Cityscapes
The Cityscapes Dataset for Semantic Urban Scene Understanding
Abstract
Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications. Object detection has benefited enormously from large-scale datasets, especially in the context of deep learning. For semantic urban scene understanding, however, no current dataset adequately captures the complexity of real-world urban scenes.
To address this, we introduce Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling. Cityscapes is comprised of a large, diverse set of stereo video sequences recorded in streets from 50 different cities. 5000 of these images have high quality pixel-level annotations; 20000 additional images have coarse annotations to enable methods that leverage large volumes of weakly-labeled data. Crucially, our effort exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity. Our accompanying empirical study provides an in-depth analysis of the dataset characteristics, as well as a performance evaluation of several state-of-the-art approaches based on our benchmark.
Common settings
- All baselines were trained using 8 GPU with a batch size of 8 (1 images per GPU) using the linear scaling rule to scale the learning rate.
- All models were trained on
cityscapes_train, and tested on cityscapes_val.
- 1x training schedule indicates 64 epochs which corresponds to slightly less than the 24k iterations reported in the original schedule from the Mask R-CNN paper
- COCO pre-trained weights are used to initialize.
- A conversion script is provided to convert Cityscapes into COCO format. Please refer to install.md for details.
CityscapesDataset implemented three evaluation methods. bbox and segm are standard COCO bbox/mask AP. cityscapes is the cityscapes dataset official evaluation, which may be slightly higher than COCO.
Faster R-CNN
| Backbone |
Style |
Lr schd |
Scale |
Mem (GB) |
Inf time (fps) |
box AP |
Config |
Download |
| R-50-FPN |
pytorch |
1x |
800-1024 |
5.2 |
- |
40.3 |
config |
model | log |
Mask R-CNN
| Backbone |
Style |
Lr schd |
Scale |
Mem (GB) |
Inf time (fps) |
box AP |
mask AP |
Config |
Download |
| R-50-FPN |
pytorch |
1x |
800-1024 |
5.3 |
- |
40.9 |
36.4 |
config |
model | log |
Citation
@inproceedings{Cordts2016Cityscapes,
title={The Cityscapes Dataset for Semantic Urban Scene Understanding},
author={Cordts, Marius and Omran, Mohamed and Ramos, Sebastian and Rehfeld, Timo and Enzweiler, Markus and Benenson, Rodrigo and Franke, Uwe and Roth, Stefan and Schiele, Bernt},
booktitle={Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2016}
}
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