Aerial Image Segmentation with PyTorch

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In this Guided Project, you will:

Create train function and evaluator for training loop

Use U-Net architecture for segmentation

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 Massachusetts Roads Segmentation Dataset and you will write a custom dataset class for Image-mask dataset. Additionally, you will apply segmentation domain augmentations 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. - Finally, we will use best trained segementation model for inference.

Skills you will develop

  • Convolutional Neural Network

  • Python Programming

  • Autoencoder

  • 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. Setting up colab runtime

  2. Setup Configurations

  3. Augmentation Functions

  4. Create Custom Dataset

  5. Load dataset into batches

  6. Create Segmentation Model

  7. Create Train and Valid function

  8. Training Loop

  9. Inference

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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

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