diff --git a/extras/easypose/detection.py b/extras/easypose/detection.py
index a57c524..0c29a2c 100644
--- a/extras/easypose/detection.py
+++ b/extras/easypose/detection.py
@@ -19,25 +19,26 @@ class RTMDet(BaseModel):
         self.dy = 0
         self.scale = 0
 
+        norm_mean = -1 * np.array([123.675, 116.28, 103.53])
+        norm_std = 1.0 / np.array([58.395, 57.12, 57.375])
+        self.norm_mean = np.reshape(norm_mean, (1, 1, 3)).astype(np.float32)
+        self.norm_std = np.reshape(norm_std, (1, 1, 3)).astype(np.float32)
+
     def preprocess(self, image: np.ndarray):
         th, tw = self.input_shape[2:]
         tensor, self.dx, self.dy, self.scale = letterbox(
             image, (tw, th), fill_value=114
         )
-        tensor -= np.array((123.675, 116.28, 103.53))
-        tensor /= np.array((58.395, 57.12, 57.375))
+        tensor = tensor.astype(np.float32, copy=False)
+        tensor += self.norm_mean
+        tensor *= self.norm_std
         tensor = tensor[..., ::-1]
-        tensor = (
-            np.expand_dims(tensor, axis=0).transpose((0, 3, 1, 2)).astype(np.float32)
-        )
+        tensor = np.expand_dims(tensor, axis=0).transpose((0, 3, 1, 2))
         return tensor
 
     def postprocess(self, tensor: List[np.ndarray]):
-        boxes = tensor[0]
-        classes = tensor[1]
-        boxes = np.squeeze(boxes, axis=0)
-        classes = np.squeeze(classes, axis=0)
-        classes = np.expand_dims(classes, axis=-1)
+        boxes = np.squeeze(tensor[0], axis=0)
+        classes = np.expand_dims(np.squeeze(tensor[1], axis=0), axis=-1)
         boxes = np.concatenate([boxes, classes], axis=-1)
 
         boxes = nms(boxes, self.iou_threshold, self.conf_threshold)
diff --git a/extras/easypose/pipeline.py b/extras/easypose/pipeline.py
index 31bf8db..333e46f 100644
--- a/extras/easypose/pipeline.py
+++ b/extras/easypose/pipeline.py
@@ -199,7 +199,8 @@ class TopDown:
             # See: /mmpose/models/pose_estimators/topdown.py - add_pred_to_datasample()
             th, tw = region.shape[:2]
             bw, bh = [p.box[2] - p.box[0], p.box[3] - p.box[1]]
-            kp[:, :2] = kp[:, :2] / np.array([tw, th]) * np.array([bw, bh])
+            kp[:, :2] /= np.array([tw, th])
+            kp[:, :2] *= np.array([bw, bh])
             kp[:, :2] += np.array([p.box[0] + bw / 2, p.box[1] + bh / 2])
             kp[:, :2] -= 0.5 * np.array([bw, bh])
 
diff --git a/extras/easypose/pose.py b/extras/easypose/pose.py
index be8a29c..677493b 100644
--- a/extras/easypose/pose.py
+++ b/extras/easypose/pose.py
@@ -43,13 +43,17 @@ class SimCC(BaseModel):
         self.dy = 0
         self.scale = 0
 
+        norm_mean = -1 * np.array([123.675, 116.28, 103.53])
+        norm_std = 1.0 / np.array([58.395, 57.12, 57.375])
+        self.norm_mean = np.reshape(norm_mean, (1, 1, 3)).astype(np.float32)
+        self.norm_std = np.reshape(norm_std, (1, 1, 3)).astype(np.float32)
+
     def preprocess(self, image: np.ndarray):
         tensor, self.dx, self.dy, self.scale = image, 0, 0, 1
-        tensor -= np.array((123.675, 116.28, 103.53))
-        tensor /= np.array((58.395, 57.12, 57.375))
-        tensor = (
-            np.expand_dims(tensor, axis=0).transpose((0, 3, 1, 2)).astype(np.float32)
-        )
+        tensor = tensor.astype(np.float32, copy=False)
+        tensor += self.norm_mean
+        tensor *= self.norm_std
+        tensor = np.expand_dims(tensor, axis=0).transpose((0, 3, 1, 2))
         return tensor
 
     def postprocess(self, tensor: List[np.ndarray]):
diff --git a/extras/easypose/utils.py b/extras/easypose/utils.py
index bb42582..b81ca8c 100644
--- a/extras/easypose/utils.py
+++ b/extras/easypose/utils.py
@@ -11,13 +11,10 @@ def letterbox(img: np.ndarray, target_size: Sequence[int], fill_value: int = 128
 
     scale = min(tw / w, th / h)
     nw, nh = int(w * scale), int(h * scale)
-
-    resized_img = cv2.resize(img, (nw, nh))
+    dx, dy = (tw - nw) // 2, (th - nh) // 2
 
     canvas = np.full((th, tw, img.shape[2]), fill_value, dtype=img.dtype)
-
-    dx, dy = (tw - nw) // 2, (th - nh) // 2
-    canvas[dy:dy + nh, dx:dx + nw, :] = resized_img
+    canvas[dy:dy + nh, dx:dx + nw, :] = cv2.resize(img, (nw, nh))
 
     return canvas, dx, dy, scale