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cityscapes

README.md

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}
}