# DeepLab in Detectron2
In this repository, we implement DeepLabV3 and DeepLabV3+ in Detectron2.
## Installation
Install Detectron2 following [the instructions](https://detectron2.readthedocs.io/tutorials/install.html).
## Training
To train a model with 8 GPUs run:
```bash
cd /path/to/detectron2/projects/DeepLab
python train_net.py --config-file configs/Cityscapes-SemanticSegmentation/deeplab_v3_plus_R_103_os16_mg124_poly_90k_bs16.yaml --num-gpus 8
```
## Evaluation
Model evaluation can be done similarly:
```bash
cd /path/to/detectron2/projects/DeepLab
python train_net.py --config-file configs/Cityscapes-SemanticSegmentation/deeplab_v3_plus_R_103_os16_mg124_poly_90k_bs16.yaml --eval-only MODEL.WEIGHTS /path/to/model_checkpoint
```
## Cityscapes Semantic Segmentation
Cityscapes models are trained with ImageNet pretraining.
| Method |
Backbone |
Output
resolution |
mIoU |
model id |
download |
| DeepLabV3 |
R101-DC5 |
1024×2048 |
76.7 |
- |
- | - |
| DeepLabV3 |
R103-DC5 |
1024×2048 |
78.5 |
28041665 |
model | metrics |
| DeepLabV3+ |
R101-DC5 |
1024×2048 |
78.1 |
- |
- | - |
| DeepLabV3+ |
R103-DC5 |
1024×2048 |
80.0 |
28054032 |
model | metrics |
Note:
- [R103](https://dl.fbaipublicfiles.com/detectron2/DeepLab/R-103.pkl): a ResNet-101 with its first 7x7 convolution replaced by 3 3x3 convolutions.
This modification has been used in most semantic segmentation papers. We pre-train this backbone on ImageNet using the default recipe of [pytorch examples](https://github.com/pytorch/examples/tree/master/imagenet).
- DC5 means using dilated convolution in `res5`.
## Citing DeepLab
If you use DeepLab, please use the following BibTeX entry.
* DeepLabv3+:
```
@inproceedings{deeplabv3plus2018,
title={Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation},
author={Liang-Chieh Chen and Yukun Zhu and George Papandreou and Florian Schroff and Hartwig Adam},
booktitle={ECCV},
year={2018}
}
```
* DeepLabv3:
```
@article{deeplabv32018,
title={Rethinking atrous convolution for semantic image segmentation},
author={Chen, Liang-Chieh and Papandreou, George and Schroff, Florian and Adam, Hartwig},
journal={arXiv:1706.05587},
year={2017}
}
```