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README.md

@@ -33,12 +33,12 @@ English | [简体中文](README_ch.md)
 - 🔥 **2022.8.18：Release FastDeploy [release/v0.2.0](https://github.com/PaddlePaddle/FastDeploy/releases/tag/release%2F0.2.0)** <br>
   - **New server-side deployment upgrade: faster inference performance, support more visual model**
     - Release high-performance inference engine SDK based on x86 CPUs and NVIDIA GPUs, with significant increase in inference speed
-    - Integrate Paddle Inference, ONNX Runtime, TensorRT and other inference engines and provide a seamless deployment experience
-    - Supports full range of object detection models such as YOLOv7, YOLOv6, YOLOv5, PP-YOLOE and provides [End-To-End Deployment Demos](examples/vision/detection/)
+    - Integrate Paddle Inference, ONNXRuntime, TensorRT and other inference engines and provide a seamless deployment experience
+    - Supports full range of object detection models such as YOLOv7, YOLOv6, YOLOv5, PP-YOLOE and provides [End-To-End Deployment Demos]](examples/vision/detection/)
     - Support over 40 key models and [Demo Examples](examples/vision/) including face detection, face recognition, real-time portrait matting, image segmentation.
     - Support deployment in both Python and C++
   - **Supports Rexchip, Amlogic, NXP and other NPU chip deployment capabilities on end-side deployment**
-    - Release Lightweight Object Detection [Picodet-NPU Deployment Demo](https://github.com/PaddlePaddle/Paddle-Lite-Demo/tree/develop/object_detection/linux/picodet_detection), providing the full quantized inference capability for INT8.
+    - Release Lightweight Object Detection [Picodet-NPU Deployment Demo](https://github.com/PaddlePaddle/Paddle-Lite-Demo/tree/develop/object_detection/linux/picodet_ detection), providing the full quantized inference capability for INT8.
 
 ## Contents
 
@@ -100,22 +100,22 @@ wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/0000000
 ```
 
 * Test inference resultsTest inference results
-
-```python
- # For deployment of GPU/TensorRT, please refer to examples/vision/detection/paddledetection/python
- import cv2
- import fastdeploy.vision as vision
 
- model = vision.detection.PPYOLOE("ppyoloe_crn_l_300e_coco/model.pdmodel",
+  ```python
+  # For deployment of GPU/TensorRT, please refer to examples/vision/detection/paddledetection/python
+  import cv2
+  import fastdeploy.vision as vision
+  ```
+
+model = vision.detection.PPYOLOE("ppyoloe_crn_l_300e_coco/model.pdmodel",
                                  "ppyoloe_crn_l_300e_coco/model.pdiparams",
                                  "ppyoloe_crn_l_300e_coco/infer_cfg.yml")
- im = cv2.imread("000000014439.jpg")
- result = model.predict(im.copy())
- print(result)
+im = cv2.imread("000000014439.jpg")
+result = model.predict(im.copy())
+print(result)
 
- vis_im = vision.vis_detection(im, result, score_threshold=0.5)
- cv2.imwrite("vis_image.jpg", vis_im)
-```
+vis_im = vision.vis_detection(im, result, score_threshold=0.5)
+cv2.imwrite("vis_image.jpg", vis_im)
 
 ### A Quick Start for C++ SDK