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+English | [简体中文](README.md)
+
+# YOLOv7 Python Deployment Demo
+
+Two steps before deployment:
+
+- 1. The hardware and software environment meets the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs_en/environment.md)
+- 2. Install FastDeploy Python whl package. Please refer to [FastDeploy Python Installation](../../../../../docs_en/quick_start)
+
+
+This doc provides a quick `infer.py` demo of YOLOv7 deployment on CPU/GPU, and accelerated GPU deployment by TensorRT. Run the following command:
+
+```bash
+# Download sample deployment code
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd examples/vision/detection/yolov7/python/
+
+# Download yolov7 model files and test images
+wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov7.onnx
+wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
+
+# CPU Inference
+python infer.py --model yolov7.onnx --image 000000014439.jpg --device cpu
+# GPU 
+python infer.py --model yolov7.onnx --image 000000014439.jpg --device gpu
+# GPU上使用TensorRT推理
+python infer.py --model yolov7.onnx --image 000000014439.jpg --device gpu --use_trt True
+```
+
+The visualisation of the results is as follows.
+
+<img width="640" src="https://user-images.githubusercontent.com/67993288/183847558-abcd9a57-9cd9-4891-b09a-710963c99b74.jpg">
+
+## YOLOv7 Python Interface
+
+```python
+fastdeploy.vision.detection.YOLOv7(model_file, params_file=None, runtime_option=None, model_format=Frontend.ONNX)
+```
+
+YOLOv7 model loading and initialisation, with model_file being the exported ONNX model format.
+
+**Parameters**
+
+> * **model_file**(str): Model file path
+> * **params_file**(str): Parameter file path. If the model format is ONNX, the parameter can be filled with an empty string.
+> * **runtime_option**(RuntimeOption): Back-end inference configuration. The default is None, i.e. the default is applied
+> * **model_format**(Frontend): Model format. The default is ONNX format
+
+### Predict Function
+
+> ```python
+> YOLOv7.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5)
+> ```
+> 
+> Model prediction interface with direct output of detection results from the image input.
+> 
+> **Parameters**
+> 
+> > * **image_data**(np.ndarray): Input image. Images need to be in HWC or BGR format
+> > * **conf_threshold**(float): Filter threshold for detection box confidence
+> > * **nms_iou_threshold**(float): iou thresholds during NMS processing
+
+> **Return**
+> 
+> > Return to`fastdeploy.vision.DetectionResult`Struct. For more details, please refer to [Vision Model Results](../../../../../docs/api/vision_results/)
+
+### Class Member Variables
+
+#### Pre-processing parameters
+
+Users can modify the following pre-processing parameters for their needs. This will affect the final reasoning and deployment results
+
+> > * **size**(list[int]):  This parameter modifies the 'resize' during preprocessing and contains two integer elements representing [width, height]. The default value is [640, 640].
+> > * **padding_value**(list[float]): This parameter modifies the value of the padding when resizing the image. It contains three floating-point elements, representing the values of the three channels. The default value is [114, 114, 114].
+> > * **is_no_pad**(bool): This parameter determines whether the image is resized by padding, `is_no_pad=ture` means no padding is used. The default value is `is_no_pad=false`.
+> > * **is_mini_pad**(bool): This parameter allows the width and height of the image after resize to be the closest value to the `size` member variable, which the pixel size of the padding can be divided by the `stride` member variable. The default value is `is_mini_pad=false`.
+> > * **stride**(int): Used with`stris_mini_pad` member value. The default value is`stride=32`
+
+## Related files
+
+- [YOLOv7 Model Introduction](..)
+- [YOLOv7 C++ Deployment](../cpp)
+- [Vision Model Results](../../../../../docs_en/api/vision_results/)