--- a
+++ b/tools/browse_dataset.py
@@ -0,0 +1,167 @@
+import argparse
+import os
+import warnings
+from pathlib import Path
+
+import mmcv
+import numpy as np
+from mmcv import Config
+
+from mmseg.datasets.builder import build_dataset
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Browse a dataset')
+    parser.add_argument('config', help='train config file path')
+    parser.add_argument(
+        '--show-origin',
+        default=False,
+        action='store_true',
+        help='if True, omit all augmentation in pipeline,'
+        ' show origin image and seg map')
+    parser.add_argument(
+        '--skip-type',
+        type=str,
+        nargs='+',
+        default=['DefaultFormatBundle', 'Normalize', 'Collect'],
+        help='skip some useless pipeline，if `show-origin` is true, '
+        'all pipeline except `Load` will be skipped')
+    parser.add_argument(
+        '--output-dir',
+        default='./output',
+        type=str,
+        help='If there is no display interface, you can save it')
+    parser.add_argument('--show', default=False, action='store_true')
+    parser.add_argument(
+        '--show-interval',
+        type=int,
+        default=999,
+        help='the interval of show (ms)')
+    parser.add_argument(
+        '--opacity',
+        type=float,
+        default=0.5,
+        help='the opacity of semantic map')
+    args = parser.parse_args()
+    return args
+
+
+def imshow_semantic(img,
+                    seg,
+                    class_names,
+                    palette=None,
+                    win_name='',
+                    show=False,
+                    wait_time=0,
+                    out_file=None,
+                    opacity=0.5):
+    """Draw `result` over `img`.
+
+    Args:
+        img (str or Tensor): The image to be displayed.
+        seg (Tensor): The semantic segmentation results to draw over
+            `img`.
+        class_names (list[str]): Names of each classes.
+        palette (list[list[int]]] | np.ndarray | None): The palette of
+            segmentation map. If None is given, random palette will be
+            generated. Default: None
+        win_name (str): The window name.
+        wait_time (int): Value of waitKey param.
+            Default: 0.
+        show (bool): Whether to show the image.
+            Default: False.
+        out_file (str or None): The filename to write the image.
+            Default: None.
+        opacity(float): Opacity of painted segmentation map.
+            Default 0.5.
+            Must be in (0, 1] range.
+    Returns:
+        img (Tensor): Only if not `show` or `out_file`
+    """
+    img = mmcv.imread(img)
+    img = img.copy()
+    if palette is None:
+        palette = np.random.randint(0, 255, size=(len(class_names), 3))
+    palette = np.array(palette)
+    assert palette.shape[0] == len(class_names)
+    assert palette.shape[1] == 3
+    assert len(palette.shape) == 2
+    assert 0 < opacity <= 1.0
+    color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)
+    for label, color in enumerate(palette):
+        color_seg[seg == label, :] = color
+    # convert to BGR
+    color_seg = color_seg[..., ::-1]
+
+    img = img * (1 - opacity) + color_seg * opacity
+    img = img.astype(np.uint8)
+    # if out_file specified, do not show image in window
+    if out_file is not None:
+        show = False
+
+    if show:
+        mmcv.imshow(img, win_name, wait_time)
+    if out_file is not None:
+        mmcv.imwrite(img, out_file)
+
+    if not (show or out_file):
+        warnings.warn('show==False and out_file is not specified, only '
+                      'result image will be returned')
+        return img
+
+
+def _retrieve_data_cfg(_data_cfg, skip_type, show_origin):
+    if show_origin is True:
+        # only keep pipeline of Loading data and ann
+        _data_cfg['pipeline'] = [
+            x for x in _data_cfg.pipeline if 'Load' in x['type']
+        ]
+    else:
+        _data_cfg['pipeline'] = [
+            x for x in _data_cfg.pipeline if x['type'] not in skip_type
+        ]
+
+
+def retrieve_data_cfg(config_path, skip_type, show_origin=False):
+    cfg = Config.fromfile(config_path)
+    train_data_cfg = cfg.data.train
+    if isinstance(train_data_cfg, list):
+        for _data_cfg in train_data_cfg:
+            if 'pipeline' in _data_cfg:
+                _retrieve_data_cfg(_data_cfg, skip_type, show_origin)
+            elif 'dataset' in _data_cfg:
+                _retrieve_data_cfg(_data_cfg['dataset'], skip_type,
+                                   show_origin)
+            else:
+                raise ValueError
+    elif 'dataset' in train_data_cfg:
+        _retrieve_data_cfg(train_data_cfg['dataset'], skip_type, show_origin)
+    else:
+        _retrieve_data_cfg(train_data_cfg, skip_type, show_origin)
+    return cfg
+
+
+def main():
+    args = parse_args()
+    cfg = retrieve_data_cfg(args.config, args.skip_type, args.show_origin)
+    dataset = build_dataset(cfg.data.train)
+    progress_bar = mmcv.ProgressBar(len(dataset))
+    for item in dataset:
+        filename = os.path.join(args.output_dir,
+                                Path(item['filename']).name
+                                ) if args.output_dir is not None else None
+        imshow_semantic(
+            item['img'],
+            item['gt_semantic_seg'],
+            dataset.CLASSES,
+            dataset.PALETTE,
+            show=args.show,
+            wait_time=args.show_interval,
+            out_file=filename,
+            opacity=args.opacity,
+        )
+        progress_bar.update()
+
+
+if __name__ == '__main__':
+    main()