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Mobile Artificial Intelligence Projects

You're reading from  Mobile Artificial Intelligence Projects

Product type Book
Published in Mar 2019
Publisher Packt
ISBN-13 9781789344073
Pages 312 pages
Edition 1st Edition
Languages
Authors (3):
Karthikeyan NG Karthikeyan NG
Profile icon Karthikeyan NG
Arun Padmanabhan Arun Padmanabhan
Profile icon Arun Padmanabhan
Matt Cole Matt Cole
Profile icon Matt Cole
View More author details

Table of Contents (12) Chapters

Preface 1. Artificial Intelligence Concepts and Fundamentals 2. Creating a Real-Estate Price Prediction Mobile App 3. Implementing Deep Net Architectures to Recognize Handwritten Digits 4. Building a Machine Vision Mobile App to Classify Flower Species 5. Building an ML Model to Predict Car Damage Using TensorFlow 6. PyTorch Experiments on NLP and RNN 7. TensorFlow on Mobile with Speech-to-Text with the WaveNet Model 8. Implementing GANs to Recognize Handwritten Digits 9. Sentiment Analysis over Text Using LinearSVC 10. What is Next? 11. Other Books You May Enjoy

Datasets for image classification

For our flower classification example, we will be using the University of Oxford's Visual Geometry Group (VGG) image dataset collection. The collection can be accessed at http://www.robots.ox.ac.uk/~vgg/data/.

The VGG is the same department that won previous ImageNet competitions. The pretrained models, such as VGG14 and VGG16, were built by this department and they won in 2014 and 2016, respectively. These datasets are used by the VGG to train and evaluate the models that they build.

The flower dataset can be found in the Fine-Grain Recognition Datasets section of the page, along with textures and pet datasets. Click on Flower Category Datasets, or use the following link to access the flower datasets from VGG, http://www.robots.ox.ac.uk/~vgg/data/flowers/.

Here, you can find two datasets, one with 17 different species of flowers, and the...

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