diff --git a/README.md b/README.md
index 7eed01867e31fc710f55cfe47f2f9917475d77dd..b836fd554191b1cf5e870d4e5839881776cc93e9 100644
--- a/README.md
+++ b/README.md
@@ -166,8 +166,8 @@ A: 请将PaddlePaddle升级至1.5.2版本或以上。
 * 2020.02.25
 
   **`v0.4.0`**
-  * 新增Fast-SCNN分割网络，提供基于cityscapes的[预训练模型](./docs/model_zoo.md)1个
-  * 新增LaneNet车道线检测网络，提供[预训练模型](https://github.com/PaddlePaddle/PaddleSeg/tree/release/v0.4.0/contrib/LaneNet#%E4%B8%83-%E5%8F%AF%E8%A7%86%E5%8C%96)一个
+  * 新增Fast-SCNN分割网络，提供基于cityscapes的[预训练模型](./docs/model_zoo.md)1个。Fast-SCNN不需要imagenet的预训练模型，精度与deeplab-mobilenet，ICNet持平，性能优于两者。
+  * 新增LaneNet车道线检测网络，提供[预训练模型](https://github.com/PaddlePaddle/PaddleSeg/tree/release/v0.4.0/contrib/LaneNet#%E4%B8%83-%E5%8F%AF%E8%A7%86%E5%8C%96)一个。
   * 新增基于PaddleSlim的分割库压缩策略([量化](./slim/quantization/README.md), [蒸馏](./slim/distillation/README.md), [剪枝](./slim/prune/README.md), [搜索](./slim/nas/README.md))
   
   
diff --git a/contrib/LaneNet/README.md b/contrib/LaneNet/README.md
index b86777305c160edae7a55349d719c9df2a2da4f9..1448951e900dbb8bb235be476698eb13d62f5e4c 100644
--- a/contrib/LaneNet/README.md
+++ b/contrib/LaneNet/README.md
@@ -108,7 +108,7 @@ SOLVER:
 使用下述命令启动训练
 
 ```shell
-CUDA_VISIBLE_DEVICES=0 python -u train.py --cfg configs/lanenet.yaml --use_gpu --use_mpio --do_eval
+CUDA_VISIBLE_DEVICES=0 python -u train.py --cfg configs/lanenet.yaml --use_gpu --do_eval
 ```
 
 ## 六. 进行评估
diff --git a/docs/models.md b/docs/models.md
index a452aa3639c3901d8f75d1aa4f5f1b7f393ce0b7..9519d4441b733e649db76794f9f98b29892871d4 100644
--- a/docs/models.md
+++ b/docs/models.md
@@ -5,6 +5,7 @@
 - [PSPNet](#PSPNet)
 - [ICNet](#ICNet)
 - [HRNet](#HRNet)
+- [Fast-SCNN](#Fast-SCNN)
 
 ## U-Net
 U-Net [1] 起源于医疗图像分割，整个网络是标准的encoder-decoder网络，特点是参数少，计算快，应用性强，对于一般场景适应度很高。U-Net最早于2015年提出，并在ISBI 2015 Cell Tracking Challenge取得了第一。经过发展，目前有多个变形和应用。
@@ -58,6 +59,14 @@ HRNet在人体姿态估计、语义分割和目标检测领域都取得了显著
 
 ![](./imgs/hrnet.png)
 
+### Fast-SCNN
+
+Fast-SCNN [6] 是一个面向实时的语义分割网络。在双分支的结构基础上，大量使用了深度可分离卷积和逆残差（inverted-residual）模块，并且使用特征融合构造金字塔池化模块 (Pyramid Pooling Module)来融合上下文信息。这使得Fast-SCNN在保持高效的情况下能学习到丰富的细节信息。
+
+整个网络结构如下：
+
+![](./imgs/fast-scnn.png)
+
 ## 参考文献
 
 [1] [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/abs/1505.04597)
@@ -72,3 +81,6 @@ HRNet在人体姿态估计、语义分割和目标检测领域都取得了显著
 
 [6] [Deep High-Resolution Representation Learning for Visual Recognition](https://arxiv.org/abs/1908.07919)
 
+[7] [Fast-SCNN: Fast Semantic Segmentation Network](https://arxiv.org/abs/1902.04502)
+
+
diff --git a/slim/distillation/README.md b/slim/distillation/README.md
index 2bd772a1001e11efa89324315fa32d44032ade05..d7af90beb3a7fd4fa6bb3775d45b0fd6aadc0133 100644
--- a/slim/distillation/README.md
+++ b/slim/distillation/README.md
@@ -89,7 +89,6 @@ python -m paddle.distributed.launch ./slim/distillation/train_distill.py \
 --log_steps 10 --cfg ./slim/distillation/cityscape.yaml \
 --teacher_cfg ./slim/distillation/cityscape_teacher.yaml \
 --use_gpu \
---use_mpio \
 --do_eval 
 ```
 
diff --git a/slim/nas/README.md b/slim/nas/README.md
index cddfc5a82f07ab0b3f2e2acad6a4c0f7b2ed650c..31e8f93f608002504cdaeaed940e4b41c138e00c 100644
--- a/slim/nas/README.md
+++ b/slim/nas/README.md
@@ -46,7 +46,7 @@ SLIM:
 ## 训练与评估
 执行以下命令，边训练边评估
 ```shell
-CUDA_VISIBLE_DEVICES=0 python -u ./slim/nas/train_nas.py --log_steps 10 --cfg configs/deeplabv3p_mobilenetv2_cityscapes.yaml --use_gpu --use_mpio \
+CUDA_VISIBLE_DEVICES=0 python -u ./slim/nas/train_nas.py --log_steps 10 --cfg configs/deeplabv3p_mobilenetv2_cityscapes.yaml --use_gpu \
 SLIM.NAS_PORT 23333 \
 SLIM.NAS_ADDRESS "" \
 SLIM.NAS_SEARCH_STEPS 2 \
diff --git a/slim/prune/README.md b/slim/prune/README.md
index b6a45238938567a845b44ff768db6982bfeab55c..25505606e3fcc8c8e7c6beba68cdb8d39c1c56b1 100644
--- a/slim/prune/README.md
+++ b/slim/prune/README.md
@@ -46,7 +46,7 @@ SLIM.PRUNE_RATIOS '[0.1,0.1,0.1]'
 
 ```shell
 CUDA_VISIBLE_DEVICES=0 
-python -u ./slim/prune/eval_prune.py --cfg configs/cityscape_fast_scnn.yaml --use_gpu --use_mpio \
+python -u ./slim/prune/eval_prune.py --cfg configs/cityscape_fast_scnn.yaml --use_gpu \
 TEST.TEST_MODEL your_trained_model \
 ```
 
diff --git a/turtorial/finetune_fast_scnn.md b/turtorial/finetune_fast_scnn.md
index 188a51edf9d138bb6832849c9ab2ad8afbcd3cd4..31541b796849277085104abf1df13284e264fae8 100644
--- a/turtorial/finetune_fast_scnn.md
+++ b/turtorial/finetune_fast_scnn.md
@@ -114,6 +114,6 @@ python pdseg/eval.py --use_gpu --cfg ./configs/fast_scnn_pet.yaml
 | ICNet/bn |(1024, 2048) |8.76ms| 0.6831 |
 | Fast-SCNN/bn | (1024, 2048) |6.28ms| 0.6964 |
 
-上述测试环境为v100. 测试使用paddle的推理接口[zero_copy](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/advanced_usage/deploy/inference/python_infer_cn.html#id8)的方式，模型输出是类别，即argmax后的值。
+上述测试环境为v100. 测试使用paddle的推理接口[zero_copy](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/advanced_guide/inference_deployment/inference/python_infer_cn.html#id8)的方式，模型输出是类别，即argmax后的值。