Wrap-up
1. Wrap-up
Congratulations on completing the course!2. What you learned
In Chapter 1, you got a refresher on image classification with convolutional neural networks. You tackled binary and multi-class classification problems and worked with convolutional layers by grouping them into blocks. Finally, you learned to leverage pre-trained models.3. What you learned
In Chapter 2, you got familiar with object recognition. You learned how to work with bounding boxes and how to use pre-trained recognition models including R-CNN and Faster R-CNN. You also post-processed the models' outputs with non-max suppression and evaluated them using intersection over union.4. What you learned
In Chapter 3, you tackled image segmentation. You learned to work with segmentation masks, and to perform all three types of segmentation: instance segmentation with a Mask R-CNN model, semantic segmentation with a U-Net, and panoptic segmentation by combining the outputs of the previous two methods.5. What you learned
Finally, in Chapter 4, you got familiar with Generative Adversarial Networks, or GANs. You learned how they work and how to build and train a Deep Convolutional GAN. You have also evaluated the quality and variety of generated images using the Frechet Inception Distance.6. Congratulations and good luck!
Once again, congratulations! I hope the knowledge and skills you've gained will help you build a variety of image-based models in PyTorch!Create Your Free Account
or
By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.