Segmentation Models: using `keras` framework.
时间: 2023-08-31 14:13:37 浏览: 117
使用segmentation_models.pytorch图像分割框架实现对人物的抠图.zip
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Segmentation Models is a Python library for image segmentation using various deep learning frameworks including Keras. It provides implementations of various popular segmentation models such as UNet, SegNet, PSPNet, and FPN along with pre-trained weights and evaluation metrics. The library also allows users to easily train their own models on custom datasets.
To use Segmentation Models with Keras, you first need to install the library using pip:
```
pip install segmentation-models
```
Then, you can import the desired segmentation model from the `segmentation_models` module and use it like any other Keras model. For example, to create a UNet model with a ResNet34 backbone:
```python
from segmentation_models import Unet
from segmentation_models.backbones import get_preprocessing
# Load and preprocess input data
X_train, y_train = ...
X_train = get_preprocessing('resnet34')(X_train)
# Create UNet model
model = Unet('resnet34', input_shape=(None, None, 3), classes=1, activation='sigmoid')
# Compile and train model
model.compile(optimizer='adam', loss='binary_crossentropy')
model.fit(X_train, y_train, ...)
```
Note that the `get_preprocessing` function is used to apply the appropriate preprocessing for the chosen backbone model. In this example, we use the `resnet34` backbone, but other options such as `vgg16`, `efficientnetb3`, and `mobilenetv2` are also available.
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