Using Machine Learning and TensorFlow to Recognize Traffic Signs
This time, TensorFlow meetup in Silicon Valley offered the attendees a practical session aimed at enabling a neural network to classify traffic signs. With machine learning, the solution can be applied to recognize signs right on the go while driving in a car.
Waleed Abdulla, a founder and CEO at Ninua, delivered a hands-on training on applying machine learning to recognize traffic signs in a video shot from a moving car. Under the tutorial, Waleed demonstrated how to build a neural network from scratch and enable it to classify traffic signs.
For that purpose, the speaker utilized the following technology stack:
- Python 3.5
- TensorFlow 0.11
- Docker image
- the NumPy library
- the scikit-image algorithms
- the Matplotlib library
- the BelgiumTS data set
In the course of the tutorial, Waleed also gave some tips on how to:
- Parse and load the training data. Though the images are saved in the uncommon .ppm format, it can be solved with the scikit library.
- Handling images of different size. To verify data range and catch bugs early, Waleed suggested printing the min() and max() values on resizing images.
- Evaluation. When visualizing the results, one has to remember to use a validation dataset and measure the accuracy of a model.
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Want details? Watch the video!
- Using Convolutional Neural Networks and TensorFlow for Image Classification
- Building a Keras-Based Image Classifier Using TensorFlow for a Back End
- Introduction to Neural Networks and Meta-Frameworks for Deep Learning with TensorFlow
About the expert
Waleed Abdulla is a founder and CEO at Ninua. As a software engineer, he is interested in deep learning and web development. Currently, Waleed is running a tech startup in Mountain View, building SymphonyTools—a social media management dashboard for businesses.
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