Deep Learning with PyTorch : Object Localization

ofrecido por
Coursera Project Network
En este proyecto guiado, tú:

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 se necesita descarga
VideoVideo de pantalla dividida
Comment DotsInglés (English)
LaptopSolo escritorio

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.

Habilidades que desarrollarás

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

Aprende paso a paso

En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:

  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

Cómo funcionan los proyectos guiados

Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.

En un video de pantalla dividida, tu instructor te guía paso a paso

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