ANN
This repository is for Asymmetric Non-local Neural Networks for Semantic Segmentation (to appear in ICCV 2019),
by Zhen Zhu, Mengde Xu, Song Bai, Tengteng Huang and Xiang Bai.
The source code is in preparing. We will release as soon as possible.
If you find our paper useful in your research, please consider citing:
@inproceedings{annn, author = {Zhen Zhu and Mengde Xu and Song Bai and Tengteng Huang and Xiang Bai}, title = {Asymmetric Non-local Neural Networks for Semantic Segmentation}, booktitle={International Conference on Computer Vision}, year = {2019}, url = {http://arxiv.org/abs/1908.07678}, }
Fig.1 Model Architecture
In this work, we present Asymmetric Non-local Neural Network to semantic segmentation for acquiring long range dependency efficiently and effectively. The whole network is shown in Fig. 1. It can fuse features between different level under a sufficient consideration of inter long range dependencies with AFNB and refine features in the same level involving the inner long range dependencies with APNB.
Preparation
python==3.6.8 gcc==5.4.0 cuda==9.2
pip install -r requirements.txt cd ext bash make.sh
Download dataset and pre-process it with scripts in datasets/seg/preprocess
.
Take Cityscapes for example.
cd datasets/seg/preprocess/cityscapes
bash cityscapes_seg_generator.sh
Add dataset path where you save the preprocessed dataset to data.conf
or directly modify the data path in scripts.
Download model pretrained on imagenet and save it in ./pretrained_models
.
Install required package
Prepare running env:
Download the leftImg8bit.zip and gtFine.zip from cityscapes-dataset.com and extract those.
Modify the variable ORI_ROOT_DIR
in cityscapes_seg_generator.sh
to where you save the extracted dataset.
run the preprocessing program.
Train
Take Cityscapes for example.
For training only on train set:
# you can change 'tag' to anything you want bash scripts/seg/cityscapes/run_fs_annn_cityscapes_seg.sh train tag
For training on both train set and validation set:
bash scripts/seg/cityscapes/run_fs_annn_cityscapes_seg_test.sh train tag
Validation
Download trained model to checkpoints/seg/cityscapes/fs_annn_cityscapes_segohem_latest.pth
and test on validation set with single scale.
bash scripts/seg/cityscapes/run_fs_annn_cityscapes_seg.sh val ohem
The final output should be:
classes IoU nIoU -------------------------------- road : 0.984 nan sidewalk : 0.863 nan building : 0.930 nan wall : 0.521 nan fence : 0.629 nan pole : 0.667 nan traffic light : 0.737 nan traffic sign : 0.816 nan vegetation : 0.927 nan terrain : 0.648 nan sky : 0.948 nan person : 0.838 0.000 rider : 0.663 0.000 car : 0.958 0.000 truck : 0.830 0.000 bus : 0.894 0.000 train : 0.858 0.000 motorcycle : 0.669 0.000 bicycle : 0.796 0.000 -------------------------------- Score Average : 0.799 0.000 -------------------------------- categories IoU nIoU -------------------------------- flat : 0.987 nan construction : 0.936 nan object : 0.735 nan nature : 0.929 nan sky : 0.948 nan human : 0.849 0.000 vehicle : 0.945 0.000 -------------------------------- Score Average : 0.904 0.000 --------------------------------
Test on test set
Download trained model to checkpoints/seg/cityscapes/fs_annn_cityscapes_segohem_latest.pth
and test on test set with multiple scales.
bash scripts/seg/cityscapes/run_fs_annn_cityscapes_seg_test.sh test ohem cd results/seg/cityscapes/fs_annn_cityscapes_segohem/test/label zip ../res.zip * # submit the result on cityscapes official web page.
Efficiency Statistics
For details, please refer to ./efficiency_statics/readme.md
.
Tab.1 Comparisons on the test set of Cityscapes with the state-of-the-art methods.(multi scale testing)
Method | Backbone | mIOU |
---|---|---|
DeepLab-V2 | ResNet-101 | 70.4 |
RefineNet | ResNet-101 | 73.6 |
GCN | ResNet-101 | 76.9 |
DUC | ResNet-101 | 77.6 |
SAC | ResNet-101 | 78.1 |
ResNet-38 | ResNet-101 | 78.4 |
PSPNet | ResNet-101 | 78.4 |
BiSeNet | ResNet-101 | 78.9 |
AAF | ResNet-101 | 79.1 |
DFN | ResNet-101 | 79.3 |
PSANet | ResNet-101 | 79.3 |
DenseASPP | DenseNet-101 | 80.1 |
Ours | ResNet-101 | 81.3 |
Tab.2 Comparisons on the validation set of PASCAL Context with the state-of-the-art methods.(multi scale testing)
Method | Backbone | mIOU |
---|---|---|
FCN-8s | - | 37.8 |
Piecewise | - | 43.3 |
DeepLab-v2 | ResNet-101 | 45.7 |
RefineNet | ResNet-152 | 47.3 |
PSPNet | Resnet-101 | 47.8 |
CCL | ResNet-101 | 51.6 |
EncNet | ResNet-101 | 51.7 |
Ours | ResNet-101 | 52.8 |
Tab.3 Comparisons on the validation set of ADE20k with the state-of-the-art methods.(multi scale testing)
Method | Backbone | mIOU |
---|---|---|
RefineNet | ResNet-152 | 40.70 |
UperNet | ResNet-101 | 42.65 |
DSSPN | ResNet-101 | 43.68 |
PSANet | ResNet-101 | 43.77 |
SAC | ResNet-101 | 44.30 |
EncNet | ResNet-101 | 44.65 |
PSPNet | ResNet-101 | 43.29 |
PSPNet | ResNet-269 | 44.94 |
Ours | ResNet-101 | 45.24 |
We genuinely thank Ansheng You for his kind help and suggestions throughout our work. We also recommend others to implement computer vision algorithm with his framework torchcv.
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