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Showing content from https://github.com/foamliu/Deep-Image-Matting-v2 below:

foamliu/Deep-Image-Matting-PyTorch: Deep Image Matting implementation in PyTorch

Deep Image Matting paper implementation in PyTorch.

1. "fc6" is dropped.
2. Indices pooling.

"fc6" is clumpy, over 100 millions parameters, makes the model hard to converge. I guess it is the reason why the model (paper) has to be trained stagewisely.

• 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.

• Python 3.5.2
• PyTorch 1.1.0

Follow the instruction to contact author for the dataset.

Go to MSCOCO to download:

• 2014 Train images

Go to PASCAL VOC to download:

• VOC challenge 2008 training/validation data
• The test data for the VOC2008 challenge

Extract training images:

If you want to visualize during training, run in your terminal:

$ tensorboard --logdir runs

1. Test:

It prints out average SAD and MSE errors when finished.

1. Download the evaluation datasets: Go to the Datasets page and download the evaluation datasets. Make sure you pick the low-resolution dataset.

2. Extract evaluation images:

1. Evaluate:

Click to view whole images:

Download pre-trained Deep Image Matting Link then run:

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