This project aims at providing a fast, modular reference implementation for semantic segmentation models using PyTorch.
pip3 install torch torchvision
pip3 install easydict
sudo apt-get install ninja-build
pip3 install tqdm
v0.1.1 (05/14/2019)
SS:Single Scale MSF:Multi-scale + Flip
Methods | Backbone | TrainSet | EvalSet | Mean IoU(ss) | Mean IoU(msf) | Model |
---|---|---|---|---|---|---|
FCN-32s | R101_v1c | train_aug | val | 71.26 | - | |
DFN(paper) | R101_v1c | train_aug | val | 79.67 | 80.6* | |
DFN(ours) | R101_v1c | train_aug | val | 79.40 | 81.40 | GoogleDrive |
80.6: this result reported in paper is further finetuned on train* dataset.
Methods | Backbone | OHEM | TrainSet | EvalSet | Mean IoU(ss) | Mean IoU(msf) | Model |
---|---|---|---|---|---|---|---|
DFN(paper) | R101_v1c | ✗ | train_fine | val | 78.5 | 79.3 | |
DFN(ours) | R101_v1c | ✗ | train_fine | val | 79.09 | 80.41 | GoogleDrive |
DFN(ours) | R101_v1c | ✓ | train_fine | val | 79.16 | 80.53 | GoogleDrive |
BiSeNet(paper) | R101_v1c | ✓ | train_fine | val | - | 80.3 | |
BiSeNet(ours) | R101_v1c | ✓ | train_fine | val | 79.09 | 80.39 | GoogleDrive |
BiSeNet(paper) | R18 | ✓ | train_fine | val | 76.21 | 78.57 | |
BiSeNet(ours) | R18 | ✓ | train_fine | val | 76.28 | 78.00 | GoogleDrive |
BiSeNet(paper) | X39 | ✓ | train_fine | val | 70.1 | 72 | |
BiSeNet(ours)* | X39 | ✓ | train_fine | val | 70.32 | 72.06 | GoogleDrive |
Methods | Backbone | OHEM | TrainSet | EvalSet | Mean IoU | Model |
---|---|---|---|---|---|---|
BiSeNet(paper) | R18 | ✓ | train_fine | val | 74.8 | |
BiSeNet(ours) | R18 | ✓ | train_fine | val | 74.83 | GoogleDrive |
BiSeNet(paper) | X39 | ✓ | train_fine | val | 69 | |
BiSeNet(ours)* | X39 | ✓ | train_fine | val | 68.51 | GoogleDrive |
BiSeNet(ours)*: because we didn't pre-train the Xception39 model on ImageNet in PyTorch, we train this experiment from scratch. We will release the pre-trained Xception39 model in PyTorch and the corresponding experiment.
Methods | Backbone | TrainSet | EvalSet | Mean IoU(ss) | Accuracy(ss) | Model |
---|---|---|---|---|---|---|
PSPNet(paper) | R50_v1c | train | val | 41.68 | 80.04 | |
PSPNet(ours) | R50_v1c | train | val | 41.65 | 79.74 | GoogleDrive |
PSPNet(paper) | R101_v1c | train | val | 41.96 | 80.64 | |
PSPNet(ours) | R101_v1c | train | val | 42.89 | 80.55 | GoogleDrive |
PSANet(paper) | R50_v1c | train | val | 41.92 | 80.17 | |
PSANet(ours)* | R50_v1c | train | val | 41.67 | 80.09 | GoogleDrive |
PSANet(paper) | R101_v1c | train | val | 42.75 | 80.71 | |
PSANet(ours) | R101_v1c | train | val | 43.04 | 80.56 | GoogleDrive |
PSANet(ours)*: The original PSANet in the paper constructs the attention map with over-parameters, while we only predict the attention map with the same size of the feature map. The performance is almost similar to the original one.
train.txt
, val.txt
, test.txt
tab
)
path-of-the-image path-of-the-groundtruth
config.py
according to your requirementsWe use the official torch.distributed.launch
in order to launch multi-gpu training.
This utility function from PyTorch spawns as many Python processes as the number
of GPUs we want to use, and each Python process will only use a single GPU.
For each experiment, you can just run this script:
export NGPUS=8
python -m torch.distributed.launch --nproc_per_node=$NGPUS train.py
In the evaluator, we have implemented the multi-gpu inference base on the multi-process. In the inference phase, the function will spawns as many Python processes as the number of GPUs we want to use, and each Python process will handle a subset of the whole evaluation dataset on a single GPU.
python3 eval.py
usage: -e epoch_idx -d device_idx [--verbose ]
[--show_image] [--save_path Pred_Save_Path]
This project is under active development. So things that are currently working might break in a future release. However, feel free to open issue if you get stuck anywhere.
The following are BibTeX references. The BibTeX entry requires the url LaTeX package.
Please consider citing this project in your publications if it helps your research.
@misc{torchseg2019,
author = {Yu, Changqian},
title = {TorchSeg},
howpublished = {\url{https://github.com/ycszen/TorchSeg}},
year = {2019}
}
Please consider citing the DFN in your publications if it helps your research.
@inproceedings{yu2018dfn,
title={Learning a Discriminative Feature Network for Semantic Segmentation},
author={Yu, Changqian and Wang, Jingbo and Peng, Chao and Gao, Changxin and Yu, Gang and Sang, Nong},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2018}
}
Please consider citing the BiSeNet in your publications if it helps your research.
@inproceedings{yu2018bisenet,
title={Bisenet: Bilateral segmentation network for real-time semantic segmentation},
author={Yu, Changqian and Wang, Jingbo and Peng, Chao and Gao, Changxin and Yu, Gang and Sang, Nong},
booktitle={European Conference on Computer Vision},
pages={334--349},
year={2018},
organization={Springer}
}
Furnace means the Alchemical Furnace. We all are the Alchemist, so I hope everyone can have a good alchemical furnace to practice the Alchemy. Hope you can be a excellent alchemist.