CODEWITHSUNDEEP
tensorflowkerassemantic-segmentationunet-neural-network
sohail bukhari
sohail bukhari

Reputation: 1

How to implement U Net for custom dataset using Tensorflow and Keras

I want to implement U Net for semantic segmentation on my own dataset which contains two classes. Can anyone please let me know how can I implement with Tensorflow and Keras. I have two classes in my dataset with their corresponding labels.

Upvotes: 0

Views: 744

Answers (1)

Zabir Al Nazi Nabil
Zabir Al Nazi Nabil

Reputation: 11218

This is an U-Net implementation for binary classification/segmentation. 

def UNET_224(dropout_val=0.0, weights=None): # No dropout by default
    if K.image_dim_ordering() == 'th':
        inputs = Input((INPUT_CHANNELS, 224, 224))
        axis = 1
    else:
        inputs = Input((224, 224, INPUT_CHANNELS))
        axis = 3
    filters = 32

    conv_224 = double_conv_layer(inputs, filters)
    pool_112 = MaxPooling2D(pool_size=(2, 2))(conv_224)

    conv_112 = double_conv_layer(pool_112, 2*filters)
    pool_56 = MaxPooling2D(pool_size=(2, 2))(conv_112)

    conv_56 = double_conv_layer(pool_56, 4*filters)
    pool_28 = MaxPooling2D(pool_size=(2, 2))(conv_56)

    conv_28 = double_conv_layer(pool_28, 8*filters)
    pool_14 = MaxPooling2D(pool_size=(2, 2))(conv_28)

    conv_14 = double_conv_layer(pool_14, 16*filters)
    pool_7 = MaxPooling2D(pool_size=(2, 2))(conv_14)

    conv_7 = double_conv_layer(pool_7, 32*filters)

    up_14 = concatenate([UpSampling2D(size=(2, 2))(conv_7), conv_14], axis=axis)
    up_conv_14 = double_conv_layer(up_14, 16*filters)

    up_28 = concatenate([UpSampling2D(size=(2, 2))(up_conv_14), conv_28], axis=axis)
    up_conv_28 = double_conv_layer(up_28, 8*filters)

    up_56 = concatenate([UpSampling2D(size=(2, 2))(up_conv_28), conv_56], axis=axis)
    up_conv_56 = double_conv_layer(up_56, 4*filters)

    up_112 = concatenate([UpSampling2D(size=(2, 2))(up_conv_56), conv_112], axis=axis)
    up_conv_112 = double_conv_layer(up_112, 2*filters)

    up_224 = concatenate([UpSampling2D(size=(2, 2))(up_conv_112), conv_224], axis=axis)
    up_conv_224 = double_conv_layer(up_224, filters, dropout_val)

    conv_final = Conv2D(OUTPUT_MASK_CHANNELS, (1, 1))(up_conv_224)
    conv_final = Activation('sigmoid')(conv_final)

    model = Model(inputs, conv_final, name="UNET_224")


    return model

Upvotes: 0

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