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Co-authored-by: leiqing <54695910+leiqing1@users.noreply.github.com>

docs/en/quick_start/models/python.md

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+# Python Deployment
+
+Make sure that FastDeploy is installed in the development environment. Refer to [FastDeploy Installation](../../build_and_install/) to install the pre-built FastDeploy, or build and install according to your own needs.
+
+This document uses the PaddleDetection target detection model PPYOLOE as an example to show an inference example on the CPU.
+
+## 1. Get the Model and Test Image
+
+``` python
+import fastdeploy as fd
+
+model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz"
+image_url - "https://bj.bcebos.com/fastdeploy/tests/test_det.jpg"
+fd.download_and_decompress(model_url, path=".")
+fd.download(image_url, path=".")
+```
+
+## 2. Load Model
+
+- More examples of models can be found in[FastDeploy/examples](https://github.com/PaddlePaddle/FastDeploy/tree/develop/examples)
+
+``` python
+model_file = "ppyoloe_crn_l_300e_coco/model.pdmodel"
+params_file = "ppyoloe_crn_l_300e_coco/model.pdiparams"
+infer_cfg_file = "ppyoloe_crn_l_300e_coco/infer_cfg.yml"
+model = fd.vision.detection.PPYOLOE(model_file, params_file, infer_cfg_file)
+```
+
+## 3. Get Prediction for Image Object Detection 
+
+``` python
+import cv2
+im = cv2.imread("000000014439.jpg")
+
+result = model.predict(im)
+print(result)
+```
+
+## 4. Visualize image prediction results
+
+``` python
+vis_im = fd.vision.visualize.vis_detection(im, result, score_threshold=0.5)
+cv2.imwrite("vis_image.jpg", vis_im)
+```
+
+After the visualization is executed, open `vis_image.jpg` and the visualization effect is as follows：
+
+<div  align="center">
+<img src="https://user-images.githubusercontent.com/19339784/184326520-7075e907-10ed-4fad-93f8-52d0e35d4964.jpg", width=480px, height=320px />
+</div>
+
+## other documents
+
+- [Switch model inference hardware and backend](../../faq/how_to_change_backend.md)