数据集Pascal VOC2012下载链接

http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar

增强数据集下载

http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/semantic_contours/benchmark.tgz

 要是不能访问，挂个VPN.

用Linux命令解压 tgz 文件，可以使用 Linux 系统自带的 tar 命令。例如，若要解压文件名为 "file.tgz" 的 tgz 文件，可以在终端输入以下命令：

tar -xzvf file.tgz

其中

• x：表示解压
• z：表示使用 gzip 压缩
• v：表示显示详细的解压过程
• f：表示指定要解压的文件

如果想使用增强的VOC数据集，请运行以下命令将增强注释转换为正确的格式。

# --nproc means 8 process for conversion, which could be omitted as well.
python tools/convert_datasets/voc_aug.py data/VOCdevkit data/VOCdevkit/VOCaug --nproc 8

voc_aug.py如下

# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os.path as osp
from functools import partial

import mmcv
import numpy as np
from PIL import Image
from scipy.io import loadmat

AUG_LEN = 10582


def convert_mat(mat_file, in_dir, out_dir):
    data = loadmat(osp.join(in_dir, mat_file))
    mask = data['GTcls'][0]['Segmentation'][0].astype(np.uint8)
    seg_filename = osp.join(out_dir, mat_file.replace('.mat', '.png'))
    Image.fromarray(mask).save(seg_filename, 'PNG')


def generate_aug_list(merged_list, excluded_list):
    return list(set(merged_list) - set(excluded_list))


def parse_args():
    parser = argparse.ArgumentParser(
        description='Convert PASCAL VOC annotations to mmsegmentation format')
    parser.add_argument('devkit_path', help='pascal voc devkit path')
    parser.add_argument('aug_path', help='pascal voc aug path')
    parser.add_argument('-o', '--out_dir', help='output path')
    parser.add_argument(
        '--nproc', default=1, type=int, help='number of process')
    args = parser.parse_args()
    return args


def main():
    args = parse_args()
    devkit_path = args.devkit_path
    aug_path = args.aug_path
    nproc = args.nproc
    if args.out_dir is None:
        out_dir = osp.join(devkit_path, 'VOC2012', 'SegmentationClassAug')
    else:
        out_dir = args.out_dir
    mmcv.mkdir_or_exist(out_dir)
    in_dir = osp.join(aug_path, 'dataset', 'cls')

    mmcv.track_parallel_progress(
        partial(convert_mat, in_dir=in_dir, out_dir=out_dir),
        list(mmcv.scandir(in_dir, suffix='.mat')),
        nproc=nproc)

    full_aug_list = []
    with open(osp.join(aug_path, 'dataset', 'train.txt')) as f:
        full_aug_list += [line.strip() for line in f]
    with open(osp.join(aug_path, 'dataset', 'val.txt')) as f:
        full_aug_list += [line.strip() for line in f]

    with open(
            osp.join(devkit_path, 'VOC2012/ImageSets/Segmentation',
                     'train.txt')) as f:
        ori_train_list = [line.strip() for line in f]
    with open(
            osp.join(devkit_path, 'VOC2012/ImageSets/Segmentation',
                     'val.txt')) as f:
        val_list = [line.strip() for line in f]

    aug_train_list = generate_aug_list(ori_train_list + full_aug_list,
                                       val_list)
    assert len(aug_train_list) == AUG_LEN, 'len(aug_train_list) != {}'.format(
        AUG_LEN)

    with open(
            osp.join(devkit_path, 'VOC2012/ImageSets/Segmentation',
                     'trainaug.txt'), 'w') as f:
        f.writelines(line + '\n' for line in aug_train_list)

    aug_list = generate_aug_list(full_aug_list, ori_train_list + val_list)
    assert len(aug_list) == AUG_LEN - len(
        ori_train_list), 'len(aug_list) != {}'.format(AUG_LEN -
                                                      len(ori_train_list))
    with open(
            osp.join(devkit_path, 'VOC2012/ImageSets/Segmentation', 'aug.txt'),
            'w') as f:
        f.writelines(line + '\n' for line in aug_list)

    print('Done!')


if __name__ == '__main__':
    main()

或者在百度网盘下载：

benchmark_RELEASE.zip_免费高速下载|百度网盘-分享无限制 (baidu.com)

数据集的相关可以参考以下博主的内容

PASCAL VOC 2012数据集及其增强版介绍_pascal voc 2012其增强版 网盘-CSDN博客