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November 13, 2024
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Instructor: Parth Dhameliya
2,881 already enrolled
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(27 reviews)
Recommended experience
Intermediate level
Prior programming experience in Python and basic pytorch. Theoretical knowledge of Convolutional Neural Network and Training process (Optimization)
(27 reviews)
Recommended experience
Intermediate level
Prior programming experience in Python and basic pytorch. Theoretical knowledge of Convolutional Neural Network and Training process (Optimization)
Create train function and evaluator for training loop
Use U-Net architecture for segmentation
Add to your LinkedIn profile
Only available on desktop
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.
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
Prior programming experience in Python and basic pytorch. Theoretical knowledge of Convolutional Neural Network and Training process (Optimization)
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Practice new skills by completing job-related tasks.
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Access the tools and resources you need in a pre-configured cloud workspace.
Available only on desktop
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27 reviews
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Reviewed on Dec 25, 2022
4 because there were quite a few mistakes which should be corrected!
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