segformer

SegFormer

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

Introduction

Official Repo

Code Snippet

Abstract

We present SegFormer, a simple, efficient yet powerful semantic segmentation framework which unifies Transformers with lightweight multilayer perception (MLP) decoders. SegFormer has two appealing features: 1) SegFormer comprises a novel hierarchically structured Transformer encoder which outputs multiscale features. It does not need positional encoding, thereby avoiding the interpolation of positional codes which leads to decreased performance when the testing resolution differs from training. 2) SegFormer avoids complex decoders. The proposed MLP decoder aggregates information from different layers, and thus combining both local attention and global attention to render powerful representations. We show that this simple and lightweight design is the key to efficient segmentation on Transformers. We scale our approach up to obtain a series of models from SegFormer-B0 to SegFormer-B5, reaching significantly better performance and efficiency than previous counterparts. For example, SegFormer-B4 achieves 50.3% mIoU on ADE20K with 64M parameters, being 5x smaller and 2.2% better than the previous best method. Our best model, SegFormer-B5, achieves 84.0% mIoU on Cityscapes validation set and shows excellent zero-shot robustness on Cityscapes-C. Code will be released at: this http URL.

Usage

To use other repositories' pre-trained models, it is necessary to convert keys.

We provide a script mit2mmseg.py in the tools directory to convert the key of models from the official repo to MMSegmentation style.

python tools/model_converters/mit2mmseg.py ${PRETRAIN_PATH} ${STORE_PATH}

This script convert model from PRETRAIN_PATH and store the converted model in STORE_PATH.

Results and models

ADE20K

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	Device	mIoU	mIoU(ms+flip)	config	download
Segformer	MIT-B0	512x512	160000	2.1	51.32	1080 Ti	37.41	38.34	config	model | log
Segformer	MIT-B1	512x512	160000	2.6	47.66	TITAN Xp	40.97	42.54	config	model | log
Segformer	MIT-B2	512x512	160000	3.6	30.88	TITAN Xp	45.58	47.03	config	model | log
Segformer	MIT-B3	512x512	160000	4.8	22.11	V100	47.82	48.81	config	model | log
Segformer	MIT-B4	512x512	160000	6.1	15.45	V100	48.46	49.76	config	model | log
Segformer	MIT-B5	512x512	160000	7.2	11.89	V100	49.13	50.22	config	model | log
Segformer	MIT-B5	640x640	160000	11.5	11.30	V100	49.62	50.36	config	model | log

Evaluation with AlignedResize:

Method	Backbone	Crop Size	Lr schd	mIoU	mIoU(ms+flip)
Segformer	MIT-B0	512x512	160000	38.1	38.57
Segformer	MIT-B1	512x512	160000	41.64	42.76
Segformer	MIT-B2	512x512	160000	46.53	47.49
Segformer	MIT-B3	512x512	160000	48.46	49.14
Segformer	MIT-B4	512x512	160000	49.34	50.29
Segformer	MIT-B5	512x512	160000	50.08	50.72
Segformer	MIT-B5	640x640	160000	50.58	50.8

We replace AlignedResize in original implementatiuon to Resize + ResizeToMultiple. If you want to test by using AlignedResize, you can change the dataset pipeline like this:

test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='Resize', scale=(2048, 512), keep_ratio=True),
    # resize image to multiple of 32, improve SegFormer by 0.5-1.0 mIoU.
    dict(type='ResizeToMultiple', size_divisor=32),
    # add loading annotation after ``Resize`` because ground truth
    # does not need to do resize data transform
    dict(type='LoadAnnotations', reduce_zero_label=True),
    dict(type='PackSegInputs')
]

Cityscapes

The lower fps result is caused by the sliding window inference scheme (window size:1024x1024).

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	Device	mIoU	mIoU(ms+flip)	config	download
Segformer	MIT-B0	1024x1024	160000	3.64	4.74	V100	76.54	78.22	config	model | log
Segformer	MIT-B1	1024x1024	160000	4.49	4.3	V100	78.56	79.73	config	model | log
Segformer	MIT-B2	1024x1024	160000	7.42	3.36	V100	81.08	82.18	config	model | log
Segformer	MIT-B3	1024x1024	160000	10.86	2.53	V100	81.94	83.14	config	model | log
Segformer	MIT-B4	1024x1024	160000	15.07	1.88	V100	81.89	83.38	config	model | log
Segformer	MIT-B5	1024x1024	160000	18.00	1.39	V100	82.25	83.48	config	model | log

Citation

@article{xie2021segformer,
  title={SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers},
  author={Xie, Enze and Wang, Wenhai and Yu, Zhiding and Anandkumar, Anima and Alvarez, Jose M and Luo, Ping},
  journal={arXiv preprint arXiv:2105.15203},
  year={2021}
}