Deep Learning with PyTorch : Object Localization

Offered By
Coursera Project Network
In this Guided Project, you will:

Create custom dataset for Localization problems

Apply augmentations for localization task and load pretrained model

Create train function and evaluator for training loop

Clock2 hours
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

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.

Skills you will develop

  • Deep Learning
  • Object Localization
  • Convolutional Neural Network
  • pytorch
  • Image Processing

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Set up colab run environment

  2. Configurations

  3. Understand the dataset

  4. Augmentations

  5. Create Custom Dataset

  6. Load dataset into batches

  7. Create Model

  8. Create Train and Eval Functions

  9. Training Loop

  10. Inference

How Guided Projects work

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

Frequently asked questions

Frequently Asked Questions

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