AI/U-2-Net
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@ -57,7 +57,7 @@ __Contact__: xuebin[at]ualberta[dot]ca
git clone https://github.com/NathanUA/U-2-Net.git git clone https://github.com/NathanUA/U-2-Net.git
``` ```
2. Download the u2net_portrait.pth in [**GoogleDrive**](https://drive.google.com/file/d/1IG3HdpcRiDoWNookbncQjeaPN28t90yW/view?usp=sharing) or [**Baidu pan(提取码：chgd)**](https://pan.baidu.com/s/1BYT5Ts6BxwpB8_l2sAyCkw)model and put it into the directory: ```./saved_models/u2net_portrait/```. 2. Download the u2net_portrait.pth from [**GoogleDrive**](https://drive.google.com/file/d/1IG3HdpcRiDoWNookbncQjeaPN28t90yW/view?usp=sharing) or [**Baidu Pan(提取码：chgd)**](https://pan.baidu.com/s/1BYT5Ts6BxwpB8_l2sAyCkw)model and put it into the directory: ```./saved_models/u2net_portrait/```.
3. Run on the testing set. <br/> 3. Run on the testing set. <br/>
(1) Download the train and test set from [**APDrawingGAN**](https://github.com/yiranran/APDrawingGAN). These images and their ground truth are stitched side-by-side (512x1024). You need to split each of these images into two 512x512 images and put them into ```./test_data/test_portrait_images/portrait_im/```. You can also download the split testing set on [GoogleDrive](https://drive.google.com/file/d/1NkTsDDN8VO-JVik6VxXyV-3l2eo29KCk/view?usp=sharing). <br/> (1) Download the train and test set from [**APDrawingGAN**](https://github.com/yiranran/APDrawingGAN). These images and their ground truth are stitched side-by-side (512x1024). You need to split each of these images into two 512x512 images and put them into ```./test_data/test_portrait_images/portrait_im/```. You can also download the split testing set on [GoogleDrive](https://drive.google.com/file/d/1NkTsDDN8VO-JVik6VxXyV-3l2eo29KCk/view?usp=sharing). <br/>
@ -99,7 +99,7 @@ glob
``` ```
git clone https://github.com/NathanUA/U-2-Net.git git clone https://github.com/NathanUA/U-2-Net.git
``` ```
2. Download the pre-trained model [u2net.pth (176.3 MB)](https://drive.google.com/file/d/1ao1ovG1Qtx4b7EoskHXmi2E9rp5CHLcZ/view?usp=sharing) or [u2netp.pth (4.7 MB)](https://drive.google.com/file/d/1rbSTGKAE-MTxBYHd-51l2hMOQPT_7EPy/view?usp=sharing) and put it into the dirctory './saved_models/u2net/' and './saved_models/u2netp/' 2. Download the pre-trained model u2net.pth (176.3 MB) from [**GoogleDrive**](https://drive.google.com/file/d/1ao1ovG1Qtx4b7EoskHXmi2E9rp5CHLcZ/view?usp=sharing) or [**Baidu Pan 提取码: pf9k**](https://pan.baidu.com/s/1WjwyEwDiaUjBbx_QxcXBwQ) or u2netp.pth (4.7 MB) from [**GoogleDrive**](https://drive.google.com/file/d/1rbSTGKAE-MTxBYHd-51l2hMOQPT_7EPy/view?usp=sharing) or [**Baidu Pan 提取码: 8xsi**](https://pan.baidu.com/s/10tW12OlecRpE696z8FxdNQ) and put it into the dirctory './saved_models/u2net/' and './saved_models/u2netp/'
3. Cd to the directory 'U-2-Net', run the train or inference process by command: ```python u2net_train.py``` 3. Cd to the directory 'U-2-Net', run the train or inference process by command: ```python u2net_train.py```
or ```python u2net_test.py``` respectively. The 'model_name' in both files can be changed to 'u2net' or 'u2netp' for using different models. or ```python u2net_test.py``` respectively. The 'model_name' in both files can be changed to 'u2net' or 'u2netp' for using different models.