Develop an iris tracking model using keypoint detection with TensorFlow and Python in this comprehensive tutorial video. Learn to install and set up the necessary tools, load and prepare data including images and labels, build and train a neural network for keypoint detection, and implement real-time iris detection. Follow along as the instructor guides you through each step, from data creation and preprocessing to model training, performance evaluation, and final implementation. Gain hands-on experience with TensorFlow, data handling, and deep learning techniques while creating a practical computer vision application.
Build a Deep Iris Detection Model Using Python and Tensorflow - Keypoint Detection
Overview
Syllabus
- Intro
- Explainer
- PART 1 - Install and Setup
- PART 2 - Load Data and Labels
- How the Data was Created
- Load Images
- Load Labels
- Combine Image and Label Samples
- View Examples
- PART 3 - Build and Train the Neural Network
- Create the Keypoint Detection Model
- Setup Loss and Optimizer
- Sense Check Predictions
- Train the Model
- PART 4 - Review Performance and Make Predictions
- View Loss Plots
- Save the Model
- PART 5 - Real Time Detection and Final Results
- Ending
Taught by
Nicholas Renotte
