDICTORS
 
-        self.reserved_classes = model_config['detection'].get(
+        self.reserved_classes = model_config['detection'].pop(
             'reserved_classes', [])
 
         model_list = model_path.split(',')
@@ -433,10 +462,15 @@ class TorchPoseTopDownPredictorWithDetector(PredictorInterface):
         pose_model_path, detection_model_path = model_list
 
         detection_obj = self.SUPPORT_DETECTION_PREDICTORS[detection_model_type]
-        self.detection_predictor = detection_obj(
-            detection_model_path, model_config=model_config['detection'])
+        if detection_model_type == 'TorchYoloXPredictor':
+            self.detection_predictor = detection_obj(
+                detection_model_path, model_config=model_config['detection'])
+        else:
+            self.detection_predictor = detection_obj(
+                detection_model_path, **model_config['detection'])
         self.pose_predictor = TorchPoseTopDownPredictor(
             pose_model_path, model_config=model_config['pose'])
+        self.return_vis_data = return_vis_data
 
     def process_det_results(self,
                             outputs,
@@ -454,12 +488,16 @@ class TorchPoseTopDownPredictorWithDetector(PredictorInterface):
         for i in range(len(outputs)):
             output = outputs[i]
             cur_data = {'img': input_data_list[i], 'detection_results': []}
-            for class_name in output['detection_class_names']:
+            if output['detection_boxes'] is None or len(
+                    output['detection_boxes']) < 1:
+                filter_outputs.append(cur_data)
+                continue
+            for j, class_name in enumerate(output['detection_class_names']):
                 if class_name in reserved_classes:
                     cur_data['detection_results'].append({
                         'bbox':
-                        np.append(output['detection_boxes'][i],
-                                  output['detection_scores'][i])
+                        np.append(output['detection_boxes'][j],
+                                  output['detection_scores'][j])
                     })
             filter_outputs.append(cur_data)
 
@@ -481,14 +519,30 @@ class TorchPoseTopDownPredictorWithDetector(PredictorInterface):
 
 
         """
-        detection_output = self.detection_predictor.predict(input_data_list)
+        if hasattr(self.detection_predictor, 'predict') and callable(
+                self.detection_predictor.predict):
+            detection_output = self.detection_predictor.predict(
+                input_data_list)
+        else:
+            detection_output = self.detection_predictor(input_data_list)
         output = self.process_det_results(detection_output, input_data_list,
                                           self.reserved_classes)
         pose_output = self.pose_predictor.predict(
             output, return_heatmap=return_heatmap)
+        if self.return_vis_data:
+            for i, img in enumerate(input_data_list):
+                if len(pose_output[i]['pose_results']) > 0:
+                    vis_result = self.pose_predictor.show_result(
+                        image_path=img,
+                        keypoints=pose_output[i],
+                    )
+                    pose_output[i]['vis_result'] = vis_result
 
         return pose_output
 
+    def __call__(self, *args, **kwargs):
+        return self.predict(*args, **kwargs)
+
 
 def vis_pose_result(model,
                     img,