diff --git a/deployment/onnxruntime-python/README.md b/deployment/onnxruntime-python/README.md
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+# ONNXRuntime Inference
+
+The ONNXRuntime inference for `yolort`, both GPU and CPU are supported.
+
+## Dependencies
+
+- Ubuntu 20.04 / Windows 10
+- ONNXRuntime 1.7 +
+- OpenCV 4.5 +
+- CUDA 11 [Optional]
+
+*We didn't impose too strong restrictions on the versions of dependencies.*
+
+## Usage
+
+1. Update your PyTorch model weights from ultralytics to yolort and export to ONNX following the [notebooks with tutorials](https://github.com/zhiqwang/yolov5-rt-stack/blob/master/notebooks/).
+
+2. To infer your own images run the script as following.
+
+    ```bash
+    python example.py [--image ../../../test/assets/zidane.jpg]
+                      [--model_path ../../../notebooks/yolov5s.onnx]
+                      [--class_names ../../../notebooks/assets/coco.names]
+                      [--gpu]  # GPU switch, which is optional, and set False as default
+    ```
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diff --git a/deployment/onnxruntime-python/example.py b/deployment/onnxruntime-python/example.py
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+import argparse
+from typing import Tuple, List
+
+import numpy as np
+import cv2
+import onnxruntime as ort
+
+
+class YOLOv5Detector:
+    def __init__(self, model_path: str, gpu: bool = True) -> None:
+        ort_device = ort.get_device()
+        self._providers = None
+
+        if ort_device == 'GPU' and gpu:
+            self._providers = ['CUDAExecutionProvider']
+            print('Inference device: GPU')
+        elif gpu:
+            print('GPU is not supported by your ONNXRuntime build. Fallback to CPU.')
+        else:
+            self._providers = ['CPUExecutionProvider']
+            print('Inference device: CPU')
+
+        self._model = ort.InferenceSession(model_path, providers=self._providers)
+        self._input_names = self._model.get_inputs()[0].name
+        print('Model was initialized.')
+
+    def _preprocessing(self, image: np.ndarray) -> np.ndarray:
+        rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        blob = rgb_image.astype(np.float32) / 255.0
+        blob = np.transpose(blob, axes=(2, 0, 1))
+
+        return blob
+
+    def __call__(self, image: np.ndarray) -> Tuple[List[float], List[int], List[List[int]]]:
+        blob = self._preprocessing(image)
+        scores, class_ids, boxes = self._model.run(output_names=None,
+                                                   input_feed={self._input_names: blob})
+        boxes = boxes.astype(np.int32)
+        boxes[:, [2, 3]] -= boxes[:, [0, 1]]  # from xyxy to xywh format
+
+        return scores.tolist(), class_ids.tolist(), boxes.tolist()
+
+
+def visualize_detection(image: np.ndarray,
+                        class_names: List[str],
+                        scores: List[float],
+                        class_ids: List[int],
+                        boxes: List[List[int]]) -> None:
+
+    for i, class_id in enumerate(class_ids):
+        x, y, w, h = boxes[i]
+        conf = round(scores[i], 2)
+
+        label = class_names[class_id] + " " + str(conf)
+        text_size = cv2.getTextSize(label, cv2.FONT_ITALIC, 0.8, 2)[0]
+
+        cv2.rectangle(image, (x, y), (x + w, y + h), (229, 160, 21), 2)
+        cv2.rectangle(image, (x, y - 25), (x + text_size[0], y), (229, 160, 21), -1)
+        cv2.putText(image, label, (x, y - 3), cv2.FONT_ITALIC, 0.8, (255, 255, 255), 2)
+
+
+def get_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser('ONNXRuntime inference script.', add_help=True)
+    parser.add_argument('--model_path', default='yolov5.onnx', type=str, required=True,
+                        help='Path to onnx model.')
+    parser.add_argument('--image', default='bus.jpg', type=str, required=True,
+                        help='Image source to be detected.')
+    parser.add_argument('--class_names', default='coco.names', type=str, required=True,
+                        help='Path of dataset labels.')
+    parser.add_argument('--gpu', action='store_true',
+                        help='To enable inference on GPU.')
+
+    return parser
+
+
+if __name__ == '__main__':
+    parser = get_parser()
+    args = parser.parse_args()
+    print('Command Line Args: ', args)
+
+    with open(args.class_names, 'r', encoding='utf-8') as f:
+        class_names = f.readlines()
+        class_names = [class_name.strip() for class_name in class_names]
+
+    detector = YOLOv5Detector(model_path=args.model_path, gpu=args.gpu)
+
+    image = cv2.imread(args.image)
+    scores, class_ids, boxes = detector(image)
+
+    visualize_detection(image, class_names, scores, class_ids, boxes)
+
+    cv2.imshow('result', image)
+    cv2.waitKey(0)