diff --git a/demo/quant/quant_post/README.md b/demo/quant/quant_post/README.md
index 33cbe4a6aaad1..09679745046f4 100755
--- a/demo/quant/quant_post/README.md
+++ b/demo/quant/quant_post/README.md
@@ -43,7 +43,7 @@ python quant_post_static.py --model_path ./inference_model/MobileNet --save_path
 
 运行以上命令后，可在``${save_path}``下看到量化后的模型文件和参数文件。
 
-> 使用的量化算法为``'KL'``, 使用训练集中的160张图片进行量化参数的校正。
+> 使用的量化算法为``'hist'``, 使用训练集中的32张图片进行量化参数的校正。
 
 
 ### 测试精度
@@ -67,6 +67,6 @@ python eval.py --model_path ./quant_model_train/MobileNet --model_name __model__
 
 精度输出为
 ```
-top1_acc/top5_acc= [0.70141864 0.89086477]
+top1_acc/top5_acc= [0.70328485 0.89183184]
 ```
-从以上精度对比可以看出，对``mobilenet``在``imagenet``上的分类模型进行离线量化后 ``top1``精度损失为``0.77%``， ``top5``精度损失为``0.46%``.
+从以上精度对比可以看出，对``mobilenet``在``imagenet``上的分类模型进行离线量化后 ``top1``精度损失为``0.59%``， ``top5``精度损失为``0.36%``.
diff --git a/demo/quant/quant_post/quant_post.py b/demo/quant/quant_post/quant_post.py
index 48a338fc28988..0d8935a5e5159 100755
--- a/demo/quant/quant_post/quant_post.py
+++ b/demo/quant/quant_post/quant_post.py
@@ -19,13 +19,15 @@
 parser = argparse.ArgumentParser(description=__doc__)
 add_arg = functools.partial(add_arguments, argparser=parser)
 # yapf: disable
-add_arg('batch_size',       int,  16,                 "Minibatch size.")
-add_arg('batch_num',       int,  10,               "Batch number")
+add_arg('batch_size',       int,  32,                 "Minibatch size.")
+add_arg('batch_num',       int,  1,               "Batch number")
 add_arg('use_gpu',          bool, True,                "Whether to use GPU or not.")
 add_arg('model_path',       str,  "./inference_model/MobileNet/",  "model dir")
 add_arg('save_path',       str,  "./quant_model/MobileNet/",  "model dir to save quanted model")
 add_arg('model_filename',       str, None,                 "model file name")
 add_arg('params_filename',      str, None,                 "params file name")
+add_arg('algo',         str, 'hist',               "calibration algorithm")
+add_arg('hist_percent',         float, 0.9999,             "The percentile of algo:hist")
 # yapf: enable
 
 
@@ -46,7 +48,9 @@ def quantize(args):
         model_filename=args.model_filename,
         params_filename=args.params_filename,
         batch_size=args.batch_size,
-        batch_nums=args.batch_num)
+        batch_nums=args.batch_num,
+        algo=args.algo,
+        hist_percent=args.hist_percent)
 
 
 def main():
diff --git a/paddleslim/quant/quanter.py b/paddleslim/quant/quanter.py
index 9324276b8a894..2522fed7e1d73 100755
--- a/paddleslim/quant/quanter.py
+++ b/paddleslim/quant/quanter.py
@@ -313,7 +313,9 @@ def quant_post_static(
         batch_size=16,
         batch_nums=None,
         scope=None,
-        algo='KL',
+        algo='hist',
+        hist_percent=0.9999,
+        bias_correction=False,
         quantizable_op_type=["conv2d", "depthwise_conv2d", "mul"],
         is_full_quantize=False,
         weight_bits=8,
@@ -358,9 +360,15 @@ def quant_post_static(
                         generated by sample_generator  as calibrate data.
         scope(paddle.static.Scope, optional): The scope to run program, use it to load 
                         and save variables. If scope is None, will use paddle.static.global_scope().
-        algo(str, optional): If algo=KL, use KL-divergenc method to 
-                        get the more precise scale factor. If algo='direct', use 
-                        abs_max method to get the scale factor. Default: 'KL'.
+        algo(str, optional): If algo='KL', use KL-divergenc method to 
+                        get the scale factor. If algo='hist', use the hist_percent of histogram 
+                        to get the scale factor. If algo='mse', search for the best scale factor which
+                        makes the mse loss minimal. Use one batch of data for mse is enough. If 
+                        algo='avg', use the average of abs_max values  to get the scale factor. If 
+                        algo='abs_max', use abs_max method to get the scale factor. Default: 'hist'.
+        hist_percent(float, optional): The percentile of histogram for algo hist.Default:0.9999.
+        bias_correction(bool, optional): Bias correction method of https://arxiv.org/abs/1810.05723.
+                        Default: False.
         quantizable_op_type(list[str], optional): The list of op types
                         that will be quantized. Default: ["conv2d", "depthwise_conv2d", 
                         "mul"].
@@ -397,6 +405,8 @@ def quant_post_static(
         batch_nums=batch_nums,
         scope=scope,
         algo=algo,
+        hist_percent=hist_percent,
+        bias_correction=bias_correction,
         quantizable_op_type=quantizable_op_type,
         is_full_quantize=is_full_quantize,
         weight_bits=weight_bits,