Skip to content
/ UIM Public

The official pytorch implementation of Exploring the Interactive Guidance for Unified and Effective Image Matting

License

Notifications You must be signed in to change notification settings

Dinghow/UIM

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

3 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Exploring the Interactive Guidance for Unified and Effective Image Matting

This repo is the official PyTorch implementation of Exploring the Interactive Guidance for Unified and Effective Image Matting. Since this work is still under review, we only provide the test code and related models, and the full code will be released later.

1. Requirements

  • Hardware: 4-8 GPUs (better with >=11G GPU memory)

  • Software: PyTorch>=1.0.0, Python3, tensorboardX, and so on.

  • to install tensorboardX, you could do as follows:

    pip install tensorboardX
    pip install tensorboard
    
  • install python packages: pip install -r requirements.txt

2. Dataset

Composition-1K

Please contact Brian Price (bprice@adobe.com) requesting for the dataset. And following the instructions for preparation.

Synthetic Unified Dataset

Since the dataset is processed from Composition-1K, you can generate it by:

  • Acquire Composition-1K as metioned above
  • Select SO and ST images from license-free websites, and composit them with SO and ST from Composition-1K to generate NSO and NST images (You need a manual selection to confirm the composited image are natural with multi-objects)

Finally, the folder structure should be:

DATA_ROOT
├── Combined_Dataset
│   ├── Test_set
│   │   │   ├── alpha
│   │   │   ├── fg
│   │   │   ├── bg
│   │   │   ├── trimaps
│   │   │   ├── ImageSets 
├── combined_4classes (the synthetic unified dataset)
│   │   ├── alphas
│   │   ├── images
│   │   │   ├── SO
│   │   │   ├── ST
│   │   │   ├── NSO
│   │   │   ├── NST
│   │   ├── trimaps

3. Model Zoo

Methods Annotations Links MSE SAD Grad Conn Notes
UIM (box) Bounding box gdrive 0.012 38.15 17.90 33.76 trimap-based
UIM (box) Bounding box gdrive 0.006 49.85 25.24 43.60 trimap-free
UIM (dextr) Extreme points gdrive 0.015 77.25 33.14 59.93 trimap-free
UIM (in_point) Foreground point gdrive 0.077 265.87 103.81 142.87 trimap-free
UIM (iog) FG/BG points gdrive 0.042 165.92 74.85 92.18 trimap-free
UIM (scribble) Scribble gdrive 0.039 139.03 39.55 58.31 trimap-free

*The metrics are all tested on Composition-1K

4. Inference

Download pretrained models and put them under ./pretrained.

Run on one GPU to evaluate the model, the examples are as follow:

  • Test on Composition-1K:
cd UIM/
sh seg_matting_tool/test.sh comp1k uim_bbox
  • Test on the synthetic unified dataset:
cd UIM/
sh seg_matting_tool/test.sh 4classes uim_bbox

5. License

This project is under the MIT license.

About

The official pytorch implementation of Exploring the Interactive Guidance for Unified and Effective Image Matting

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published