diff --git a/easycv/predictors/pose_predictor.py b/easycv/predictors/pose_predictor.py
index 0ff672e9..1c82d6ea 100644
--- a/easycv/predictors/pose_predictor.py
+++ b/easycv/predictors/pose_predictor.py
@@ -19,6 +19,8 @@
 from easycv.utils.misc import deprecated
 from .base import InputProcessor, OutputProcessor, PredictorV2
 
+np.set_printoptions(suppress=True)
+
 
 def _box2cs(image_size, box):
     """This encodes bbox(x,y,w,h) into (center, scale)
@@ -222,11 +224,12 @@ def process_single(self, input):
             bboxes = bboxes[valid_idx]
             person_results = [person_results[i] for i in valid_idx]
 
-        output_person_info = []
+        results = []
         for person_result in person_results:
-            box = person_result['bbox']  # x,y,x,y
-            box = [box[0], box[1], box[2] - box[0], box[3] - box[1]]  # x,y,w,h
-            center, scale = _box2cs(self.cfg.data_cfg['image_size'], box)
+            box = person_result['bbox']  # x,y,x,y,s
+            boxc = [box[0], box[1], box[2] - box[0],
+                    box[3] - box[1]]  # x,y,w,h
+            center, scale = _box2cs(self.cfg.data_cfg['image_size'], boxc)
             data = {
                 'image_id':
                 0,
@@ -264,11 +267,10 @@ def process_single(self, input):
                 output['img_fields'],
             }
             box_id += 1
-            output_person_info.append(data)
+            data_processor = self.processor(data)
+            data_processor['bbox'] = box
+            results.append(data_processor)
 
-        results = []
-        for output in output_person_info:
-            results.append(self.processor(output))
         return results
 
     def __call__(self, inputs):
@@ -296,12 +298,7 @@ class PoseTopDownOutputProcessor(OutputProcessor):
     def __call__(self, inputs):
         output = {}
         output['keypoints'] = inputs['preds']
-        output['bbox'] = inputs['boxes']  # c1, c2, s1, s2, area, core
-
-        for i, bbox in enumerate(output['bbox']):
-            center, scale = bbox[:2], bbox[2:4]
-            output['bbox'][i][:4] = bbox_cs2xyxy(center, scale)
-        output['bbox'] = output['bbox'][:, [0, 1, 2, 3, 5]]
+        output['bbox'] = np.array(inputs['boxes'])  # x1, y1, x2, y2 score
 
         return output
 
@@ -403,6 +400,7 @@ def prepare_model(self):
         return model
 
     def model_forward(self, inputs, return_heatmap=False):
+        boxes = inputs['bbox'].cpu().numpy()
         if self.model_type == 'raw':
             with torch.no_grad():
                 result = self.model(
@@ -423,6 +421,7 @@ def model_forward(self, inputs, return_heatmap=False):
             result = decode_heatmap(output_heatmap, img_metas,
                                     self.cfg.model.test_cfg)
 
+        result['boxes'] = np.array(boxes)
         return result
 
     def get_input_processor(self):