code_test/configs/cityscapes

• Cityscapes Dataset
• • Common settings
  • • Faster R-CNN
    • Mask R-CNN

Cityscapes Dataset

@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}
}

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