OWOD/projects/Panoptic-DeepLab

Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation

Bowen Cheng, Maxwell D. Collins, Yukun Zhu, Ting Liu, Thomas S. Huang, Hartwig Adam, Liang-Chieh Chen

[arXiv] [BibTeX] [Reference implementation]

Installation

Install Detectron2 following the instructions.

Training

To train a model with 8 GPUs run:

cd /path/to/detectron2/projects/Panoptic-DeepLab
python train_net.py --config-file config/Cityscapes-PanopticSegmentation/panoptic_deeplab_R_52_os16_mg124_poly_90k_bs32_crop_512_1024.yaml --num-gpus 8

Evaluation

Model evaluation can be done similarly:

cd /path/to/detectron2/projects/Panoptic-DeepLab
python train_net.py --config-file config/Cityscapes-PanopticSegmentation/panoptic_deeplab_R_52_os16_mg124_poly_90k_bs32_crop_512_1024.yaml --eval-only MODEL.WEIGHTS /path/to/model_checkpoint

Cityscapes Panoptic Segmentation

Cityscapes models are trained with ImageNet pretraining.

Method	Backbone	Output resolution	PQ	SQ	RQ	mIoU	AP	Memory (M)	model id	download
Panoptic-DeepLab	R50-DC5	1024×2048	58.6	80.9	71.2	75.9	29.8	8668	-	model | metrics
Panoptic-DeepLab	R52-DC5	1024×2048	60.3	81.5	72.9	78.2	33.2	9682		model | metrics

Note:

• R52: a ResNet-50 with its first 7x7 convolution replaced by 3 3x3 convolutions. This modification has been used in most semantic segmentation papers. We pre-train this backbone on ImageNet using the default recipe of pytorch examples.
• DC5 means using dilated convolution in res5.
• We use a smaller training crop size (512x1024) than the original paper (1025x2049), we find using larger crop size (1024x2048) could further improve PQ by 1.5% but also degrades AP by 3%.

Citing Panoptic-DeepLab

If you use Panoptic-DeepLab, please use the following BibTeX entry.

• CVPR 2020 paper:

@inproceedings{cheng2020panoptic,
  title={Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation},
  author={Cheng, Bowen and Collins, Maxwell D and Zhu, Yukun and Liu, Ting and Huang, Thomas S and Adam, Hartwig and Chen, Liang-Chieh},
  booktitle={CVPR},
  year={2020}
}

• ICCV 2019 COCO-Mapillary workshp challenge report:

@inproceedings{cheng2019panoptic,
  title={Panoptic-DeepLab},
  author={Cheng, Bowen and Collins, Maxwell D and Zhu, Yukun and Liu, Ting and Huang, Thomas S and Adam, Hartwig and Chen, Liang-Chieh},
  booktitle={ICCV COCO + Mapillary Joint Recognition Challenge Workshop},
  year={2019}
}