add map_fps fig (#638)

* add map_fps fig

README.md

@@ -63,6 +63,18 @@
 
 **注意:** Synchronized batch normalization 只能在多GPU环境下使用，不能在CPU环境或者单GPU环境下使用。
 
+以下为选取各模型结构和骨干网络的代表模型COCO数据集精度mAP和单卡Tesla V100上预测速度(FPS)关系图。
+
+<div align="center">
+  <img src="docs/images/map_fps.png" />
+</div>
+
+**说明：**
+- `CBResNet`为`Cascade-Faster-RCNN-CBResNet200vd-FPN`模型，COCO数据集mAP高达53.3%
+- `Cascade-Faster-RCNN`为`Cascade-Faster-RCNN-ResNet50vd-DCN`，PaddleDetection将其优化到COCO数据mAP为47.8%时推理速度为20FPS
+- PaddleDetection增强版`YOLOv3-ResNet50vd-DCN`在COCO数据集mAP高于原作10.6个绝对百分点，推理速度为61.3FPS，快于原作约70%
+- 图中模型均可在[模型库](#模型库)中获取
+
 ## 文档教程
 
 **最新动态：** 已发布文档教程：[https://paddledetection.readthedocs.io](https://paddledetection.readthedocs.io)

README_en.md

@@ -76,6 +76,18 @@ Advanced Features:
 
 **NOTE:** Synchronized batch normalization can only be used on multiple GPU devices, can not be used on CPU devices or single GPU device.
 
+The following is the relationship between COCO mAP and FPS on Tesla V100 of representative models of each architectures and backbones.
+
+<div align="center">
+  <img src="docs/images/map_fps.png" />
+</div>
+
+**NOTE:**
+- `CBResNet` stands for `Cascade-Faster-RCNN-CBResNet200vd-FPN`, which has highest mAP on COCO as 53.3% 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%
+- The enhanced `YOLOv3-ResNet50vd-DCN` is 10.6 absolute percentage points higher than paper on COCO mAP, and inference speed is nearly 70% faster than the darknet framework
+- All these models can be get in [Model Zoo](#Model-Zoo)
+
 ## Tutorials
 
 **News:** Documentation:[https://paddledetection.readthedocs.io](https://paddledetection.readthedocs.io)

deploy/python/infer.py

@@ -517,7 +517,7 @@ def predict_video():
     fps = 30
     width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
     height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
-    fourcc = cv2.VideoWriter_fourcc(* 'mp4v')
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
     video_name = os.path.split(FLAGS.video_file)[-1]
     if not os.path.exists(FLAGS.output_dir):
         os.makedirs(FLAGES.output_dir)