Aerial Image Segmentation with PyTorch
10 ratings

Create train function and evaluator for training loop
Use U-Net architecture for segmentation
Showcase this hands-on experience in an interview
10 ratings
Create train function and evaluator for training loop
Use U-Net architecture for segmentation
Showcase this hands-on experience in an interview
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.
Prior programming experience in Python and basic pytorch. Theoretical knowledge of Convolutional Neural Network and Training process (Optimization)
Convolutional Neural Network
Python Programming
Autoencoder
pytorch
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Setting up colab runtime
Setup Configurations
Augmentation Functions
Create Custom Dataset
Load dataset into batches
Create Segmentation Model
Create Train and Valid function
Training Loop
Inference
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
by AG
Dec 25, 20224 because there were quite a few mistakes which should be corrected!
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