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FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation

Official implementation of FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation.
A Faster, Stronger and Lighter framework for semantic segmentation, achieving the state-of-the-art performance and more than 3x acceleration.

@inproceedings{wu2019fastfcn,
  title     = {FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation},
  author    = {Wu, Huikai and Zhang, Junge and Huang, Kaiqi and Liang, Kongming and Yu Yizhou},
  booktitle = {arXiv preprint arXiv:1903.11816},
  year = {2019}
}

Contact: Hui-Kai Wu (huikaiwu@icloud.com)

Update

2020-04-15: Now support inference on a single image !!!

CUDA_VISIBLE_DEVICES=0,1,2,3 python -m experiments.segmentation.test_single_image --dataset [pcontext|ade20k] \
    --model [encnet|deeplab|psp] --jpu [JPU|JPU_X] \
    --backbone [resnet50|resnet101] [--ms] --resume {MODEL} --input-path {INPUT} --save-path {OUTPUT}

2020-04-15: New joint upsampling module is now available !!!

--jpu [JPU|JPU_X]: JPU is the original module in the arXiv paper; JPU_X is a pyramid version of JPU.

2020-02-20: FastFCN can now run on every OS with PyTorch>=1.1.0 and Python==3.*.*

Replace all C/C++ extensions with pure python extensions.

Version

Original code, producing the results reported in the arXiv paper. [branch:v1.0.0]
Pure PyTorch code, with torch.nn.DistributedDataParallel and torch.nn.SyncBatchNorm. [branch:latest]
Pure Python code. [branch:master]

Overview

Framework

Joint Pyramid Upsampling (JPU)

Install

PyTorch >= 1.1.0 (Note: The code is test in the environment with python=3.6, cuda=9.0)

Download FastFCN

git clone https://github.com/wuhuikai/FastFCN.git
cd FastFCN

Install Requirements
```
nose
tqdm
scipy
cython
requests
```

Train and Test

PContext

python -m scripts.prepare_pcontext

Method	Backbone	mIoU	FPS	Model	Scripts
EncNet	ResNet-50	49.91	18.77
EncNet+JPU (ours)	ResNet-50	51.05	37.56	GoogleDrive	bash
PSP	ResNet-50	50.58	18.08
PSP+JPU (ours)	ResNet-50	50.89	28.48	GoogleDrive	bash
DeepLabV3	ResNet-50	49.19	15.99
DeepLabV3+JPU (ours)	ResNet-50	50.07	20.67	GoogleDrive	bash
EncNet	ResNet-101	52.60 (MS)	10.51
EncNet+JPU (ours)	ResNet-101	54.03 (MS)	32.02	GoogleDrive	bash

ADE20K

python -m scripts.prepare_ade20k

Training Set

Method	Backbone	mIoU (MS)	Model	Scripts
EncNet	ResNet-50	41.11
EncNet+JPU (ours)	ResNet-50	42.75	GoogleDrive	bash
EncNet	ResNet-101	44.65
EncNet+JPU (ours)	ResNet-101	44.34	GoogleDrive	bash

Training Set + Val Set

Method	Backbone	FinalScore (MS)	Model	Scripts
EncNet+JPU (ours)	ResNet-50		GoogleDrive	bash
EncNet	ResNet-101	55.67
EncNet+JPU (ours)	ResNet-101	55.84	GoogleDrive	bash

Note: EncNet (ResNet-101) is trained with crop_size=576, while EncNet+JPU (ResNet-101) is trained with crop_size=480 for fitting 4 images into a 12G GPU.

Visual Results

Dataset	Input	GT	EncNet	Ours
PContext
ADE20K

More Visual Results

Acknowledgement

Code borrows heavily from PyTorch-Encoding.