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  3. Deep Learning for Images with PyTorch
  • 1

    Image classification with CNNs

    Kostenlos

    Learn about image classification with CNNs, the difference between the binary and multi-class image classification models, and how to use transfer learning for image classification in PyTorch.

  • 2

    Object recognition

    Detect objects in images by predicting bounding boxes around them and evaluate the performance of object recognition models.

  • 3

    Image Segmentation

    Learn about the three types of image segmentation (semantic, instance, and panoptic), their applications, and the appropriate machine learning model architectures to perform each of them.

  • 4

    Image Generation with GANs

    Generate completely new images with Generative Adversarial Networks (GANs). Learn to build and train a Deep Convolutional GAN, and how to evaluate the quality and variety of its outputs.

Initializing

Ubung

Segmenting with pre-trained Mask R-CNN

In this exercise, you will use the pre-trained Mask R-CNN model to perform instance segmentation on the following image of two cats.

two cats image

The model you will use has been pre-trained on the COCO dataset, which contains images of common objects, including animals. Thanks to this, the model should be able to recognize cats out of the box, without the need to fine-tune it.

Your task is to load the model and the two cats image, prepare the image, and pass it to the model to obtain the predictions. Image from PIL, torch, and transforms from torchvision have been imported for you.

Anweisungen

100 XP
  • Import maskrcnn_resnet50_fpn from the appropriate torchvision module.
  • Load the pretrained Mask R-CNN to model.
  • Transform the two cats image to a tensor and unsqueeze it.
  • Perform inference by passing the image to the model and assign the output to prediction.