foamliu / Mobile Image Matting
Licence: mit
a lightweight image matting model
Stars: ✭ 61
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python
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Deep Mobile Matting
This is a lightweight image matting model in PyTorch.
Features
- MobileNetV2 as backbone.
- DeepLabv3 heads.
- Small model (size: 23.5MB, FLOPs: 11.39GB, total params: 7.62 millions)
Performance
- The Composition-1k testing dataset.
- Evaluate with whole image.
- SAD normalized by 1000.
- Input image is normalized with mean=[0.485, 0.456, 0.406] and std=[0.229, 0.224, 0.225].
- Both erode and dialte to generate trimap.
| Models | SAD | MSE | Download |
|---|---|---|---|
| paper-stage0 | 59.6 | 0.019 | |
| paper-stage1 | 54.6 | 0.017 | |
| paper-stage3 | 50.4 | 0.014 | |
| my-stage0 | 127.4 | 0.068 | Link |
Dependencies
- Python 3.6.8
- PyTorch 1.3
Dataset
Adobe Deep Image Matting Dataset
Follow the instruction to contact author for the dataset.
MSCOCO
Go to MSCOCO to download:
PASCAL VOC
Go to PASCAL VOC to download:
- VOC challenge 2008 training/validation data
- The test data for the VOC2008 challenge
Usage
Data Pre-processing
Extract training images:
$ python pre_process.py
# python data_gen.py
Train
$ python train.py
If you want to visualize during training, run in your terminal:
$ tensorboard --logdir runs
Experimental results
The Composition-1k testing dataset
- Test:
$ python test.py
It prints out average SAD and MSE errors when finished.
Demo
Download pre-trained Deep Image Matting Link then run:
$ python demo.py
| Image/Trimap | Output/GT | New BG/Compose |
|---|---|---|
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