diff --git a/tools/analysis_tools/visualization_cam.py b/tools/analysis_tools/visualization_cam.py
new file mode 100644
index 000000000..334de4adf
--- /dev/null
+++ b/tools/analysis_tools/visualization_cam.py
@@ -0,0 +1,134 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+"""Use the pytorch-grad-cam tool to visualize Class Activation Maps (CAM).
+
+requirement: pip install grad-cam
+"""
+
+from argparse import ArgumentParser
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from mmengine.model import revert_sync_batchnorm
+from PIL import Image
+from pytorch_grad_cam import GradCAM, LayerCAM, XGradCAM, GradCAMPlusPlus, EigenCAM, EigenGradCAM
+from pytorch_grad_cam.utils.image import preprocess_image, show_cam_on_image
+
+from mmengine import Config
+from mmseg.apis import inference_model, init_model, show_result_pyplot
+from mmseg.utils import register_all_modules
+
+
+class SemanticSegmentationTarget:
+    """wrap the model.
+
+    requirement: pip install grad-cam
+
+    Args:
+        category (int): Visualization class.
+        mask (ndarray): Mask of class.
+        size (tuple): Image size.
+    """
+
+    def __init__(self, category, mask, size):
+        self.category = category
+        self.mask = torch.from_numpy(mask)
+        self.size = size
+        if torch.cuda.is_available():
+            self.mask = self.mask.cuda()
+
+    def __call__(self, model_output):
+        model_output = torch.unsqueeze(model_output, dim=0)
+        model_output = F.interpolate(
+            model_output, size=self.size, mode='bilinear')
+        model_output = torch.squeeze(model_output, dim=0)
+
+        return (model_output[self.category, :, :] * self.mask).sum()
+
+
+def main():
+    parser = ArgumentParser()
+    parser.add_argument('img', help='Image file')
+    parser.add_argument('config', help='Config file')
+    parser.add_argument('checkpoint', help='Checkpoint file')
+    parser.add_argument(
+        '--out-file',
+        default='prediction.png',
+        help='Path to output prediction file')
+    parser.add_argument(
+        '--cam-file',
+        default='vis_cam.png',
+        help='Path to output cam file')
+    parser.add_argument(
+        '--target-layers',
+        default='backbone.layer4[2]',
+        help='Target layers to visualize CAM')
+    parser.add_argument(
+        '--category-index',
+        default='7',
+        help='Category to visualize CAM')
+    parser.add_argument(
+        '--device',
+        default='cuda:0',
+        help='Device used for inference')
+    args = parser.parse_args()
+
+    # build the model from a config file and a checkpoint file
+    register_all_modules()
+    model = init_model(args.config, args.checkpoint, device=args.device)
+    if args.device == 'cpu':
+        model = revert_sync_batchnorm(model)
+
+    # test a single image
+    result = inference_model(model, args.img)
+
+    # show the results
+    show_result_pyplot(
+        model,
+        args.img,
+        result,
+        draw_gt=False,
+        show=False if args.out_file is not None else True,
+        out_file=args.out_file)
+
+    # result data conversion
+    prediction_data = result.pred_sem_seg.data
+    pre_np_data = prediction_data.cpu().numpy().squeeze(0)
+
+    target_layers = args.target_layers
+    target_layers = [eval(f'model.{target_layers}')]
+
+    category = int(args.category_index)
+    mask_float = np.float32(pre_np_data == category)
+
+    # data processing
+    image = np.array(Image.open(args.img).convert('RGB'))
+    height, width = image.shape[0], image.shape[1]
+    rgb_img = np.float32(image) / 255
+    config = Config.fromfile(args.config)
+    image_mean = config.data_preprocessor['mean']
+    image_std = config.data_preprocessor['std']
+    input_tensor = preprocess_image(
+        rgb_img,
+        mean=[x / 255 for x in image_mean],
+        std=[x / 255 for x in image_std])
+
+    # Grad CAM(Class Activation Maps)
+    # Can also be LayerCAM, XGradCAM, GradCAMPlusPlus, EigenCAM, EigenGradCAM
+    targets = [
+        SemanticSegmentationTarget(category, mask_float,
+                                   (height, width))
+    ]
+    with GradCAM(
+            model=model,
+            target_layers=target_layers,
+            use_cuda=torch.cuda.is_available()) as cam:
+        grayscale_cam = cam(input_tensor=input_tensor, targets=targets)[0, :]
+        cam_image = show_cam_on_image(rgb_img, grayscale_cam, use_rgb=True)
+
+        # save cam file
+        Image.fromarray(cam_image).save(args.cam_file)
+
+
+if __name__ == '__main__':
+    main()