# U2Netp
|Module Name |U2Netp|
| :--- | :---: |
|Category |Image segmentation|
|Network |U^2Net|
|Dataset|-|
|Fine-tuning supported or not|No|
|Module Size |6.7MB|
|Data indicators|-|
|Latest update date|2021-02-26|
## I. Basic Information
- ### Application Effect Display
- Sample results:
- ### Module Introduction
- Network architecture:
- For more information, please refer to: [U2Net](https://github.com/xuebinqin/U-2-Net)
## II. Installation
- ### 1、Environmental Dependence
- paddlepaddle >= 2.0.0
- paddlehub >= 2.0.0
- ### 2、Installation
- ```shell
$ hub install U2Netp
```
- In case of any problems during installation, please refer to:[Windows_Quickstart](../../../../docs/docs_en/get_start/windows_quickstart.md)
| [Linux_Quickstart](../../../../docs/docs_en/get_start/linux_quickstart.md) | [Mac_Quickstart](../../../../docs/docs_en/get_start/mac_quickstart.md)
## III. Module API Prediction
- ### 1、Prediction Code Example
```python
import cv2
import paddlehub as hub
model = hub.Module(name='U2Netp')
result = model.Segmentation(
images=[cv2.imread('/PATH/TO/IMAGE')],
paths=None,
batch_size=1,
input_size=320,
output_dir='output',
visualization=True)
```
- ### 2、API
```python
def Segmentation(
images=None,
paths=None,
batch_size=1,
input_size=320,
output_dir='output',
visualization=False):
```
- Prediction API, obtaining segmentation result.
- **Parameter**
* images (list[np.ndarray]) : Image data, ndarray.shape is in the format [H, W, C], BGR.
* paths (list[str]) : Image path.
* batch_size (int) : Batch size.
* input_size (int) : Input image size, default is 320.
* output_dir (str) : Save path of images, 'output' by default.
* visualization (bool) : Whether to save the results as picture files.
- **Return**
* results (list[np.ndarray]): The list of segmentation results.
## IV. Release Note
- 1.0.0
First release