[Cherry pick] update readme (#4410)

* chang image

[Cherry pick] update readme (#4410)
* chang image
b7748ee2 · Kaipeng Deng · GitHub · 6e65f11d · b7748ee2 · b7748ee2
7 changed file
--- a/README_cn.md
+++ b/README_cn.md
@@ -4,7 +4,7 @@
 # 产品动态
 - 2021.11.03: 发布[release/2.3版本](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3)，发布**轻量级检测特色模型**⚡[PP-PicoDet](configs/picodet)，发布**轻量级关键点特色模型**⚡[PP-TinyPose](configs/keypoint/tiny_pose)。新增[Swin Transformer](configs/faster_rcnn)，[TOOD](configs/tood)，[GFL](configs/gfl)目标检测模型。发布[Sniper](configs/sniper)小目标检测优化模型，发布针对EdgeBoard优化[PP-YOLO-EB](configs/ppyolo)模型。新增轻量化关键点模型[Lite HRNet](configs/keypoint)关键点模型并支持Paddle Lite部署。
- 2021.08.10: 发布[release/2.2版本]((https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.2))，发布Transformer检测系列模型，包括[DETR](configs/detr), [Deformable DETR](configs/deformable_detr), [Sparse RCNN](configs/sparse_rcnn)。新增Dark HRNet关键点模型和MPII数据集[关键点模型](configs/keypoint)，新增[人头](configs/mot/headtracking21)、[车辆](configs/mot/vehicle)跟踪垂类模型。
+- 2021.08.10: 发布[release/2.2版本](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.2)，发布Transformer检测系列模型，包括[DETR](configs/detr), [Deformable DETR](configs/deformable_detr), [Sparse RCNN](configs/sparse_rcnn)。新增Dark HRNet关键点模型和MPII数据集[关键点模型](configs/keypoint)，新增[人头](configs/mot/headtracking21)、[车辆](configs/mot/vehicle)跟踪垂类模型。
 - 2021.05.20: 发布[release/2.1版本](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1)，新增[关键点检测](configs/keypoint)，模型包括HigherHRNet，HRNet。新增[多目标跟踪](configs/mot)能力，模型包括DeepSORT，JDE，FairMOT。发布PPYOLO系列模型压缩模型，新增[ONNX模型导出教程](deploy/EXPORT_ONNX_MODEL.md)。
 # 简介
@@ -13,13 +13,8 @@ PaddleDetection为基于飞桨PaddlePaddle的端到端目标检测套件，提
 ### PaddleDetection提供了目标检测、实例分割、多目标跟踪、关键点检测等多种能力
-<div width="900" align="center">
+<div width="1000" align="center">
-  <img src="docs/images/det.jpg" width="400" height="300" title="目标检测"/>
+  <img src="docs/images/ppdet.gif"/>
-  <img src="docs/images/ins.jpg" width="400" height="300" title="实例分割"/>
-</div>
-<div width="900" align="center">
-  <img src="docs/images/mot.gif" width="400" height="300" title="多目标跟踪"/>
-  <img src="docs/images/pose.gif" width="400" height="300" title="关键点检测"/>
 </div>
@@ -33,7 +28,7 @@ PaddleDetection为基于飞桨PaddlePaddle的端到端目标检测套件，提
 ### 套件结构概览
-<table>
+<table align="center">
  <tbody>
    <tr align="center" valign="bottom">
      <td>
@@ -66,7 +61,7 @@ PaddleDetection为基于飞桨PaddlePaddle的端到端目标检测套件，提
            <li>RetinaNet</li>
            <li>YOLOv3</li>
            <li>YOLOv4</li>  
-            <li>PP-YOLO</li>
+            <li>PP-YOLOv1/v2/Tiny</li>
            <li>SSD</li>
          </ul>
        </ul>
@@ -75,6 +70,7 @@ PaddleDetection为基于飞桨PaddlePaddle的端到端目标检测套件，提
            <li>CornerNet-Squeeze</li>
            <li>FCOS</li>  
            <li>TTFNet</li>
+            <li>PicoDet</li>
          </ul>
        </ul>
        <ul>
@@ -92,6 +88,29 @@ PaddleDetection为基于飞桨PaddlePaddle的端到端目标检测套件，提
             <li>BlazeFace-NAS</li>
            </ul>
        </ul>
+        <ul>
+          <li><b>Transformer</b></li>
+            <ul>
+             <li>DETR/Deformable DETR</li>
+             <li>Sparse RCNN</li>
+             <li>Swin Transformer</li>
+            </ul>
+        </ul>
+        <ul>
+          <li><b>Multi-Object-Tracking</b></li>
+            <ul>
+             <li>JDE</li>
+             <li>FairMOT</li>
+             <li>DeepSort</li>
+            </ul>
+        </ul>
+        <ul>
+          <li><b>KeyPoint-Detection</b></li>
+            <ul>
+             <li>HRNet</li>
+             <li>HigherHRNet</li>
+            </ul>
+        </ul>
      </td>
      <td>
        <ul>
@@ -109,6 +128,7 @@ PaddleDetection为基于飞桨PaddlePaddle的端到端目标检测套件，提
          <li>MobileNetv1/v3</li>  
          <li>GhostNet</li>
          <li>Efficientnet</li>  
+          <li>BlazeNet</li>  
        </ul>
      </td>
      <td>
@@ -120,6 +140,11 @@ PaddleDetection为基于飞桨PaddlePaddle的端到端目标检测套件，提
            <li>Non-local</li>
          </ul>  
        </ul>
+        <ul><li><b>KeyPoint</b></li>
+          <ul>
+            <li>DarkPose</li>
+          </ul>  
+        </ul>
        <ul><li><b>FPN</b></li>
          <ul>
            <li>BiFPN</li>
@@ -151,15 +176,18 @@ PaddleDetection为基于飞桨PaddlePaddle的端到端目标检测套件，提
      <td>
        <ul>
          <li>Resize</li>  
+          <li>Lighting</li>  
          <li>Flipping</li>  
          <li>Expand</li>
          <li>Crop</li>
          <li>Color Distort</li>  
          <li>Random Erasing</li>  
          <li>Mixup </li>
+          <li>Mosaic</li>
          <li>Cutmix </li>
          <li>Grid Mask</li>
          <li>Auto Augment</li>  
+          <li>Random Perspective</li>  
        </ul>  
      </td>  
    </tr>
@@ -186,6 +214,16 @@ PaddleDetection为基于飞桨PaddlePaddle的端到端目标检测套件，提
 - `PP-YOLO v2`是对`PP-YOLO`模型的进一步优化，在COCO数据集精度49.5%，Tesla V100预测速度68.9FPS
 - 图中模型均可在[模型库](#模型库)中获取
+各移动端模型在COCO数据集上精度mAP和高通骁龙865处理器上预测速度(FPS)对比图。
+<div align="center">
+  <img src="docs/images/mobile_fps_map.png" width=600/>
+</div>
+**说明：**
+- 测试数据均使用高通骁龙865(4\*A77 + 4\*A55)处理器batch size为1, 开启4线程测试，测试使用NCNN预测库，测试脚本见[MobileDetBenchmark](https://github.com/JiweiMaster/MobileDetBenchmark)
+- [PP-PicoDet](configs/picodet)及[PP-YOLO-Tiny](configs/ppyolo)为PaddleDetection自研模型，其余模型PaddleDetection暂未提供
 ## 文档教程
 ### 入门教程

--- a/README_en.md
+++ b/README_en.md
@@ -3,8 +3,8 @@ English | [简体中文](README_cn.md)
 # Product news
- 2021.11.03: Release [release/2.3]((https://github.com/PaddlePaddle/Paddleetection/tree/release/2.3) version. Release mobile object detection model ⚡[PP-PicoDet](configs/picodet), mobile keypoint detection model ⚡[PP-TinyPose](configs/keypoint/tiny_pose). Release object detection models, including [Swin-Transformer](configs/faster_rcnn), [TOOD](configs/tood), [GFL](configs/gfl), release [Sniper](configs/sniper) tiny object detection models and optimized [PP-YOLO-EB](configs/ppyolo) model for EdgeBoard. Release mobile keypoint detection model [Lite HRNet](configs/keypoint).
+- 2021.11.03: Release [release/2.3](https://github.com/PaddlePaddle/Paddleetection/tree/release/2.3) version. Release mobile object detection model ⚡[PP-PicoDet](configs/picodet), mobile keypoint detection model ⚡[PP-TinyPose](configs/keypoint/tiny_pose). Release object detection models, including [Swin-Transformer](configs/faster_rcnn), [TOOD](configs/tood), [GFL](configs/gfl), release [Sniper](configs/sniper) tiny object detection models and optimized [PP-YOLO-EB](configs/ppyolo) model for EdgeBoard. Release mobile keypoint detection model [Lite HRNet](configs/keypoint).
- 2021.08.10: Release [release/2.2]((https://github.com/PaddlePaddle/Paddleetection/tree/release/2.2) version. Release Transformer object detection models, including [DETR](configs/detr), [Deformable DETR](configs/deformable_detr), [Sparse RCNN](configs/sparse_rcnn). Release [keypoint detection](configs/keypoint) models, including DarkHRNet and model trained on MPII dataset. Release [head-tracking](configs/mot/headtracking21) and [vehicle-tracking](configs/mot/vehicle) multi-object tracking models.
+- 2021.08.10: Release [release/2.2](https://github.com/PaddlePaddle/Paddleetection/tree/release/2.2) version. Release Transformer object detection models, including [DETR](configs/detr), [Deformable DETR](configs/deformable_detr), [Sparse RCNN](configs/sparse_rcnn). Release [keypoint detection](configs/keypoint) models, including DarkHRNet and model trained on MPII dataset. Release [head-tracking](configs/mot/headtracking21) and [vehicle-tracking](configs/mot/vehicle) multi-object tracking models.
 - 2021.05.20: Release [release/2.1]((https://github.com/PaddlePaddle/Paddleetection/tree/release/2.1) version. Release [Keypoint Detection](configs/keypoint), including HigherHRNet and HRNet, [Multi-Object Tracking](configs/mot), including DeepSORT，JDE and FairMOT. Release model compression for PPYOLO series models.Update documents such as [EXPORT ONNX MODEL](deploy/EXPORT_ONNX_MODEL.md).
@@ -14,13 +14,8 @@ PaddleDetection is an end-to-end object detection development kit based on Paddl
 ### PaddleDetection provides image processing capabilities such as object detection, instance segmentation, multi-object tracking, keypoint detection and etc.
-<div width="900" align="center">
+<div width="1000" align="center">
-  <img src="docs/images/det.jpg" width="400" height="300" title="目标检测"/>
+  <img src="docs/images/ppdet.gif"/>
-  <img src="docs/images/ins.jpg" width="400" height="300" title="实例分割"/>
-</div>
-<div width="900" align="center">
-  <img src="docs/images/mot.gif" width="400" height="300" title="多目标跟踪"/>
-  <img src="docs/images/pose.gif" width="400" height="300" title="关键点检测"/>
 </div>
@@ -40,7 +35,7 @@ Based on the high performance core of PaddlePaddle, advantages of training speed
 #### Overview of Kit Structures
-<table>
+<table align="center">
  <tbody>
    <tr align="center" valign="bottom">
      <td>
@@ -65,7 +60,7 @@ Based on the high performance core of PaddlePaddle, advantages of training speed
            <li>Cascade-RCNN</li>
            <li>Libra RCNN</li>
            <li>Hybrid Task RCNN</li>
-            <li>PSS-Det RCNN</li>
+            <li>PSS-Det</li>
          </ul>
        </ul>
        <ul><li><b>One-Stage Detection</b></li>
@@ -73,7 +68,7 @@ Based on the high performance core of PaddlePaddle, advantages of training speed
            <li>RetinaNet</li>
            <li>YOLOv3</li>
            <li>YOLOv4</li>  
-            <li>PP-YOLO</li>
+            <li>PP-YOLOv1/v2/Tiny</li>
            <li>SSD</li>
          </ul>
        </ul>
@@ -82,6 +77,7 @@ Based on the high performance core of PaddlePaddle, advantages of training speed
            <li>CornerNet-Squeeze</li>
            <li>FCOS</li>  
            <li>TTFNet</li>
+            <li>PicoDet</li>
          </ul>
        </ul>
        <ul>
@@ -92,13 +88,36 @@ Based on the high performance core of PaddlePaddle, advantages of training speed
            </ul>
        </ul>
        <ul>
-          <li><b>Face-Detection</b></li>
+          <li><b>Face-Detction</b></li>
            <ul>
             <li>FaceBoxes</li>
             <li>BlazeFace</li>
             <li>BlazeFace-NAS</li>
            </ul>
        </ul>
+        <ul>
+          <li><b>Transformer</b></li>
+            <ul>
+             <li>DETR/Deformable DETR</li>
+             <li>Sparse RCNN</li>
+             <li>Swin Transformer</li>
+            </ul>
+        </ul>
+        <ul>
+          <li><b>Multi-Object-Tracking</b></li>
+            <ul>
+             <li>JDE</li>
+             <li>FairMOT</li>
+             <li>DeepSort</li>
+            </ul>
+        </ul>
+        <ul>
+          <li><b>KeyPoint-Detection</b></li>
+            <ul>
+             <li>HRNet</li>
+             <li>HigherHRNet</li>
+            </ul>
+        </ul>
      </td>
      <td>
        <ul>
@@ -116,6 +135,7 @@ Based on the high performance core of PaddlePaddle, advantages of training speed
          <li>MobileNetv1/v3</li>  
          <li>GhostNet</li>
          <li>Efficientnet</li>  
+          <li>BlazeNet</li>  
        </ul>
      </td>
      <td>
@@ -127,6 +147,11 @@ Based on the high performance core of PaddlePaddle, advantages of training speed
            <li>Non-local</li>
          </ul>  
        </ul>
+        <ul><li><b>KeyPoint</b></li>
+          <ul>
+            <li>DarkPose</li>
+          </ul>  
+        </ul>
        <ul><li><b>FPN</b></li>
          <ul>
            <li>BiFPN</li>
@@ -158,15 +183,18 @@ Based on the high performance core of PaddlePaddle, advantages of training speed
      <td>
        <ul>
          <li>Resize</li>  
+          <li>Lighting</li>  
          <li>Flipping</li>  
          <li>Expand</li>
          <li>Crop</li>
          <li>Color Distort</li>  
          <li>Random Erasing</li>  
          <li>Mixup </li>
+          <li>Mosaic</li>
          <li>Cutmix </li>
          <li>Grid Mask</li>
          <li>Auto Augment</li>  
+          <li>Random Perspective</li>  
        </ul>  
      </td>  
    </tr>
@@ -178,24 +206,35 @@ Based on the high performance core of PaddlePaddle, advantages of training speed
 </table>
 #### Overview of Model Performance
-The relationship between COCO mAP and FPS on Tesla V100 of representative models of each architectures and backbones.
+The relationship between COCO mAP and FPS on Tesla V100 of representative models of each server side architectures and backbones.
 <div align="center">
  <img src="docs/images/fps_map.png" />
-</div>
+  </div>
-**NOTE:**
+  **NOTE:**
- `CBResNet stands` for `Cascade-Faster-RCNN-CBResNet200vd-FPN`, which has highest mAP on COCO as 53.3%
+  - `CBResNet stands` for `Cascade-Faster-RCNN-CBResNet200vd-FPN`, which has highest mAP on COCO as 53.3%
- `Cascade-Faster-RCNN` stands for `Cascade-Faster-RCNN-ResNet50vd-DCN`, which has been optimized to 20 FPS inference speed when COCO mAP as 47.8% in PaddleDetection models
+  - `Cascade-Faster-RCNN` stands for `Cascade-Faster-RCNN-ResNet50vd-DCN`, which has been optimized to 20 FPS inference speed when COCO mAP as 47.8% in PaddleDetection models
- `PP-YOLO` achieves mAP of 45.9% on COCO and 72.9FPS on Tesla V100. Both precision and speed surpass [YOLOv4](https://arxiv.org/abs/2004.10934)
+  - `PP-YOLO` achieves mAP of 45.9% on COCO and 72.9FPS on Tesla V100. Both precision and speed surpass [YOLOv4](https://arxiv.org/abs/2004.10934)
- `PP-YOLO v2` is optimized version of `PP-YOLO` which has mAP of 49.5% and 68.9FPS on Tesla V100
+  - `PP-YOLO v2` is optimized version of `PP-YOLO` which has mAP of 49.5% and 68.9FPS on Tesla V100
- All these models can be get in [Model Zoo](#Model-Zoo)
+  - All these models can be get in [Model Zoo](#ModelZoo)
+The relationship between COCO mAP and FPS on Qualcomm Snapdragon 865 of representative mobile side models.
+<div align="center">
+  <img src="docs/images/mobile_fps_map.png" width=600 />
+</div>
+**NOTE:**
+- All data tested on Qualcomm Snapdragon 865(4\*A77 + 4\*A55) processor with batch size of 1 and CPU threads of 4, and use NCNN library in testing, benchmark scripts is publiced at [MobileDetBenchmark](https://github.com/JiweiMaster/MobileDetBenchmark)
+- [PP-PicoDet](configs/picodet) and [PP-YOLO-Tiny](configs/ppyolo) are developed and released by PaddleDetection, other models are not provided in PaddleDetection.
 ## Tutorials

--- a/docs/images/det.jpg
+++ b/docs/images/det.jpg
--- a/docs/images/ins.jpg
+++ b/docs/images/ins.jpg
--- a/docs/images/mobile_fps_map.png
+++ b/docs/images/mobile_fps_map.png
--- a/docs/images/pose.gif
+++ b/docs/images/pose.gif
--- a/docs/images/mot.gif
+++ b/docs/images/mot.gif