def METHOD_NAME(args):
    yolo = YOLO(**{'model_path': args.model_path,
                   'anchors_path': args.anchors,
                   'classes_path': args.yolo_classes_path,
                   'score': args.score,
                   'gpu_num': args.gpu_num,
                   'model_image_size': (416, 416),
                   })
    if not os.path.exists(args.result_path):
        os.mkdir(args.result_path)
    if args.mode.lower() == 'detect':
        img_list = list(Path(args.input_path).iterdir())
        elapsed_t = 0
        img_cnt = 0
        for img_path in img_list:
            pred, _, t = detect_logo_only(str(img_path), yolo,
                                 save_img=args.save_img, 
                                 save_img_path=args.result_path,
                                 crop=args.crop)
            '''
            for p in pred:
                x1 = p[0]
                y1 = p[1]
                x2 = p[2]
                y2 = p[3]
                roi = img[y1:y2, x1:x2]
            '''
            if t == 0:
                continue
            elapsed_t += t
            img_cnt += 1
        print('\nDetecting Logos from images completed! It tooks {:.3f} FPS'.format(img_cnt/elapsed_t))
    elif args.mode.lower() == 'recog':
        model_preproc, params = initialize(yolo, args.recog_model,
                                           args.DB_path)
        img_list = list(Path(args.input_path).iterdir())
        elapsed_t = 0
        img_cnt = 0
        for img_path in img_list:
            _, _, t = detect_and_match(str(img_path), model_preproc, params,
                             save_img=args.save_img, save_img_path=args.result_path)
            if t == 0:
                continue
            elapsed_t += t
            img_cnt += 1
        print('\nDetecting and Recognizing Logos from images completed! It tooks {:.3f} FPS'.format(img_cnt/elapsed_t))
    else: # args.model == 'DB'
        construct_DB(args.DB_list, args.recog_model, args.DB_path)
    print("Done...!")