Cityscapes
-
Supported annotations
- Polygons (segmentation task)
-
Supported attributes
- ‘is_crowd’ (boolean, should be defined for labels as
checkbox
-es) Specifies if the annotation label can distinguish between different instances. If False, the annotation id field encodes the instance id.
- ‘is_crowd’ (boolean, should be defined for labels as
Cityscapes export
Downloaded file: a zip archive of the following structure:
.
├── label_color.txt
├── gtFine
│ ├── <subset_name>
│ │ └── <city_name>
│ │ ├── image_0_gtFine_instanceIds.png
│ │ ├── image_0_gtFine_color.png
│ │ ├── image_0_gtFine_labelIds.png
│ │ ├── image_1_gtFine_instanceIds.png
│ │ ├── image_1_gtFine_color.png
│ │ ├── image_1_gtFine_labelIds.png
│ │ ├── ...
└── imgsFine # if saving images was requested
└── leftImg8bit
├── <subset_name>
│ └── <city_name>
│ ├── image_0_leftImg8bit.png
│ ├── image_1_leftImg8bit.png
│ ├── ...
label_color.txt
a file that describes the color for each label
# label_color.txt example
# r g b label_name
0 0 0 background
0 255 0 tree
...
*_gtFine_color.png
class labels encoded by its color.*_gtFine_labelIds.png
class labels are encoded by its index.*_gtFine_instanceIds.png
class and instance labels encoded by an instance ID. The pixel values encode class and the individual instance: the integer part of a division by 1000 of each ID provides class ID, the remainder is the instance ID. If a certain annotation describes multiple instances, then the pixels have the regular ID of that class
Cityscapes annotations import
Uploaded file: a zip archive with the following structure:
.
├── label_color.txt # optional
└── gtFine
└── <city_name>
├── image_0_gtFine_instanceIds.png
├── image_1_gtFine_instanceIds.png
├── ...
Creating task with Cityscapes dataset
Create a task with the labels you need or you can use the labels and colors of the original dataset. To work with the Cityscapes format, you must have a black color label for the background.
Original Cityscapes color map:
[
{"name": "unlabeled", "color": "#000000", "attributes": []},
{"name": "egovehicle", "color": "#000000", "attributes": []},
{"name": "rectificationborder", "color": "#000000", "attributes": []},
{"name": "outofroi", "color": "#000000", "attributes": []},
{"name": "static", "color": "#000000", "attributes": []},
{"name": "dynamic", "color": "#6f4a00", "attributes": []},
{"name": "ground", "color": "#510051", "attributes": []},
{"name": "road", "color": "#804080", "attributes": []},
{"name": "sidewalk", "color": "#f423e8", "attributes": []},
{"name": "parking", "color": "#faaaa0", "attributes": []},
{"name": "railtrack", "color": "#e6968c", "attributes": []},
{"name": "building", "color": "#464646", "attributes": []},
{"name": "wall", "color": "#66669c", "attributes": []},
{"name": "fence", "color": "#be9999", "attributes": []},
{"name": "guardrail", "color": "#b4a5b4", "attributes": []},
{"name": "bridge", "color": "#966464", "attributes": []},
{"name": "tunnel", "color": "#96785a", "attributes": []},
{"name": "pole", "color": "#999999", "attributes": []},
{"name": "polegroup", "color": "#999999", "attributes": []},
{"name": "trafficlight", "color": "#faaa1e", "attributes": []},
{"name": "trafficsign", "color": "#dcdc00", "attributes": []},
{"name": "vegetation", "color": "#6b8e23", "attributes": []},
{"name": "terrain", "color": "#98fb98", "attributes": []},
{"name": "sky", "color": "#4682b4", "attributes": []},
{"name": "person", "color": "#dc143c", "attributes": []},
{"name": "rider", "color": "#ff0000", "attributes": []},
{"name": "car", "color": "#00008e", "attributes": []},
{"name": "truck", "color": "#000046", "attributes": []},
{"name": "bus", "color": "#003c64", "attributes": []},
{"name": "caravan", "color": "#00005a", "attributes": []},
{"name": "trailer", "color": "#00006e", "attributes": []},
{"name": "train", "color": "#005064", "attributes": []},
{"name": "motorcycle", "color": "#0000e6", "attributes": []},
{"name": "bicycle", "color": "#770b20", "attributes": []},
{"name": "licenseplate", "color": "#00000e", "attributes": []}
]
Upload images when creating a task:
images.zip/
├── image_0.jpg
├── image_1.jpg
├── ...
After creating the task, upload the Cityscapes annotations as described in the previous section.