mmsegmentation/configs/segnext

• SegNeXt
• • Introduction
  • Abstract
  • Results and models
  • • ADE20K

SegNeXt

SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation

Introduction

Official Repo

Code Snippet

Abstract

We present SegNeXt, a simple convolutional network architecture for semantic segmentation. Recent transformer-based models have dominated the field of semantic segmentation due to the efficiency of self-attention in encoding spatial information. In this paper, we show that convolutional attention is a more efficient and effective way to encode contextual information than the self-attention mechanism in transformers. By re-examining the characteristics owned by successful segmentation models, we discover several key components leading to the performance improvement of segmentation models. This motivates us to design a novel convolutional attention network that uses cheap convolutional operations. Without bells and whistles, our SegNeXt significantly improves the performance of previous state-of-the-art methods on popular benchmarks, including ADE20K, Cityscapes, COCO-Stuff, Pascal VOC, Pascal Context, and iSAID. Notably, SegNeXt outperforms EfficientNet-L2 w/ NAS-FPN and achieves 90.6% mIoU on the Pascal VOC 2012 test leaderboard using only 1/10 parameters of it. On average, SegNeXt achieves about 2.0% mIoU improvements compared to the state-of-the-art methods on the ADE20K datasets with the same or fewer computations. Code is available at this https URL (Jittor) and this https URL (Pytorch).

@article{guo2022segnext,
  title={SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation},
  author={Guo, Meng-Hao and Lu, Cheng-Ze and Hou, Qibin and Liu, Zhengning and Cheng, Ming-Ming and Hu, Shi-Min},
  journal={arXiv preprint arXiv:2209.08575},
  year={2022}
}

Results and models

ADE20K

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	mIoU	mIoU(ms+flip)	config	download
SegNeXt	MSCAN-T	512x512	160000	17.88	52.38	41.50	42.59	config	model | log
SegNeXt	MSCAN-S	512x512	160000	21.47	42.27	44.16	45.81	config	model | log
SegNeXt	MSCAN-B	512x512	160000	31.03	35.15	48.03	49.68	config	model | log
SegNeXt	MSCAN-L	512x512	160000	43.32	22.91	50.99	52.10	config	model | log

Note:

• When we integrated SegNeXt into MMSegmentation, we modified some layers' names to make them more precise and concise without changing the model architecture. Therefore, the keys of pre-trained weights are different from the original weights, but don't worry about these changes. we have converted them and uploaded the checkpoints, you might find URL of pre-trained checkpoints in config files and can use them directly for training.

• The total batch size is 16. We trained for SegNeXt with a single GPU as the performance degrades significantly when usingSyncBN (mainly in OverlapPatchEmbed modules of MSCAN) of PyTorch 1.9.

• There will be subtle differences when model testing as Non-negative Matrix Factorization (NMF) in LightHamHead will be initialized randomly. To control this randomness, please set the random seed when model testing. You can modify ./tools/test.py like:

def main():
    from mmseg.apis import set_random_seed
    random_seed = xxx # set random seed recorded in training log
    set_random_seed(random_seed, deterministic=False)
    ...

• This model performance is sensitive to the seed values used, please refer to the log file for the specific settings of the seed. If you choose a different seed, the results might differ from the table results. Take SegNeXt Large for example, its results range from 49.60 to 51.0.