diff --git a/projects/BEVFusion/README.md b/projects/BEVFusion/README.md
index 0828bd4f8d..9d5ebd4c52 100644
--- a/projects/BEVFusion/README.md
+++ b/projects/BEVFusion/README.md
@@ -34,7 +34,7 @@ python projects/BEVFusion/setup.py develop
 Run a demo on NuScenes data using [BEVFusion model](https://drive.google.com/file/d/1QkvbYDk4G2d6SZoeJqish13qSyXA4lp3/view?usp=share_link):
 
 ```shell
-python demo/multi_modality_demo.py demo/data/nuscenes/n015-2018-07-24-11-22-45+0800__LIDAR_TOP__1532402927647951.pcd.bin demo/data/nuscenes/ demo/data/nuscenes/n015-2018-07-24-11-22-45+0800.pkl projects/BEVFusion/configs/bevfusion_lidar-cam_voxel0075_second_secfpn_8xb4-cyclic-20e_nus-3d.py ${CHECKPOINT_FILE} --cam-type all --score-thr 0.2 --show
+python projects/BEVFusion/demo/multi_modality_demo.py demo/data/nuscenes/n015-2018-07-24-11-22-45+0800__LIDAR_TOP__1532402927647951.pcd.bin demo/data/nuscenes/ demo/data/nuscenes/n015-2018-07-24-11-22-45+0800.pkl projects/BEVFusion/configs/bevfusion_lidar-cam_voxel0075_second_secfpn_8xb4-cyclic-20e_nus-3d.py ${CHECKPOINT_FILE} --cam-type all --score-thr 0.2 --show
 ```
 
 ### Training commands
diff --git a/projects/BEVFusion/demo/multi_modalit_demo.py b/projects/BEVFusion/demo/multi_modalit_demo.py
new file mode 100644
index 0000000000..9f82867d40
--- /dev/null
+++ b/projects/BEVFusion/demo/multi_modalit_demo.py
@@ -0,0 +1,78 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from argparse import ArgumentParser
+
+import mmcv
+
+from mmdet3d.apis import inference_multi_modality_detector, init_model
+from mmdet3d.registry import VISUALIZERS
+
+
+def parse_args():
+    parser = ArgumentParser()
+    parser.add_argument('pcd', help='Point cloud file')
+    parser.add_argument('img', help='image file')
+    parser.add_argument('ann', help='ann file')
+    parser.add_argument('config', help='Config file')
+    parser.add_argument('checkpoint', help='Checkpoint file')
+    parser.add_argument(
+        '--device', default='cuda:0', help='Device used for inference')
+    parser.add_argument(
+        '--cam-type',
+        type=str,
+        default='CAM_FRONT',
+        help='choose camera type to inference')
+    parser.add_argument(
+        '--score-thr', type=float, default=0.0, help='bbox score threshold')
+    parser.add_argument(
+        '--out-dir', type=str, default='demo', help='dir to save results')
+    parser.add_argument(
+        '--show',
+        action='store_true',
+        help='show online visualization results')
+    parser.add_argument(
+        '--snapshot',
+        action='store_true',
+        help='whether to save online visualization results')
+    args = parser.parse_args()
+    return args
+
+
+def main(args):
+    # build the model from a config file and a checkpoint file
+    model = init_model(args.config, args.checkpoint, device=args.device)
+
+    # init visualizer
+    visualizer = VISUALIZERS.build(model.cfg.visualizer)
+    visualizer.dataset_meta = model.dataset_meta
+
+    # test a single image and point cloud sample
+    result, data = inference_multi_modality_detector(model, args.pcd, args.img,
+                                                     args.ann, args.cam_type)
+    points = data['inputs']['points']
+    if isinstance(result.img_path, list):
+        img = []
+        for img_path in result.img_path:
+            single_img = mmcv.imread(img_path)
+            single_img = mmcv.imconvert(single_img, 'bgr', 'rgb')
+            img.append(single_img)
+    else:
+        img = mmcv.imread(result.img_path)
+        img = mmcv.imconvert(img, 'bgr', 'rgb')
+    data_input = dict(points=points, img=img)
+
+    # show the results
+    visualizer.add_datasample(
+        'result',
+        data_input,
+        data_sample=result,
+        draw_gt=False,
+        show=args.show,
+        wait_time=-1,
+        out_file=args.out_dir,
+        pred_score_thr=args.score_thr,
+        vis_task='multi-modality_det')
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    main(args)