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on
1
- Intro
2
- Explainer
3
- PART 1 - Install and Setup
4
- PART 2 - Load Data and Labels
5
- How the Data was Created
6
- Load Images
7
- Load Labels
8
- Combine Image and Label Samples
9
- View Examples
10
- PART 3 - Build and Train the Neural Network
11
- Create the Keypoint Detection Model
12
- Setup Loss and Optimizer
13
- Sense Check Predictions
14
- Train the Model
15
- PART 4 - Review Performance and Make Predictions
16
- View Loss Plots
17
- Save the Model
18
- PART 5 - Real Time Detection and Final Results
19
- Ending
Description:
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

Nicholas Renotte
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