Awesome
A<sup>2</sup>U Matting
<p align="center"> <img src="example.png" width="800" title="Example"/> </p>The official repository for paper:
Learning Affinity-Aware Upsampling for Deep Image Matting, CVPR2021
Prerequisite
git clone https://github.com/dongdong93/a2u_matting.git
cd a2u_matting/scripts
git clone https://github.com/mapillary/inplace_abn.git
cd inplace_abn
python setup.py install
cd ../Biupdownsample
python setup.py develop
Installation
Our code has been tested on Python 3.6, Pytorch 1.3.1, CUDA 10.0 (inplace_abn only supports CUDA>=10.0). See other required packages in requirements.txt.
Demo
sh demo.sh
Our model trained on the Adobe Image Matting dataset can be downloaded from:
| Model | SAD | MSE | Grad | Conn | config |
|---|---|---|---|---|---|
| Ours | 32.10 | 0.0078 | 16.33 | 29.00 | ./config/adobe.yaml |
Disclaimer:
- This is a reimplementation. The metrics may be slightly different from the ones reported in our original paper.
Another pretrained model on the Adobe Image Matting dataset with 'unshare' setting can be downloaded from:
| Model | SAD | MSE | Grad | Conn | config |
|---|---|---|---|---|---|
| Ours-unshare | 31.87 | 0.0075 | 14.25 | 29.17 | ./config/adobe_unshare.yaml |
As covered by ADOBE IMAGE MATTNG DATASET LICENSE AGREEMENT, the trained models included in this repository can only be used/distributed for non-commercial purposes.
Training
Data Preparation
- Please contact Deep Image Matting for the Adobe Image Matting dataset;
- The final structure for use:
$PATH_TO_DATASET/Combined_Dataset
├──── Training_set
│ ├──── alpha (431 images)
│ ├──── fg (431 images)
│ └──── train2014 (82783 images)
├──── Test_set
│ ├──── alpha (50 images)
│ ├──── merged (1000 images)
│ └──── trimaps (1000 images)
- Please contact HAttMatting for the Distinctions-646 dataset and organize it following the same structure;
Backbone
Download the pretrained ResNet34 from here and save to ./pretrained. (It will be automatically downloaded)
Train your model
sh train.sh
Models will be saved to ./savemodel by default. FYI, it can be trained with 2 1080Ti using the provided config files.
Inference
sh test.sh
Results will be saved to ./results by default. Quantitative metrics should be measured by matlab_testcode/test_withpic.m. The python version is for reference only.
Citation
If you find this work or code useful for your research, please consider citing:
@inproceedings{dai2021learning,
title={Learning Affinity-Aware Upsampling for Deep Image Matting},
author={Dai, Yutong and Lu, Hao and Shen, Chunhua},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={6841--6850},
year={2021}
}