diff --git a/cv/classification/densenet/paddlepaddle/README.md b/cv/classification/densenet/paddlepaddle/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..7c4658ebdfe6ab026890535a69db4622d47602e0
--- /dev/null
+++ b/cv/classification/densenet/paddlepaddle/README.md
@@ -0,0 +1,55 @@
+# DenseNet
+## Model description
+A DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional inputs from all preceding layers and passes on its own feature-maps to all subsequent layers.
+
+## Step 1: Installing
+```bash
+git clone --recursive  https://github.com/PaddlePaddle/PaddleClas.git
+cd PaddleClas
+
+pip install urllib3==1.26.6
+pip install scikit-learn==0.21.0
+pip install opencv-python==4.6.0.66
+pip install opencv-python-headless
+yum install mesa-libGL -y
+pip install visualdl==2.2.0
+pip uninstall markdown
+pip install easydict
+```
+
+Sign up and login in [ImageNet official website](https://www.image-net.org/index.php), then choose 'Download' to download the whole ImageNet dataset. Specify `/path/to/imagenet` to your ImageNet path in later training process.
+
+The ImageNet dataset path structure should look like:
+
+```bash
+imagenet
+├── train
+│   └── n01440764
+│       ├── n01440764_10026.JPEG
+│       └── ...
+├── train_list.txt
+├── val
+│   └── n01440764
+│       ├── ILSVRC2012_val_00000293.JPEG
+│       └── ...
+└── val_list.txt
+```
+
+## Step 2: Training
+
+```bash
+# Make sure your dataset path is the same as above
+cd PaddleClas
+# Link your dataset to default location
+ln -s /path/to/imagenet ./dataset/ILSVRC2012
+
+export FLAGS_cudnn_exhaustive_search=True
+export FLAGS_cudnn_batchnorm_spatial_persistent=True
+
+export CUDA_VISIBLE_DEVICES=0,1,2,3
+python3 -u -m paddle.distributed.launch --gpus=0,1,2,3 tools/train.py -c ppcls/configs/ImageNet/DenseNet/DenseNet121.yaml -o Arch.pretrained=False -o Global.device=gpu
+```
+
+## Reference
+
+- [PaddleClas](https://github.com/PaddlePaddle/PaddleClas)
\ No newline at end of file