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Create custom dataset for Localization problems
Apply augmentations for localization task and load pretrained model
Create train function and evaluator for training loop
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Object Localization is the task of locating an instance of a particular object category in an image, typically by specifying a tightly cropped bounding box centered on the instance. In this 2-hour project-based course, you will be able to understand the Object Localization Dataset and you will write a custom dataset class for Image-bounding box dataset. Additionally, you will apply augmentation for localization task to augment images as well as its effect on bounding box. For localization task augmentation you will use albumentation library. We will plot the (image-bounding box) pair. Thereafter, we will load a pretrained state of the art convolutional neural network using timm library.Moreover, we are going to create train function and evaluator function which will be helpful to write training loop. Lastly, you will use best trained model to find bounding box given any image.
Prior programming experience in Python and basic pytorch. Theoretical knowledge of Convolutional Neural Network and Training process (Optimization)
Deep Learning
Object Localization
Convolutional Neural Network
pytorch
Image Processing
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Set up colab run environment
Configurations
Understand the dataset
Augmentations
Create Custom Dataset
Load dataset into batches
Create Model
Create Train and Eval Functions
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
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You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.
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