Running semantic segmentation

Good job designing the U-Net! You will find an already pre-trained model very similar to the one you have just built available to you. This model has been trained on a large set of images and contains a few small additions to the architecture, such as the batch norm layers.

You can instantiate the model as UNet(), which will provide the model with the pre-trained weights. You task is to use it to produce segmentation masks for the following image of a car.

car image

Image from PIL has already been imported for you.

Bu egzersiz

Deep Learning for Images with PyTorch

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Instantiate UNet() in a variable called model.
Load the image at car.jpg to a variable called image.
Produce segmentation masks by passing the image to the model and squeeze(0)-ing the output.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Load model
model = ____
model.eval()

# Load and transform image
image = ____
transform = transforms.Compose([transforms.ToTensor()])
image_tensor = transform(image).unsqueeze(0)

# Predict segmentation mask
with torch.no_grad():
    prediction = ____

# Display mask
plt.imshow(prediction[1, :, :])
plt.show()

Kodu Düzenle ve Çalıştır