[Project] [Paper] [arXiv] [Home]

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 ([email protected])

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.

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

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

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

3. Install Requirements

   nose
   tqdm
   scipy
   cython
   requests
   

python -m scripts.prepare_pcontext

python -m scripts.prepare_ade20k

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.

Dataset	Input	GT	EncNet	Ours
PContext
ADE20K

Code borrows heavily from PyTorch-Encoding.