diff --git a/docs/quick_start.md b/docs/quick_start.md
index aefbeff652236edac79f0199881a6f203668408e..38a67e12750c636d09bdc0c1263ece049214b17d 100644
--- a/docs/quick_start.md
+++ b/docs/quick_start.md
@@ -1,6 +1,6 @@
 # 10分钟快速上手使用
 
-本文档在一个小数据集上展示了如何通过PaddleX进行训练，您可以阅读PaddleX的**使用教程**来了解更多模型任务的训练使用方式。本示例同步在AIStudio上，可直接[在线体验模型训练](https://aistudio.baidu.com/aistudio/projectdetail/423472)
+本文档在一个小数据集上展示了如何通过PaddleX进行训练，您可以阅读PaddleX的**使用教程**来了解更多模型任务的训练使用方式。本示例同步在AIStudio上，可直接[在线体验模型训练](https://aistudio.baidu.com/aistudio/projectdetail/439860)
 
 ## 1. 准备蔬菜分类数据集
 ```
diff --git a/paddlex/cv/transforms/cls_transforms.py b/paddlex/cv/transforms/cls_transforms.py
index e83a71db0f5e0179d9dcc613140a07b3c97c15f0..525fa2dcb09b317dac542c019a38a5b4a51caa55 100644
--- a/paddlex/cv/transforms/cls_transforms.py
+++ b/paddlex/cv/transforms/cls_transforms.py
@@ -385,15 +385,12 @@ class RandomDistort:
             'saturation': self.saturation_prob,
             'hue': self.hue_prob,
         }
-        im = im.astype('uint8')
-        im = Image.fromarray(im)
         for id in range(len(ops)):
             params = params_dict[ops[id].__name__]
             prob = prob_dict[ops[id].__name__]
             params['im'] = im
             if np.random.uniform(0, 1) < prob:
                 im = ops[id](**params)
-        im = np.asarray(im).astype('float32')
         if label is None:
             return (im, )
         else:
diff --git a/paddlex/cv/transforms/det_transforms.py b/paddlex/cv/transforms/det_transforms.py
index 607c0a6b53013dd89b22cd3e94a1b7551c0b0649..2914fdb42a027860be30f661ed3d39fb3f75fe46 100644
--- a/paddlex/cv/transforms/det_transforms.py
+++ b/paddlex/cv/transforms/det_transforms.py
@@ -541,15 +541,13 @@ class RandomDistort:
             'saturation': self.saturation_prob,
             'hue': self.hue_prob
         }
-        im = im.astype('uint8')
-        im = Image.fromarray(im)
         for id in range(4):
             params = params_dict[ops[id].__name__]
             prob = prob_dict[ops[id].__name__]
             params['im'] = im
+            
             if np.random.uniform(0, 1) < prob:
                 im = ops[id](**params)
-        im = np.asarray(im).astype('float32')
         if label_info is None:
             return (im, im_info)
         else:
@@ -598,7 +596,7 @@ class MixupImage:
             img1.astype('float32') * factor
         img[:img2.shape[0], :img2.shape[1], :] += \
             img2.astype('float32') * (1.0 - factor)
-        return img.astype('uint8')
+        return img.astype('float32')
 
     def __call__(self, im, im_info=None, label_info=None):
         """
diff --git a/paddlex/cv/transforms/ops.py b/paddlex/cv/transforms/ops.py
index 9af31e8b0cf631050623786bf86803dde8bd2b9b..dd517d4ccb7c113cfd00460e5df27125248bb602 100644
--- a/paddlex/cv/transforms/ops.py
+++ b/paddlex/cv/transforms/ops.py
@@ -111,32 +111,41 @@ def bgr2rgb(im):
     return im[:, :, ::-1]
 
 
-def brightness(im, brightness_lower, brightness_upper):
-    brightness_delta = np.random.uniform(brightness_lower, brightness_upper)
-    im = ImageEnhance.Brightness(im).enhance(brightness_delta)
+def hue(im, hue_lower, hue_upper):
+    delta = np.random.uniform(hue_lower, hue_upper)
+    u = np.cos(delta * np.pi)
+    w = np.sin(delta * np.pi)
+    bt = np.array([[1.0, 0.0, 0.0], [0.0, u, -w], [0.0, w, u]])
+    tyiq = np.array([[0.299, 0.587, 0.114], [0.596, -0.274, -0.321],
+                     [0.211, -0.523, 0.311]])
+    ityiq = np.array([[1.0, 0.956, 0.621], [1.0, -0.272, -0.647],
+                      [1.0, -1.107, 1.705]])
+    t = np.dot(np.dot(ityiq, bt), tyiq).T
+    im = np.dot(im, t)
     return im
 
 
-def contrast(im, contrast_lower, contrast_upper):
-    contrast_delta = np.random.uniform(contrast_lower, contrast_upper)
-    im = ImageEnhance.Contrast(im).enhance(contrast_delta)
+def saturation(im, saturation_lower, saturation_upper):
+    delta = np.random.uniform(saturation_lower, saturation_upper)
+    gray = im * np.array([[[0.299, 0.587, 0.114]]], dtype=np.float32)
+    gray = gray.sum(axis=2, keepdims=True)
+    gray *= (1.0 - delta)
+    im *= delta
+    im += gray
     return im
 
 
-def saturation(im, saturation_lower, saturation_upper):
-    saturation_delta = np.random.uniform(saturation_lower, saturation_upper)
-    im = ImageEnhance.Color(im).enhance(saturation_delta)
+def contrast(im, contrast_lower, contrast_upper):
+    delta = np.random.uniform(contrast_lower, contrast_upper)
+    im *= delta
     return im
 
 
-def hue(im, hue_lower, hue_upper):
-    hue_delta = np.random.uniform(hue_lower, hue_upper)
-    im = np.array(im.convert('HSV'))
-    im[:, :, 0] = im[:, :, 0] + hue_delta
-    im = Image.fromarray(im, mode='HSV').convert('RGB')
+def brightness(im, brightness_lower, brightness_upper):
+    delta = np.random.uniform(brightness_lower, brightness_upper)
+    im += delta
     return im
 
-
 def rotate(im, rotate_lower, rotate_upper):
     rotate_delta = np.random.uniform(rotate_lower, rotate_upper)
     im = im.rotate(int(rotate_delta))
diff --git a/paddlex/cv/transforms/seg_transforms.py b/paddlex/cv/transforms/seg_transforms.py
index 0635da99868f64f771a4a9b1dff444d3c847a4de..0b535e75b5cde07c6f81b916d6535eb5ca5c2aca 100644
--- a/paddlex/cv/transforms/seg_transforms.py
+++ b/paddlex/cv/transforms/seg_transforms.py
@@ -889,15 +889,12 @@ class RandomDistort:
             'saturation': self.saturation_prob,
             'hue': self.hue_prob
         }
-        im = im.astype('uint8')
-        im = Image.fromarray(im)
         for id in range(4):
             params = params_dict[ops[id].__name__]
             prob = prob_dict[ops[id].__name__]
             params['im'] = im
             if np.random.uniform(0, 1) < prob:
                 im = ops[id](**params)
-        im = np.asarray(im).astype('float32')
         if label is None:
             return (im, im_info)
         else: