Aerial Image Segmentation with PyTorch
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.
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- Level Intermediate
- Duration 2 hours
- Course by Coursera Project Network
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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.Modules
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- Aerial Image Segmentation with PyTorch
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Discover the art of Aerial Image Segmentation with PyTorch in this engaging 2-hour project-based course. Guided by Coursera, you'll dive into the Massachusetts Roads Segmentation Dataset, write custom dataset classes, and apply advanced image-mask augmentations. Learn to utilize a pretrained convolutional neural network, create essential training functions, and train your model effectively. Ideal for intermediate learners, this free course empowers you to master segmentation model inference with hands-on experience.
Parth Dhameliya