I got this function that builds a DeeplabV3+ model with a ResNet50 backend for semantic segmentation

```
def DeepLabV3_ResNet50(size, classes):
input = keras.Input(shape=(size, size, 3))
resnet50 = keras.applications.ResNet50(weights="imagenet", include_top=False, input_tensor = input)
x = resnet50.get_layer("conv4_block6_2_relu").output
x = DSP_pooling(x)
a = layers.UpSampling2D(size=(size // 4 // x.shape[1], size // 4 // x.shape[2]),interpolation="bilinear",)(x)
b = resnet50.get_layer("conv2_block3_2_relu").output
b = block(b, filters = 48, kernel = 1)
x = layers.Concatenate(axis=-1)([a, b])
x = block(x)
x = block(x)
x = layers.UpSampling2D(size=(size // x.shape[1], size // x.shape[2]),interpolation="bilinear",)(x)
output = layers.Conv2D(classes, kernel_size=(1, 1), padding="same")(x)
return keras.Model(inputs = input, outputs = output)
model = DeepLabV3_ResNet50(size = image_size, classes = labels)
model.summary()
```

To improve my validation accuracy, I figured that switching the backend from ResNet50 to ResNet101 might be a good try. Changing `resnet50.get_layer()`

to `resnet101.get_layer()`

is not enough. How do I know which convolutional blocks I should pick for my `resnet101.get_layer()`

function?