deeplabv3

Rethinking atrous convolution for semantic image segmentation

Introduction

Official Repo

Code Snippet

Abstract

In this work, we revisit atrous convolution, a powerful tool to explicitly adjust filter's field-of-view as well as control the resolution of feature responses computed by Deep Convolutional Neural Networks, in the application of semantic image segmentation. To handle the problem of segmenting objects at multiple scales, we design modules which employ atrous convolution in cascade or in parallel to capture multi-scale context by adopting multiple atrous rates. Furthermore, we propose to augment our previously proposed Atrous Spatial Pyramid Pooling module, which probes convolutional features at multiple scales, with image-level features encoding global context and further boost performance. We also elaborate on implementation details and share our experience on training our system. The proposed `DeepLabv3' system significantly improves over our previous DeepLab versions without DenseCRF post-processing and attains comparable performance with other state-of-art models on the PASCAL VOC 2012 semantic image segmentation benchmark.

DeepLabV3 (ArXiv'2017)

@article{chen2017rethinking,
  title={Rethinking atrous convolution for semantic image segmentation},
  author={Chen, Liang-Chieh and Papandreou, George and Schroff, Florian and Adam, Hartwig},
  journal={arXiv preprint arXiv:1706.05587},
  year={2017}
}

Results and models

:::{note} D-8 here corresponding to the output stride 8 setting for DeepLab series. :::

Cityscapes

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	mIoU	mIoU(ms+flip)	config	download
DeepLabV3	R-50-D8	512x1024	40000	6.1	2.57	79.09	80.45	config	model | log
DeepLabV3	R-101-D8	512x1024	40000	9.6	1.92	77.12	79.61	config	model | log
DeepLabV3	R-50-D8	769x769	40000	6.9	1.11	78.58	79.89	config	model | log
DeepLabV3	R-101-D8	769x769	40000	10.9	0.83	79.27	80.11	config	model | log
DeepLabV3	R-18-D8	512x1024	80000	1.7	13.78	76.70	78.27	config	model | log
DeepLabV3	R-50-D8	512x1024	80000	-	-	79.32	80.57	config	model | log
DeepLabV3	R-101-D8	512x1024	80000	-	-	80.20	81.21	config	model | log
DeepLabV3 (FP16)	R-101-D8	512x1024	80000	5.75	3.86	80.48	-	config	model | log
DeepLabV3	R-18-D8	769x769	80000	1.9	5.55	76.60	78.26	config	model | log
DeepLabV3	R-50-D8	769x769	80000	-	-	79.89	81.06	config	model | log
DeepLabV3	R-101-D8	769x769	80000	-	-	79.67	80.81	config	model | log
DeepLabV3	R-101-D16-MG124	512x1024	40000	4.7	- 6.96	76.71	78.63	config	model | log
DeepLabV3	R-101-D16-MG124	512x1024	80000	-	-	78.36	79.84	config	model | log
DeepLabV3	R-18b-D8	512x1024	80000	1.6	13.93	76.26	77.88	config	model | log
DeepLabV3	R-50b-D8	512x1024	80000	6.0	2.74	79.63	80.98	config	model | log
DeepLabV3	R-101b-D8	512x1024	80000	9.5	1.81	80.01	81.21	config	model | log
DeepLabV3	R-18b-D8	769x769	80000	1.8	5.79	76.63	77.51	config	model | log
DeepLabV3	R-50b-D8	769x769	80000	6.8	1.16	78.80	80.27	config	model | log
DeepLabV3	R-101b-D8	769x769	80000	10.7	0.82	79.41	80.73	config	model | log

ADE20K

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	mIoU	mIoU(ms+flip)	config	download
DeepLabV3	R-50-D8	512x512	80000	8.9	14.76	42.42	43.28	config	model | log
DeepLabV3	R-101-D8	512x512	80000	12.4	10.14	44.08	45.19	config	model | log
DeepLabV3	R-50-D8	512x512	160000	-	-	42.66	44.09	config	model | log
DeepLabV3	R-101-D8	512x512	160000	-	-	45.00	46.66	config	model | log

Pascal VOC 2012 + Aug

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	mIoU	mIoU(ms+flip)	config	download
DeepLabV3	R-50-D8	512x512	20000	6.1	13.88	76.17	77.42	config	model | log
DeepLabV3	R-101-D8	512x512	20000	9.6	9.81	78.70	79.95	config	model | log
DeepLabV3	R-50-D8	512x512	40000	-	-	77.68	78.78	config	model | log
DeepLabV3	R-101-D8	512x512	40000	-	-	77.92	79.18	config	model | log

Pascal Context

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	mIoU	mIoU(ms+flip)	config	download
DeepLabV3	R-101-D8	480x480	40000	9.2	7.09	46.55	47.81	config	model | log
DeepLabV3	R-101-D8	480x480	80000	-	-	46.42	47.53	config	model | log

Pascal Context 59

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	mIoU	mIoU(ms+flip)	config	download
DeepLabV3	R-101-D8	480x480	40000	-	-	52.61	54.28	config	model | log
DeepLabV3	R-101-D8	480x480	80000	-	-	52.46	54.09	config	model | log

COCO-Stuff 10k

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	mIoU	mIoU(ms+flip)	config	download
DeepLabV3	R-50-D8	512x512	20000	9.6	10.8	34.66	36.08	config	model | log
DeepLabV3	R-101-D8	512x512	20000	13.2	8.7	37.30	38.42	config	model | log
DeepLabV3	R-50-D8	512x512	40000	-	-	35.73	37.09	config	model | log
DeepLabV3	R-101-D8	512x512	40000	-	-	37.81	38.80	config	model | log

COCO-Stuff 164k

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	mIoU	mIoU(ms+flip)	config	download
DeepLabV3	R-50-D8	512x512	80000	9.6	10.8	39.38	40.03	config	model | log
DeepLabV3	R-101-D8	512x512	80000	13.2	8.7	40.87	41.50	config	model | log
DeepLabV3	R-50-D8	512x512	160000	-	-	41.09	41.69	config	model | log
DeepLabV3	R-101-D8	512x512	160000	-	-	41.82	42.49	config	model | log
DeepLabV3	R-50-D8	512x512	320000	-	-	41.37	42.22	config	model | log
DeepLabV3	R-101-D8	512x512	320000	-	-	42.61	43.42	config	model | log

Note:

• FP16 means Mixed Precision (FP16) is adopted in training.