Deep Learning with PyTorch : Image Segmentation

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In this Free Guided Project, you will:

Use U-Net architecture for segmentation

Create train function and evaluator for training loop

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2 hours
Intermediate
No download needed
Split-screen video
English
Desktop only

In this 2-hour project-based course, you will be able to : - Understand the Segmentation Dataset and you will write a custom dataset class for Image-mask dataset. Additionally, you will apply segmentation augmentation to augment images as well as its masks. For image-mask augmentation you will use albumentation library. You will plot the image-Mask pair. - Load a pretrained state of the art convolutional neural network for segmentation problem(for e.g, Unet) using segmentation model pytorch library. - Create train function and evaluator function which will helpful to write training loop. Moreover, you will use training loop to train the model.

Requirements

Prior programming experience in Python and basic pytorch. Theoretical knowledge of Convolutional Neural Network and Training process (Optimization)

Skills you will develop

  • Mathematical Optimization

  • Convolutional Neural Network

  • Autoencoder

  • Python Programming

  • pytorch

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Set up colab runtime environment

  2. Setup Configurations

  3. Augmentations

  4. Custom Dataset

  5. Load Dataset into batches

  6. Create Segmentation Model

  7. Create Train and Eval Function

  8. Train Model

  9. Inference

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Reviews

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Frequently Asked Questions

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Yes, everything you need to complete your Guided Project will be available in a cloud desktop that is available in your browser.

You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.