Sascha Dittmann explains how to make Object Detection in TensorFlow.js

Contents:

  • 00:00 | Intro
  • 00:55 | Preview what I will build
  • 01:35 | Create a Custom Vision project
  • 03:01 | Upload & tag the images
  • 05:41 | Train the model
  • 06:30 | Evaluate / test the model
  • 07:42 | Export & download the model
  • 09:10 | A first look inside the web app
  • 10:32 | Integrate the model with TensorFlow.js
  • 15:45 | Check the results
  • 16:12 | Performance Optimizations

In this deeplizard episode, learn how to prepare and process our own custom data set of sign language digits, which will be used to train our fine-tuned MobileNet model in a future episode.

VIDEO SECTIONS

  • 00:00 Welcome to DEEPLIZARD – Go to deeplizard.com for learning resources
  • 00:40 Obtain the Data
  • 01:30 Organize the Data
  • 09:42 Process the Data
  • 13:11 Collective Intelligence and the DEEPLIZARD HIVEMIND

deeplizard  introduces MobileNets, a class of light weight deep convolutional neural networks that are vastly smaller in size and faster in performance than many other popular models.

VIDEO SECTIONS

  • 00:00 Welcome to DEEPLIZARD – Go to deeplizard.com for learning resources
  • 00:17 Intro to MobileNets
  • 02:56 Accessing MobileNet with Keras
  • 07:25 Getting Predictions from MobileNet
  • 13:32 Collective Intelligence and the DEEPLIZARD HIVEMIND

In this video, Mandy from deeplizard  demonstrates how to use the fine-tuned VGG16 Keras model that we trained in the last episode to predict on images of cats and dogs in our test set.

Index:

  • 00:00 Welcome to DEEPLIZARD – Go to deeplizard.com for learning resources
  • 00:17 Predict with a Fine-tuned Model
  • 05:40 Plot Predictions With A Confusion Matrix
  • 05:16 Collective Intelligence and the DEEPLIZARD HIVEMIND