diff --git a/cv/detection/centermask2/pytorch/README.md b/cv/detection/centermask2/pytorch/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..aa8a69c23bf12eaa849aadb33ba88a76d28deb9c
--- /dev/null
+++ b/cv/detection/centermask2/pytorch/README.md
@@ -0,0 +1,69 @@
+# [CenterMask](https://arxiv.org/abs/1911.06667)2
+
+[[`CenterMask(original code)`](https://github.com/youngwanLEE/CenterMask)][[`vovnet-detectron2`](https://github.com/youngwanLEE/vovnet-detectron2)][[`arxiv`](https://arxiv.org/abs/1911.06667)] [[`BibTeX`](#CitingCenterMask)]
+
+**CenterMask2** is an upgraded implementation on top of [detectron2](https://github.com/facebookresearch/detectron2) beyond original [CenterMask](https://github.com/youngwanLEE/CenterMask) based on [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark).
+
+> **[CenterMask : Real-Time Anchor-Free Instance Segmentation](https://arxiv.org/abs/1911.06667) (CVPR 2020)**<br>
+> [Youngwan Lee](https://github.com/youngwanLEE) and Jongyoul Park<br>
+> Electronics and Telecommunications Research Institute (ETRI)<br>
+> pre-print : https://arxiv.org/abs/1911.06667
+
+
+<div align="center">
+  <img src="https://dl.dropbox.com/s/yg9zr1tvljoeuyi/architecture.png" width="850px" />
+</div>
+
+  
+  
+## Installation
+All you need to use centermask2 is [detectron2](https://github.com/facebookresearch/detectron2). It's easy!    
+you just install [detectron2](https://github.com/facebookresearch/detectron2) following [INSTALL.md](https://github.com/facebookresearch/detectron2/blob/master/INSTALL.md).   
+Prepare for coco dataset following [this instruction](https://github.com/facebookresearch/detectron2/tree/master/datasets).
+```bash
+git clone https://github.com/facebookresearch/detectron2.git
+python3 -m pip install -e detectron2
+git clone https://github.com/youngwanLEE/centermask2.git
+cd centermask2
+```
+
+## Step 2: Preparing datasets
+
+Go to visit [COCO official website](https://cocodataset.org/#download), then select the COCO dataset you want to download.
+
+Take coco2017 dataset as an example, specify `/path/to/coco2017` to your COCO path in later training process, the unzipped dataset path structure sholud look like:
+
+```bash
+coco2017
+├── annotations
+│   ├── instances_train2017.json
+│   ├── instances_val2017.json
+│   └── ...
+├── train2017
+│   ├── 000000000009.jpg
+│   ├── 000000000025.jpg
+│   └── ...
+├── val2017
+│   ├── 000000000139.jpg
+│   ├── 000000000285.jpg
+│   └── ...
+├── train2017.txt
+├── val2017.txt
+└── ...
+```
+
+```bash
+mkdir -p <project_path>/datasets/
+ln -s /path/to/coco2017 <project_path>/datasets/
+```
+
+
+
+To train a model, run
+For example, to launch CenterMask training with VoVNetV2-39 backbone on 8 GPUs,
+one should execute:
+```bash
+cd centermask2
+python3 train_net.py --config-file "configs/centermask/centermask_V_39_eSE_FPN_ms_3x.yaml" --num-gpus 8
+```
+