diff --git a/configs/ppyolo/README.md b/configs/ppyolo/README.md
index 4a8c8912197681a07c6701e1bb1ab3afc23b9eb5..67a90f4883fffc40c6bb184cd3383c2a21a2dad9 100644
--- a/configs/ppyolo/README.md
+++ b/configs/ppyolo/README.md
@@ -189,11 +189,6 @@ CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inferenc
 ```
 
 
-## Future work
-
-1. more PP-YOLO tiny model
-2. PP-YOLO model with more backbones
-
 ## Appendix
 
 Optimizing method and ablation experiments of PP-YOLO compared with YOLOv3.
diff --git a/configs/ppyolo/README_cn.md b/configs/ppyolo/README_cn.md
index 1d942c51e0a7f89fde64ed9e9921b8b308089814..2f165223e71024c9cee01b3cceae358b597f7648 100644
--- a/configs/ppyolo/README_cn.md
+++ b/configs/ppyolo/README_cn.md
@@ -182,11 +182,6 @@ CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inferenc
 ```
 
 
-## 未来工作
-
-1. 发布PP-YOLO-tiny模型
-2. 发布更多骨干网络的PP-YOLO模型
-
 ## 附录
 
 PP-YOLO模型相对于YOLOv3模型优化项消融实验数据如下表所示。
diff --git a/static/configs/ppyolo/README.md b/static/configs/ppyolo/README.md
index eb9091287e24abdcf9b9d162733ea4d2b58f77d6..852138eb7184516de46d379d40bd713a89c09176 100644
--- a/static/configs/ppyolo/README.md
+++ b/static/configs/ppyolo/README.md
@@ -19,7 +19,7 @@ PP-YOLO reached mmAP(IoU=0.5:0.95) as 45.9% on COCO test-dev2017 dataset, and in
   <img src="../../docs/images/ppyolo_map_fps.png" width=500 />
 </div>
 
-PP-YOLO improved performance and speed of YOLOv3 with following methods:
+PP-YOLO and PP-YOLOv2 improved performance and speed of YOLOv3 with following methods:
 
 - Better backbone: ResNet50vd-DCN
 - Larger training batch size: 8 GPUs and mini-batch size as 24 on each GPU
@@ -31,6 +31,9 @@ PP-YOLO improved performance and speed of YOLOv3 with following methods:
 - [CoordConv](https://arxiv.org/abs/1807.03247)
 - [Spatial Pyramid Pooling](https://arxiv.org/abs/1406.4729)
 - Better ImageNet pretrain weights
+- [PAN](https://arxiv.org/abs/1803.01534)
+- Iou aware Loss
+- larger input size
 
 ## Model Zoo
 
@@ -114,6 +117,9 @@ PP-YOLO trained on Pascal VOC dataset as follows:
 | PP-YOLO            |     8      |      12    | ResNet50vd |     608     |          84.9          | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/configs/ppyolo/ppyolo_voc.yml)                   |
 | PP-YOLO            |     8      |      12    | ResNet50vd |     416     |          84.3          | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/configs/ppyolo/ppyolo_voc.yml)                   |
 | PP-YOLO            |     8      |      12    | ResNet50vd |     320     |          82.2          | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/configs/ppyolo/ppyolo_voc.yml)                   |
+| PP-YOLO_EB            |     8      |      8    | ResNet34vd |     480     |          86.4         | [model](https://bj.bcebos.com/v1/paddlemodels/object_detection/ppyolo_eb_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/configs/ppyolo/ppyolo_eb_voc.yml)                   |
+
+**Notes:** PP-YOLO-EB is specially designed for [EdgeBoard](https://ai.baidu.com/tech/hardware/deepkit) hardware.
 
 ## Getting Start
 
@@ -194,11 +200,6 @@ CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output/ppyolo -
 CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output/ppyolo --image_file=demo/000000014439_640x640.jpg --use_gpu=True --run_benchmark=True --run_mode=trt_fp16
 ```
 
-## Future work
-
-1. more PP-YOLO tiny model
-2. PP-YOLO model with more backbones
-
 ## Appendix
 
 Optimizing method and ablation experiments of PP-YOLO compared with YOLOv3.
@@ -223,3 +224,29 @@ Optimizing method and ablation experiments of PP-YOLO compared with YOLOv3.
 - All models are trained on COCO train2017 datast and evaluated on val2017 & test-dev2017 dataset，`Box AP` is evaluation results as `mAP(IoU=0.5:0.95)`.
 - Inference speed is tested on single Tesla V100 with batch size as 1 following test method and environment configuration in benchmark above.
 - [YOLOv3-DarkNet53](../yolov3_darknet.yml) with mAP as 38.9 is optimized YOLOv3 model in PaddleDetection，see [Model Zoo](../../docs/MODEL_ZOO.md) for details.
+
+
+## Citation
+
+```
+@article{huang2021pp,
+  title={PP-YOLOv2: A Practical Object Detector},
+  author={Huang, Xin and Wang, Xinxin and Lv, Wenyu and Bai, Xiaying and Long, Xiang and Deng, Kaipeng and Dang, Qingqing and Han, Shumin and Liu, Qiwen and Hu, Xiaoguang and others},
+  journal={arXiv preprint arXiv:2104.10419},
+  year={2021}
+}
+@misc{long2020ppyolo,
+title={PP-YOLO: An Effective and Efficient Implementation of Object Detector},
+author={Xiang Long and Kaipeng Deng and Guanzhong Wang and Yang Zhang and Qingqing Dang and Yuan Gao and Hui Shen and Jianguo Ren and Shumin Han and Errui Ding and Shilei Wen},
+year={2020},
+eprint={2007.12099},
+archivePrefix={arXiv},
+primaryClass={cs.CV}
+}
+@misc{ppdet2019,
+title={PaddleDetection, Object detection and instance segmentation toolkit based on PaddlePaddle.},
+author={PaddlePaddle Authors},
+howpublished = {\url{https://github.com/PaddlePaddle/PaddleDetection}},
+year={2019}
+}
+```
diff --git a/static/configs/ppyolo/README_cn.md b/static/configs/ppyolo/README_cn.md
index 1de0d8197c0614a5d92a6ec0b2a72475c81cdeb4..3025a7a1ffede65b31516c0eeaac271e554f9c93 100644
--- a/static/configs/ppyolo/README_cn.md
+++ b/static/configs/ppyolo/README_cn.md
@@ -19,7 +19,7 @@ PP-YOLO在[COCO](http://cocodataset.org) test-dev2017数据集上精度达到45.
   <img src="../../docs/images/ppyolo_map_fps.png" width=500 />
 </div>
 
-PP-YOLO从如下方面优化和提升YOLOv3模型的精度和速度：
+PP-YOLO和PP-YOLOv2从如下方面优化和提升YOLOv3模型的精度和速度：
 
 - 更优的骨干网络: ResNet50vd-DCN
 - 更大的训练batch size: 8 GPUs，每GPU batch_size=24，对应调整学习率和迭代轮数
@@ -31,6 +31,9 @@ PP-YOLO从如下方面优化和提升YOLOv3模型的精度和速度：
 - [CoordConv](https://arxiv.org/abs/1807.03247)
 - [Spatial Pyramid Pooling](https://arxiv.org/abs/1406.4729)
 - 更优的预训练模型
+- [PAN](https://arxiv.org/abs/1803.01534)
+- Iou aware Loss
+- 更大的输入尺寸
 
 ## 模型库
 
@@ -110,6 +113,10 @@ PP-YOLO在Pascal VOC数据集上训练模型如下:
 | PP-YOLO            |    8    |       12      | ResNet50vd |     608     |          84.9          | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/configs/ppyolo/ppyolo_voc.yml)                   |
 | PP-YOLO            |    8    |       12      | ResNet50vd |     416     |          84.3          | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/configs/ppyolo/ppyolo_voc.yml)                   |
 | PP-YOLO            |    8    |       12      | ResNet50vd |     320     |          82.2          | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/configs/ppyolo/ppyolo_voc.yml)                   |
+| PP-YOLO_EB            |     8      |      8    | ResNet34vd |     480     |          86.4         | [model](https://bj.bcebos.com/v1/paddlemodels/object_detection/ppyolo_eb_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/configs/ppyolo/ppyolo_eb_voc.yml)                   |
+
+**注意:** PP-YOLO-EB是针对[EdgeBoard](https://ai.baidu.com/tech/hardware/deepkit)硬件专门设计的模型.
+
 
 ## 使用说明
 
@@ -188,11 +195,6 @@ CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output/ppyolo -
 CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output/ppyolo --image_file=demo/000000014439_640x640.jpg --use_gpu=True --run_benchmark=True --run_mode=trt_fp16
 ```
 
-## 未来工作
-
-1. 发布PP-YOLO-tiny模型
-2. 发布更多骨干网络的PP-YOLO模型
-
 ## 附录
 
 PP-YOLO模型相对于YOLOv3模型优化项消融实验数据如下表所示。
@@ -217,3 +219,29 @@ PP-YOLO模型相对于YOLOv3模型优化项消融实验数据如下表所示。
 - Box AP为在COCO train2017数据集训练，val2017和test-dev2017数据集上评估`mAP(IoU=0.5:0.95)`数据
 - 推理速度为单卡V100上，batch size=1, 使用上述benchmark测试方法的测试结果，测试环境配置为CUDA 10.2，CUDNN 7.5.1
 - [YOLOv3-DarkNet53](../yolov3_darknet.yml)精度38.9为PaddleDetection优化后的YOLOv3模型，可参见[模型库](../../docs/MODEL_ZOO_cn.md)
+
+
+## 引用
+
+```
+@article{huang2021pp,
+  title={PP-YOLOv2: A Practical Object Detector},
+  author={Huang, Xin and Wang, Xinxin and Lv, Wenyu and Bai, Xiaying and Long, Xiang and Deng, Kaipeng and Dang, Qingqing and Han, Shumin and Liu, Qiwen and Hu, Xiaoguang and others},
+  journal={arXiv preprint arXiv:2104.10419},
+  year={2021}
+}
+@misc{long2020ppyolo,
+title={PP-YOLO: An Effective and Efficient Implementation of Object Detector},
+author={Xiang Long and Kaipeng Deng and Guanzhong Wang and Yang Zhang and Qingqing Dang and Yuan Gao and Hui Shen and Jianguo Ren and Shumin Han and Errui Ding and Shilei Wen},
+year={2020},
+eprint={2007.12099},
+archivePrefix={arXiv},
+primaryClass={cs.CV}
+}
+@misc{ppdet2019,
+title={PaddleDetection, Object detection and instance segmentation toolkit based on PaddlePaddle.},
+author={PaddlePaddle Authors},
+howpublished = {\url{https://github.com/PaddlePaddle/PaddleDetection}},
+year={2019}
+}
+```
diff --git a/static/docs/images/ppyolo_map_fps.png b/static/docs/images/ppyolo_map_fps.png
index c66ad2fb490d661fa9a773aa382ea5911957994e..f860d220d1c831e42a23e38fc78732426c23e2cc 100644
Binary files a/static/docs/images/ppyolo_map_fps.png and b/static/docs/images/ppyolo_map_fps.png differ