diff --git a/configs/face_detection/README_en.md b/configs/face_detection/README_en.md
index bf798b3c0b01654239df07338c6dc7174958024f..f7295f32da996cb2502ae0094b6a8dda540f3920 100644
--- a/configs/face_detection/README_en.md
+++ b/configs/face_detection/README_en.md
@@ -110,6 +110,59 @@ legend_name = 'Paddle-BlazeFace';
 matlab -nodesktop -nosplash -nojvm -r "run wider_eval.m;quit;"
 ```
 
+### Use by Python Code
+In order to support development, here is an example of using the Paddle Detection whl package to make predictions through Python code.
+```python
+import cv2
+import paddle
+import numpy as np
+from ppdet.core.workspace import load_config
+from ppdet.engine import Trainer
+from ppdet.metrics import get_infer_results
+from ppdet.data.transform.operators import NormalizeImage, Permute
+
+
+if __name__ == '__main__':
+    # prepare for the parameters
+    config_path = 'PaddleDetection/configs/face_detection/blazeface_1000e.yml'
+    cfg = load_config(config_path)
+    weight_path = 'PaddleDetection/output/blazeface_1000e.pdparams'
+    infer_img_path = 'PaddleDetection/demo/hrnet_demo.jpg'
+    cfg.weights = weight_path
+    bbox_thre = 0.8
+    paddle.set_device('gpu')
+    # create the class object
+    trainer = Trainer(cfg, mode='test')
+    trainer.load_weights(cfg.weights)
+    trainer.model.eval()
+    normaler = NormalizeImage(mean=[123, 117, 104], std=[127.502231, 127.502231, 127.502231], is_scale=False)
+    permuter = Permute()
+    # read the image file
+    im = cv2.imread(infer_img_path)
+    im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
+    # prepare for the data dict
+    data_dict = {'image': im}
+    data_dict = normaler(data_dict)
+    data_dict = permuter(data_dict)
+    h, w, c = im.shape
+    data_dict['im_id'] = paddle.Tensor(np.array([[0]]))
+    data_dict['im_shape'] = paddle.Tensor(np.array([[h, w]], dtype=np.float32))
+    data_dict['scale_factor'] = paddle.Tensor(np.array([[1., 1.]], dtype=np.float32))
+    data_dict['image'] = paddle.Tensor(data_dict['image'].reshape((1, c, h, w)))
+    data_dict['curr_iter'] = paddle.Tensor(np.array([0]))
+    # do the prediction
+    outs = trainer.model(data_dict)
+    # to do the postprocess to get the final bbox info
+    for key in ['im_shape', 'scale_factor', 'im_id']:
+        outs[key] = data_dict[key]
+    for key, value in outs.items():
+        outs[key] = value.numpy()
+    clsid2catid, catid2name = {0: 'face'}, {0: 0}
+    batch_res = get_infer_results(outs, clsid2catid)
+    bbox = [sub_dict for sub_dict in batch_res['bbox'] if sub_dict['score'] > bbox_thre]
+    print(bbox)
+```
+
 
 ## Citations