def METHOD_NAME():
    model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz"
    input_url1 = "https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg"
    result_url = "https://bj.bcebos.com/fastdeploy/tests/data/ppyoloe_baseline.pkl"
    fd.download_and_decompress(model_url, "resources")
    fd.download(input_url1, "resources")
    fd.download(result_url, "resources")
    model_path = "resources/ppyoloe_crn_l_300e_coco"
    model_file = os.path.join(model_path, "model.pdmodel")
    params_file = os.path.join(model_path, "model.pdiparams")
    config_file = os.path.join(model_path, "infer_cfg.yml")
    rc.test_option.use_ort_backend()
    model = fd.vision.detection.PPYOLOE(
        model_file, params_file, config_file, runtime_option=rc.test_option)
    # compare diff
    im1 = cv2.imread("./resources/000000014439.jpg")
    for i in range(2):
        result = model.predict(im1)
        with open("resources/ppyoloe_baseline.pkl", "rb") as f:
            boxes, scores, label_ids = pickle.load(f)
        pred_boxes = np.array(result.boxes)
        pred_scores = np.array(result.scores)
        pred_label_ids = np.array(result.label_ids)
        diff_boxes = np.fabs(boxes - pred_boxes)
        diff_scores = np.fabs(scores - pred_scores)
        diff_label_ids = np.fabs(label_ids - pred_label_ids)
        print(diff_boxes.max(), diff_scores.max(), diff_label_ids.max())
        score_threshold = 0.0
        assert diff_boxes[scores > score_threshold].max(
        ) < 1e-04, "There's diff in boxes."
        assert diff_scores[scores > score_threshold].max(
        ) < 1e-04, "There's diff in scores."
        assert diff_label_ids[scores > score_threshold].max(
        ) < 1e-04, "There's diff in label_ids."