Deep Learning with PyTorch : Object Localization

提供方
Coursera Project Network
在此指导项目中,您将:

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
Intermediate中级
Cloud无需下载
Video分屏视频
Comment Dots英语(English)
Laptop仅限桌面

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.

您要培养的技能

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

分步进行学习

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:

  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

指导项目工作原理

您的工作空间就是浏览器中的云桌面,无需下载

在分屏视频中,您的授课教师会为您提供分步指导

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