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huochaitiantang/pytorch-deep-image-matting: Pytorch implementation of deep image matting
pytorch-deep-image-matting
This repository includes the non-official pytorch implementation of deep image matting.
model SAD ↓ MSE ↓ Grad ↓ Conn ↓ link stage0-paper 59.6 0.019 40.5 59.3 stage1-paper 54.6 0.017 36.7 55.3 stage0-our 56.01 0.0173 33.71 57.57 stage1-our 54.42 0.0175 35.01 54.85 download stage1-our-skip 52.99 0.0171 31.56 53.24 download
- Lower metrics show better performance.
- Training batch=1, images=43100, epochs=12 for stage1-our-skip model, it takes about 1 days.
- Test maxSize=1600.
- GPU memory >= 10GB
- 2020.09.09: run the demo.py with the latest model (stage1-skip-sad-52.9.pth), and update the visualization results.
- 2020.09.09: adopt VGG-16 backbone with skip connection, and we get better performance with less training costs (12 epochs), Get Stage1-Skip-SAD=52.9
- 2019.10.29: conduct stage0 experiment using current code. Get Stage0-SAD=56.0.
- 2019.09.09: conv6 kernel size from 1x1 to 3x3. Get Stage1-SAD=54.4. The performance of stage1 is as good as paper. While using model released before this day, please change the kernel_size=1 and padding=0 of conv6 in file core/net.py.
- 2019.08.24: Fix cv2.dilate and cv2.erode iterations is set default = 1 and set triamp dilate and erode as the test 1k tirmap (k_size:2-5, iterations:5-15). Get Stage1-SAD=57.1.
- 2019.07.05: Training with refine stage, fixed encoder-decoder. Get Stage2-SAD=57.7.
- 2019.06.23: Training with alpha loss and composite loss. Get Stage1-SAD=58.7.
- 2019.06.17: Training trimap generated by erode as well as dialte to balance the 0 and 1 value. Get Stage0-SAD=62.0.
- 2019.04.22: Input image is normalized by mean=[0.485, 0.456, 0.406] and std=[0.229, 0.224, 0.225] and fix crop error. Get Stage0-SAD=69.1.
- 2018.12.14: Initial. Get Stage0-SAD=72.9.
- Python 2.7.12 or 3.6.5
- Pytorch 0.4.0 or 1.0.0
- OpenCV 3.4.3
Download our model to the ./model and run the following command. Then the predict alpha mattes will locate in the folder ./result/example/pred.
Adobe-Deep-Image-Matting-Dataset
Please concat author for available.
MSCOCO-2017-Train-Dataset
Download
Download
Run the following command and the composite training and test dataset will locate in Combined_Dataset/Training_set/comp and Combined_Dataset/Test_set/comp, Combined_Dataset is the extracted folder of Adobe-Deep-Image-Matting-Dataset
python tools/composite.py
Run the following command and the pretrained model will locate in ./model/vgg_state_dict.pth
python tools/chg_model.py
Run the following command and start the training
Run the following command and start the test of Adobe-1k-Composite-Dataset
Please eval with official Matlab Code. and get the SAD, MSE, Grad Conn.
Running model is Stage1-Skip-SAD=52.9, please click to view whole images.
As covered by the ADOBE IMAGE DATASET LICENSE AGREEMENT, the pre-trained models included in this repository can only be used and distributed for non-commercial purposes.
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