## [Highly Accurate Dichotomous Image Segmentation （ECCV 2022）](https://arxiv.org/pdf/2203.03041.pdf) [Xuebin Qin](https://xuebinqin.github.io/), [Hang Dai](https://scholar.google.co.uk/citations?user=6yvjpQQAAAAJ&hl=en), [Xiaobin Hu](https://scholar.google.de/citations?user=3lMuodUAAAAJ&hl=en), [Deng-Ping Fan*](https://dengpingfan.github.io/), [Ling Shao](https://scholar.google.com/citations?user=z84rLjoAAAAJ&hl=en), [Luc Van Gool](https://scholar.google.com/citations?user=TwMib_QAAAAJ&hl=en).
## This is the official repo for our newly formulated DIS task: [**Project Page**](https://xuebinqin.github.io/dis/index.html), [**Arxiv**](https://arxiv.org/pdf/2203.03041.pdf).
## Updates !!!
## ** (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).
** (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.
## 1. [Our DIS5K Dataset V1.0 (Version Alias: DIS5K Sailship)](https://xuebinqin.github.io/dis/index.html)
### 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)   
## 2. APPLICATIONS of Our DIS5K Dataset
### 3D Modeling  ### Image Editing  ### Art Design Materials  ### Still Image Animation  ### AR  ### 3D Rendering 
## 3. Architecture of Our IS-Net
## 4. Human Correction Efforts (HCE)
## 5. Experimental Results
### 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 ### Qualitative Comparisons Against SOTAs  ### Quantitative Comparisons Against SOTAs 
## 6. Run Our Code
### (1) Clone this repo ``` git clone https://github.com/xuebinqin/DIS.git ``` ### (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).
## 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).
## Acknowledgements
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.
## Citation
``` @InProceedings{qin2022, author={Xuebin Qin and Hang Dai and Xiaobin Hu and Deng-Ping Fan and Ling Shao and Luc Van Gool}, title={Highly Accurate Dichotomous Image Segmentation}, booktitle={ECCV}, year={2022} } ```
## Our Previous Works: [U²-Net](https://github.com/xuebinqin/U-2-Net), [BASNet](https://github.com/xuebinqin/BASNet).
``` @InProceedings{Qin_2020_PR, title = {U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection}, author = {Qin, Xuebin and Zhang, Zichen and Huang, Chenyang and Dehghan, Masood and Zaiane, Osmar and Jagersand, Martin}, journal = {Pattern Recognition}, volume = {106}, pages = {107404}, year = {2020} } @InProceedings{Qin_2019_CVPR, author = {Qin, Xuebin and Zhang, Zichen and Huang, Chenyang and Gao, Chao and Dehghan, Masood and Jagersand, Martin}, title = {BASNet: Boundary-Aware Salient Object Detection}, booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2019} } @article{qin2021boundary, 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}, journal={arXiv preprint arXiv:2101.04704}, year={2021} }