Deep Learning with PyTorch : Image Segmentation
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Use U-Net architecture for segmentation
Create train function and evaluator for training loop
在面试中展现此实践经验
1,668 人已注册
Use U-Net architecture for segmentation
Create train function and evaluator for training loop
在面试中展现此实践经验
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.
Prior programming experience in Python and basic pytorch. Theoretical knowledge of Convolutional Neural Network and Training process (Optimization)
Mathematical Optimization
Convolutional Neural Network
Autoencoder
Python Programming
pytorch
在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:
Set up colab runtime environment
Setup Configurations
Augmentations
Custom Dataset
Load Dataset into batches
Create Segmentation Model
Create Train and Eval Function
Train Model
Inference
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由 YY 提供
Feb 20, 2022Great instructor and very practical hands-on approach. I would prefer more explanation on other encoder and weight presets as that will be important for transferring the knowledge learned here!
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