diff --git a/PyTorch/contrib/cv/classification/EfficientNet-B3/README -raw.md b/PyTorch/contrib/cv/classification/EfficientNet-B3/README -raw.md
new file mode 100644
index 0000000000000000000000000000000000000000..a448405d963d209c90f5f9b31a563cca0269ff84
--- /dev/null
+++ b/PyTorch/contrib/cv/classification/EfficientNet-B3/README -raw.md	
@@ -0,0 +1,52 @@
+# EfficientNet-B3
+
+This implements training of Efficientnet-B3 on the ImageNet dataset, mainly modified from [pycls](https://github.com/facebookresearch/pycls).
+
+## EfficientNet-B3 Detail 
+
+For details, see[pycls](https://github.com/facebookresearch/pycls).
+
+
+## Requirements 
+
+- Install PyTorch ([pytorch.org](http://pytorch.org))
+- pip install pycls
+- git clone https://github.com/facebookresearch/pycls
+- pip install -r requirements.txt
+- modify path of dataset in pycls/datasets/loader.py, you can modify the variable _DATA_DIR to your path of imagenet dataset.
+
+## Training 
+
+To train a model, run scripts with the desired model architecture and the path to the ImageNet dataset:
+
+```bash
+# 1p train 1p
+bash test/train_full_1p.sh --data_path={data/path} # train accuracy
+
+bash test/train_performance_1p.sh --data_path={data/path} # train performance
+
+#  8p train 8p
+bash test/train_full_8p.sh --data_path={data/path} # train accuracy
+
+bash test/train_performance_8p.sh --data_path={data/path} # train performance
+
+# 1p eval 1p
+bash test/train_eval_8p.sh --data_path={data/path}
+
+# online inference demo 
+python3.7 demo.py
+
+# To ONNX
+python3.7.5 pthtar2onnx.py
+
+```
+
+## EfficientNet-B3 training result 
+
+| Acc@1  | FPS  | Npu_nums | Epochs | AMP_Type |
+| :----: | :--: | :------: | :----: | :------: |
+|   -    | 267   |    1     |  100   |    O2    |
+| 77.3418 | 1558  |    8     |  100   |    O2    |
+
+
+
diff --git a/PyTorch/contrib/cv/classification/EfficientNet-B3/README.md b/PyTorch/contrib/cv/classification/EfficientNet-B3/README.md
index a448405d963d209c90f5f9b31a563cca0269ff84..cd3ee2f5e46e4059473cefd59d07c4f748d34cca 100644
--- a/PyTorch/contrib/cv/classification/EfficientNet-B3/README.md
+++ b/PyTorch/contrib/cv/classification/EfficientNet-B3/README.md
@@ -4,7 +4,8 @@ This implements training of Efficientnet-B3 on the ImageNet dataset, mainly modi
 
 ## EfficientNet-B3 Detail 
 
-For details, see[pycls](https://github.com/facebookresearch/pycls).
+1. Migrate to Torch1.8
+2. Note: The original readme.md is changed to README_raw.md
 
 
 ## Requirements 
@@ -14,39 +15,45 @@ For details, see[pycls](https://github.com/facebookresearch/pycls).
 - git clone https://github.com/facebookresearch/pycls
 - pip install -r requirements.txt
 - modify path of dataset in pycls/datasets/loader.py, you can modify the variable _DATA_DIR to your path of imagenet dataset.
+- Download the ImageNet dataset and Dataset must be in Efficientnet-B3/pycls/datasets/data
+- Note: Torch and Apex are linux_AARCH64 architecture packages
+  - CANN: 5.0.RC1
+  - Pytorch: 1.8.1+ascend.rc2.20220712
+  - torch-npu: 1.8.1rc2.post20220505
+  - python：3.7.5
+  - OS:Ubuntu 18.04.6+aarch
 
 ## Training 
 
 To train a model, run scripts with the desired model architecture and the path to the ImageNet dataset:
 
 ```bash
-# 1p train 1p
-bash test/train_full_1p.sh --data_path={data/path} # train accuracy
+# Link the dataset from source to target
+ln -s {data/path} EfficientNet-B3/pycls/datasets/data
 
+# 1p train 1p
+# run 1 epoch
 bash test/train_performance_1p.sh --data_path={data/path} # train performance
 
-#  8p train 8p
-bash test/train_full_8p.sh --data_path={data/path} # train accuracy
-
+# 8p train 8p
+# run 1 epcoh
 bash test/train_performance_8p.sh --data_path={data/path} # train performance
+# run 100 epoch, running time: 27h  
+bash test/train_full_8p.sh --data_path={data/path} # train accuracy
 
 # 1p eval 1p
 bash test/train_eval_8p.sh --data_path={data/path}
-
-# online inference demo 
-python3.7 demo.py
-
-# To ONNX
-python3.7.5 pthtar2onnx.py
-
 ```
 
 ## EfficientNet-B3 training result 
 
-| Acc@1  | FPS  | Npu_nums | Epochs | AMP_Type |
-| :----: | :--: | :------: | :----: | :------: |
-|   -    | 267   |    1     |  100   |    O2    |
-| 77.3418 | 1558  |    8     |  100   |    O2    |
+| Acc@1  | FPS  | Npu_nums | Epochs | AMP_Type | Torch |
+| :----: | :--: | :------: | :----: | :------: | :---: |
+|   -    | 267   |    1     |  100   |    O2    |  1.5  | 
+| 77.3418 | 1558  |    8     |  100   |    O2    |  1.5  |
+|   -    | 321   |    1     |  100   |    O2    |  1.8  |
+| 77.0613 | 1317  |    8     |  100   |    O2    |  1.8  |   
+
 
 
 
diff --git a/PyTorch/contrib/cv/classification/EfficientNet-B3/requirements.txt b/PyTorch/contrib/cv/classification/EfficientNet-B3/requirements.txt
index 678848ed816ab39b511aa8cc0181870d2b3a5e82..9e9fc6bb5abaf62cbe84e595a7648ed2fcb865e6 100644
--- a/PyTorch/contrib/cv/classification/EfficientNet-B3/requirements.txt
+++ b/PyTorch/contrib/cv/classification/EfficientNet-B3/requirements.txt
@@ -1,7 +1,22 @@
-isort==4.3.21
-fvcore
-parameterized
-setuptools
-simplejson
-yacs
-
+certifi==2018.10.15
+cycler==0.11.0
+decorator==5.1.1
+fonttools==4.33.3
+iopath==0.1.9
+kiwisolver==1.4.3
+matplotlib==3.5.2
+mpmath==1.2.1
+numpy==1.21.6
+opencv-python==4.6.0.66
+packaging==21.3
+Pillow==9.1.1
+portalocker==2.4.0
+pyparsing==3.0.9
+python-dateutil==2.8.2
+PyYAML==6.0
+simplejson==3.17.6
+six==1.16.0
+sympy==1.10.1
+tqdm==4.64.0
+typing_extensions==4.2.0
+yacs==0.1.8
diff --git a/PyTorch/contrib/cv/classification/EfficientNet-B3/test/train_eval_8p.sh b/PyTorch/contrib/cv/classification/EfficientNet-B3/test/train_eval_8p.sh
index e96a5c713df539915fe13ef7b598e61a6abc5b22..7904f2eb47925508b8ca7908cf84bdcf546271cb 100644
--- a/PyTorch/contrib/cv/classification/EfficientNet-B3/test/train_eval_8p.sh
+++ b/PyTorch/contrib/cv/classification/EfficientNet-B3/test/train_eval_8p.sh
@@ -41,7 +41,7 @@ fi
 
 # 数据集软链到脚本内部
 cur_path=`pwd`
-default_data_path=${cur_path}/pycls/datasets/data/
+default_data_path=${cur_path}/pycls/datasets/data
 rm -rf ${default_data_path}/imagenet
 ln -s ${data_path} ${default_data_path}/imagenet
 
diff --git a/PyTorch/contrib/cv/classification/EfficientNet-B3/test/train_full_1p.sh b/PyTorch/contrib/cv/classification/EfficientNet-B3/test/train_full_1p.sh
index ee7ed711a41adbbf38ae2374fe6c0fa1e9b890e6..077f096b4d446fde3b94276ce9ee03583567fdd7 100644
--- a/PyTorch/contrib/cv/classification/EfficientNet-B3/test/train_full_1p.sh
+++ b/PyTorch/contrib/cv/classification/EfficientNet-B3/test/train_full_1p.sh
@@ -49,7 +49,7 @@ fi
 
 # 数据集软链到脚本内
 cur_path=`pwd`
-default_data_path=${cur_path}/pycls/datasets/data/
+default_data_path=${cur_path}/pycls/datasets/data
 rm -rf ${default_data_path}/imagenet
 ln -s ${data_path} ${default_data_path}/imagenet
 
diff --git a/PyTorch/contrib/cv/classification/EfficientNet-B3/test/train_full_8p.sh b/PyTorch/contrib/cv/classification/EfficientNet-B3/test/train_full_8p.sh
index a2d1bc6d9d70b67eda2e39536497ca4d2c95f14e..8b8cd6793b4ede788c13da49f5f9d24f0032192c 100644
--- a/PyTorch/contrib/cv/classification/EfficientNet-B3/test/train_full_8p.sh
+++ b/PyTorch/contrib/cv/classification/EfficientNet-B3/test/train_full_8p.sh
@@ -33,7 +33,7 @@ fi
 
 # 数据集软链到脚本内部
 cur_path=`pwd`
-default_data_path=${cur_path}/pycls/datasets/data/
+default_data_path=${cur_path}/pycls/datasets/data
 rm -rf ${default_data_path}/imagenet
 ln -s ${data_path} ${default_data_path}/imagenet
 
diff --git a/PyTorch/contrib/cv/classification/EfficientNet-B3/test/train_performance_1p.sh b/PyTorch/contrib/cv/classification/EfficientNet-B3/test/train_performance_1p.sh
index b6dbf22b45ae38de530c1a7114ae8f9c98982491..c7b30add5a6fee06e9aae890c31fe5a166c2dfc4 100644
--- a/PyTorch/contrib/cv/classification/EfficientNet-B3/test/train_performance_1p.sh
+++ b/PyTorch/contrib/cv/classification/EfficientNet-B3/test/train_performance_1p.sh
@@ -49,7 +49,7 @@ fi
 
 # 数据集软链到脚本内
 cur_path=`pwd`
-default_data_path=${cur_path}/pycls/datasets/data/
+default_data_path=${cur_path}/pycls/datasets/data
 rm -rf ${default_data_path}/imagenet
 ln -s ${data_path} ${default_data_path}/imagenet
 
diff --git a/PyTorch/contrib/cv/classification/EfficientNet-B3/test/train_performance_8p.sh b/PyTorch/contrib/cv/classification/EfficientNet-B3/test/train_performance_8p.sh
index a0c0fe31396d8e23830816b1898fa75bee3b022c..fe0ba90c3731eecacf721fa4e2bd7644e56d9fb3 100644
--- a/PyTorch/contrib/cv/classification/EfficientNet-B3/test/train_performance_8p.sh
+++ b/PyTorch/contrib/cv/classification/EfficientNet-B3/test/train_performance_8p.sh
@@ -33,7 +33,7 @@ fi
 
 # 数据集软链到脚本内部
 cur_path=`pwd`
-default_data_path=${cur_path}/pycls/datasets/data/
+default_data_path=${cur_path}/pycls/datasets/data
 rm -rf ${default_data_path}/imagenet
 ln -s ${data_path} ${default_data_path}/imagenet
 
diff --git a/PyTorch/contrib/cv/classification/EfficientNet-B3/tools/train_net.py b/PyTorch/contrib/cv/classification/EfficientNet-B3/tools/train_net.py
index 270aca8c403f67efb914df69931d6fcf8406ead1..e5d6d0abd96956be62d6549dcec1860686b79f79 100644
--- a/PyTorch/contrib/cv/classification/EfficientNet-B3/tools/train_net.py
+++ b/PyTorch/contrib/cv/classification/EfficientNet-B3/tools/train_net.py
@@ -29,6 +29,8 @@ import pycls.core.trainer as trainer
 from pycls.core.config import cfg
 import argparse,sys,os,torch
 import torch
+if torch.__version__>= '1.8':
+    import torch_npu
 
 def init_process_group(proc_rank, world_size, device_type="npu", port="29588"):
     """Initializes the default process group."""