--- a
+++ b/tools/convert_datasets/coco_stuff10k.py
@@ -0,0 +1,306 @@
+import argparse
+import os.path as osp
+import shutil
+from functools import partial
+
+import mmcv
+import numpy as np
+from PIL import Image
+from scipy.io import loadmat
+
+COCO_LEN = 10000
+
+clsID_to_trID = {
+    0: 0,
+    1: 1,
+    2: 2,
+    3: 3,
+    4: 4,
+    5: 5,
+    6: 6,
+    7: 7,
+    8: 8,
+    9: 9,
+    10: 10,
+    11: 11,
+    13: 12,
+    14: 13,
+    15: 14,
+    16: 15,
+    17: 16,
+    18: 17,
+    19: 18,
+    20: 19,
+    21: 20,
+    22: 21,
+    23: 22,
+    24: 23,
+    25: 24,
+    27: 25,
+    28: 26,
+    31: 27,
+    32: 28,
+    33: 29,
+    34: 30,
+    35: 31,
+    36: 32,
+    37: 33,
+    38: 34,
+    39: 35,
+    40: 36,
+    41: 37,
+    42: 38,
+    43: 39,
+    44: 40,
+    46: 41,
+    47: 42,
+    48: 43,
+    49: 44,
+    50: 45,
+    51: 46,
+    52: 47,
+    53: 48,
+    54: 49,
+    55: 50,
+    56: 51,
+    57: 52,
+    58: 53,
+    59: 54,
+    60: 55,
+    61: 56,
+    62: 57,
+    63: 58,
+    64: 59,
+    65: 60,
+    67: 61,
+    70: 62,
+    72: 63,
+    73: 64,
+    74: 65,
+    75: 66,
+    76: 67,
+    77: 68,
+    78: 69,
+    79: 70,
+    80: 71,
+    81: 72,
+    82: 73,
+    84: 74,
+    85: 75,
+    86: 76,
+    87: 77,
+    88: 78,
+    89: 79,
+    90: 80,
+    92: 81,
+    93: 82,
+    94: 83,
+    95: 84,
+    96: 85,
+    97: 86,
+    98: 87,
+    99: 88,
+    100: 89,
+    101: 90,
+    102: 91,
+    103: 92,
+    104: 93,
+    105: 94,
+    106: 95,
+    107: 96,
+    108: 97,
+    109: 98,
+    110: 99,
+    111: 100,
+    112: 101,
+    113: 102,
+    114: 103,
+    115: 104,
+    116: 105,
+    117: 106,
+    118: 107,
+    119: 108,
+    120: 109,
+    121: 110,
+    122: 111,
+    123: 112,
+    124: 113,
+    125: 114,
+    126: 115,
+    127: 116,
+    128: 117,
+    129: 118,
+    130: 119,
+    131: 120,
+    132: 121,
+    133: 122,
+    134: 123,
+    135: 124,
+    136: 125,
+    137: 126,
+    138: 127,
+    139: 128,
+    140: 129,
+    141: 130,
+    142: 131,
+    143: 132,
+    144: 133,
+    145: 134,
+    146: 135,
+    147: 136,
+    148: 137,
+    149: 138,
+    150: 139,
+    151: 140,
+    152: 141,
+    153: 142,
+    154: 143,
+    155: 144,
+    156: 145,
+    157: 146,
+    158: 147,
+    159: 148,
+    160: 149,
+    161: 150,
+    162: 151,
+    163: 152,
+    164: 153,
+    165: 154,
+    166: 155,
+    167: 156,
+    168: 157,
+    169: 158,
+    170: 159,
+    171: 160,
+    172: 161,
+    173: 162,
+    174: 163,
+    175: 164,
+    176: 165,
+    177: 166,
+    178: 167,
+    179: 168,
+    180: 169,
+    181: 170,
+    182: 171
+}
+
+
+def convert_to_trainID(tuple_path, in_img_dir, in_ann_dir, out_img_dir,
+                       out_mask_dir, is_train):
+    imgpath, maskpath = tuple_path
+    shutil.copyfile(
+        osp.join(in_img_dir, imgpath),
+        osp.join(out_img_dir, 'train2014', imgpath) if is_train else osp.join(
+            out_img_dir, 'test2014', imgpath))
+    annotate = loadmat(osp.join(in_ann_dir, maskpath))
+    mask = annotate['S'].astype(np.uint8)
+    mask_copy = mask.copy()
+    for clsID, trID in clsID_to_trID.items():
+        mask_copy[mask == clsID] = trID
+    seg_filename = osp.join(out_mask_dir, 'train2014',
+                            maskpath.split('.')[0] +
+                            '_labelTrainIds.png') if is_train else osp.join(
+                                out_mask_dir, 'test2014',
+                                maskpath.split('.')[0] + '_labelTrainIds.png')
+    Image.fromarray(mask_copy).save(seg_filename, 'PNG')
+
+
+def generate_coco_list(folder):
+    train_list = osp.join(folder, 'imageLists', 'train.txt')
+    test_list = osp.join(folder, 'imageLists', 'test.txt')
+    train_paths = []
+    test_paths = []
+
+    with open(train_list) as f:
+        for filename in f:
+            basename = filename.strip()
+            imgpath = basename + '.jpg'
+            maskpath = basename + '.mat'
+            train_paths.append((imgpath, maskpath))
+
+    with open(test_list) as f:
+        for filename in f:
+            basename = filename.strip()
+            imgpath = basename + '.jpg'
+            maskpath = basename + '.mat'
+            test_paths.append((imgpath, maskpath))
+
+    return train_paths, test_paths
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description=\
+        'Convert COCO Stuff 10k annotations to mmsegmentation format')  # noqa
+    parser.add_argument('coco_path', help='coco stuff path')
+    parser.add_argument('-o', '--out_dir', help='output path')
+    parser.add_argument(
+        '--nproc', default=16, type=int, help='number of process')
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+    coco_path = args.coco_path
+    nproc = args.nproc
+
+    out_dir = args.out_dir or coco_path
+    out_img_dir = osp.join(out_dir, 'images')
+    out_mask_dir = osp.join(out_dir, 'annotations')
+
+    mmcv.mkdir_or_exist(osp.join(out_img_dir, 'train2014'))
+    mmcv.mkdir_or_exist(osp.join(out_img_dir, 'test2014'))
+    mmcv.mkdir_or_exist(osp.join(out_mask_dir, 'train2014'))
+    mmcv.mkdir_or_exist(osp.join(out_mask_dir, 'test2014'))
+
+    train_list, test_list = generate_coco_list(coco_path)
+    assert (len(train_list) +
+            len(test_list)) == COCO_LEN, 'Wrong length of list {} & {}'.format(
+                len(train_list), len(test_list))
+
+    if args.nproc > 1:
+        mmcv.track_parallel_progress(
+            partial(
+                convert_to_trainID,
+                in_img_dir=osp.join(coco_path, 'images'),
+                in_ann_dir=osp.join(coco_path, 'annotations'),
+                out_img_dir=out_img_dir,
+                out_mask_dir=out_mask_dir,
+                is_train=True),
+            train_list,
+            nproc=nproc)
+        mmcv.track_parallel_progress(
+            partial(
+                convert_to_trainID,
+                in_img_dir=osp.join(coco_path, 'images'),
+                in_ann_dir=osp.join(coco_path, 'annotations'),
+                out_img_dir=out_img_dir,
+                out_mask_dir=out_mask_dir,
+                is_train=False),
+            test_list,
+            nproc=nproc)
+    else:
+        mmcv.track_progress(
+            partial(
+                convert_to_trainID,
+                in_img_dir=osp.join(coco_path, 'images'),
+                in_ann_dir=osp.join(coco_path, 'annotations'),
+                out_img_dir=out_img_dir,
+                out_mask_dir=out_mask_dir,
+                is_train=True), train_list)
+        mmcv.track_progress(
+            partial(
+                convert_to_trainID,
+                in_img_dir=osp.join(coco_path, 'images'),
+                in_ann_dir=osp.join(coco_path, 'annotations'),
+                out_img_dir=out_img_dir,
+                out_mask_dir=out_mask_dir,
+                is_train=False), test_list)
+
+    print('Done!')
+
+
+if __name__ == '__main__':
+    main()