## ** (2022-Jul.-17)** Our paper, code and dataset are now officially released!!! Please check our project page for more details: [**Project Page**](https://xuebinqin.github.io/dis/index.html).<br>
** (2022-Jul.-5)** Our DIS work is now accepted by ECCV 2022, the code and dataset will be released before July 17th, 2022. Please be aware of our updates.
### Download： [Google Drive](https://drive.google.com/file/d/1jOC2zK0GowBvEt03B7dGugCRDVAoeIqq/view?usp=sharing) or [Baidu Pan 提取码：rtgw](https://pan.baidu.com/s/1y6CQJYledfYyEO0C_Gejpw?pwd=rtgw)
### Predicted Maps, [(Google Drive)](https://drive.google.com/file/d/1FMtDLFrL6xVc41eKlLnuZWMBAErnKv0Y/view?usp=sharing), [(Baidu Pan 提取码：ph1d)](https://pan.baidu.com/s/1WUk2RYYpii2xzrvLna9Fsg?pwd=ph1d), of Our IS-Net and Other SOTAs
### (2) Configuring the environment: go to the root ```DIS``` folder and run
```
conda env create -f pytorch18.yml
```
Or you can check the ```requirements.txt``` to configure the dependancies.
### (3) Train:
#### (a) Open ```train_valid_inference_main.py```, set the path of your to-be-inferenced ```train_datasets``` and ```valid_datasets```, e.g., ```valid_datasets=[dataset_vd]```
#### (b) Set the ```hypar["mode"]``` to ```"train"```
#### (c) Create a new folder ```your_model_weights``` in the directory ```saved_models``` and set it as the ```hypar["model_path"] ="../saved_models/your_model_weights"``` and make sure ```hypar["valid_out_dir"]```(line 668) is set to ```""```, otherwise the prediction maps of the validation stage will be saved to that directory, which will slow the training speed down
#### (d) Run
```
python train_valid_inference_main.py
```
### (4) Inference
#### (a). Download the pre-trained weights (for fair academic comparisons only, the optimized model for engineering or common use will be released soon) ```isnet.pth``` from [(Google Drive)](https://drive.google.com/file/d/1KyMpRjewZdyYfxHPYcd-ZbanIXtin0Sn/view?usp=sharing) or [(Baidu Pan 提取码：xbfk)](https://pan.baidu.com/s/1-X2WutiBkWPt-oakuvZ10w?pwd=xbfk) and store ```isnet.pth``` in ```saved_models/IS-Net```
#### (b) Open ```train_valid_inference_main.py```, set the path of your to-be-inferenced ```valid_datasets```, e.g., ```valid_datasets=[dataset_te1, dataset_te2, dataset_te3, dataset_te4]```
#### (c) Set the ```hypar["mode"]``` to ```"valid"```
#### (d) Set the output directory of your predicted maps, e.g., ```hypar["valid_out_dir"] = "../DIS5K-Results-test"```
#### (e) Run
```
python train_valid_inference_main.py
```
### (5) Use of our Human Correction Efforts(HCE) metric, set the ground truth directory ```gt_root``` and the prediction directory ```pred_root```. To reduce the time costs for computing HCE, the skeletion of the DIS5K dataset can be pre-computed and stored in ```gt_ske_root```. If ```gt_ske_root=""```, the HCE code will compute the skeleton online which usually takes a lot for time for large size ground truth. Then, run ```python hce_metric_main.py```. Other metrics are evaluated based on the [SOCToolbox](https://github.com/mczhuge/SOCToolbox).
<br>
## 7. Term of Use
Our code and evaluation metric use Apache License 2.0. The Terms of use for our DIS5K dataset is provided as [DIS5K-Dataset-Terms-of-Use.pdf](DIS5K-Dataset-Terms-of-Use.pdf).
<br>
## Acknowledgements
<br>
We would like to thank Dr. [Ibrahim Almakky](https://scholar.google.co.uk/citations?user=T9MTcK0AAAAJ&hl=en) for his helps in implementing the dataloader cache machanism of loading large-size training samples and Jiayi Zhu for his efforts in re-organizing our code and dataset.
title={Boundary-aware segmentation network for mobile and web applications},
author={Qin, Xuebin and Fan, Deng-Ping and Huang, Chenyang and Diagne, Cyril and Zhang, Zichen and Sant'Anna, Adri{\`a} Cabeza and Suarez, Albert and Jagersand, Martin and Shao, Ling},
