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Mapillary Vistas Dataset

First of all, please request the research edition dataset from here. The downloaded file is named as mapillary-vistas-dataset_public_v1.1.zip.

Then simply unzip the file by

unzip mapillary-vistas-dataset_public_v1.1.zip

The folder structure will look like:

Mapillary
├── config.json
├── demo.py
├── Mapillary Vistas Research Edition License.pdf
├── README
├── requirements.txt
├── training
│   ├── images
│   ├── instances
│   ├── labels
│   ├── panoptic
├── validation
│   ├── images
│   ├── instances
│   ├── labels
│   ├── panoptic
├── testing
│   ├── images
│   ├── instances
│   ├── labels
│   ├── panoptic

Note that, the instances, labels and panoptic folders inside testing are empty.

Suppose you store your dataset at ~/username/data/Mapillary, please update the dataset path in config.py,

__C.DATASET.MAPILLARY_DIR = '~/username/data/Mapillary'

Cityscapes Dataset

Download Dataset

First of all, please request the dataset from here. You need multiple files.

- leftImg8bit_trainvaltest.zip
- gtFine_trainvaltest.zip
- leftImg8bit_trainextra.zip
- gtCoarse.zip
- leftImg8bit_sequence.zip    # This file is very large, 324G. You only need it if you want to run sdc_aug experiments.

If you prefer to use command lines (e.g., wget) to download the dataset,

# First step, obtain your login credentials.
Please register an account at https://www.cityscapes-dataset.com/login/.

# Second step, log into cityscapes system, suppose you already have a USERNAME and a PASSWORD.
wget --keep-session-cookies --save-cookies=cookies.txt --post-data 'username=USERNAME&password=PASSWORD&submit=Login' https://www.cityscapes-dataset.com/login/

# Third step, download the zip files you need.
wget -c -t 0 --load-cookies cookies.txt --content-disposition https://www.cityscapes-dataset.com/file-handling/?packageID=3

# The corresponding packageID is listed below,
1  -> gtFine_trainvaltest.zip (241MB)            md5sum: 4237c19de34c8a376e9ba46b495d6f66
2  -> gtCoarse.zip (1.3GB)                       md5sum: 1c7b95c84b1d36cc59a9194d8e5b989f
3  -> leftImg8bit_trainvaltest.zip (11GB)        md5sum: 0a6e97e94b616a514066c9e2adb0c97f
4  -> leftImg8bit_trainextra.zip (44GB)          md5sum: 9167a331a158ce3e8989e166c95d56d4
14 -> leftImg8bit_sequence.zip (324GB)           md5sum: 4348961b135d856c1777f7f1098f7266

Prepare Folder Structure

Now unzip those files, the desired folder structure will look like,

Cityscapes
├── leftImg8bit_trainvaltest
│   ├── leftImg8bit
│   │   ├── train
│   │   │   ├── aachen
│   │   │   │   ├── aachen_000000_000019_leftImg8bit.png
│   │   │   │   ├── aachen_000001_000019_leftImg8bit.png
│   │   │   │   ├── ...
│   │   │   ├── bochum
│   │   │   ├── ...
│   │   ├── val
│   │   ├── test
├── gtFine_trainvaltest
│   ├── gtFine
│   │   ├── train
│   │   │   ├── aachen
│   │   │   │   ├── aachen_000000_000019_gtFine_color.png
│   │   │   │   ├── aachen_000000_000019_gtFine_instanceIds.png
│   │   │   │   ├── aachen_000000_000019_gtFine_labelIds.png
│   │   │   │   ├── aachen_000000_000019_gtFine_polygons.json
│   │   │   │   ├── ...
│   │   │   ├── bochum
│   │   │   ├── ...
│   │   ├── val
│   │   ├── test
├── leftImg8bit_trainextra
│   ├── leftImg8bit
│   │   ├── train_extra
│   │   │   ├── augsburg
│   │   │   ├── bad-honnef
│   │   │   ├── ...
├── gtCoarse
│   ├── gtCoarse
│   │   ├── train
│   │   ├── train_extra
│   │   ├── val
├── leftImg8bit_sequence
│   ├── train
│   ├── val
│   ├── test

CamVid Dataset

Please download and prepare this dataset according to the tutorial. The desired folder structure will look like,

CamVid
├── train
├── trainannot
├── val
├── valannot
├── test
├── testannot

KITTI Dataset

Please download this dataset at the KITTI Semantic Segmentation benchmark webpage.

Now unzip the file, the desired folder structure will look like,

KITTI
├── training
│   ├── image_2
│   ├── instance
│   ├── semantic
├── test
│   ├── image_2

There is no official training/validation split as the dataset only has 200 training samples. We randomly create three splits at here in order to perform cross-validation.