Implement GradCAM function practically
Create train and eval function
Showcase this hands-on experience in an interview
Gradient-weighted Class Activation Mapping (Grad-CAM), uses the class-specific gradient information flowing into the final convolutional layer of a CNN to produce a coarse localization map of the important regions in the image. In this 2-hour long project-based course, you will implement GradCAM on simple classification dataset. You will write a custom dataset class for Image-Classification dataset. Thereafter, you will create custom CNN architecture. Moreover, you are going to create train function and evaluator function which will be helpful to write the training loop. After, saving the best model, you will write GradCAM function which return the heatmap of localization map of a given class. Lastly, you plot the heatmap which the given input image.
Prior programming experience in Python, PyTorch. Theoretical knowledge of Convolutional Neural Network, Training process (Optimization) and GradCAM.
Deep Learning
GradCAM
Convolutional Neural Network
pytorch
Computer Vision
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Set up colab runtime environment
Configurations
Augmentations
Load Image Dataset
Load Dataset into batches
Create Model
Create Train and eval function
Training Loop
Get GradCAM
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