Neural-net object recognition
Using neural-nets to recognise objects.

Why?

Because we need to recognise real-world objects in order to do some tasks.

Neural-nets

After the long AI winter, neural-nets have sprung up again, due to more powerful machines, millions of easily accessible images via internet, and an understanding of back-propagation. They make very good classifiers for known object sets, and can be run on machines available to the average user - a quality desktop computer with a $AU1000.00 GPU does nicely.

Process - retrain an existing image classifier for new categories

This section is a collection of notes about how the object recognition system was set up. Basically the strategy was to use the retraining mechanism described at https://www.tensorflow.org/hub/tutorials/image_retraining "How to Retrain an Image Classifier for New Categories" to train to recognise kangaroos. This involved

    Getting a copy of openImages. This is really big collection classified (labelled) images which contain many kangaroo pictures.
    Installing tensorflow_hub, which includes a 'pretrained' object classifier.
    Putting appropriate images into sub-directories of a animal_photos directory (kangaroo and non-kangaroo images copied from openImages).
    Running the retrainer to produce a new trained model.
    Using the new trained model to classify new images.
    For those thinking of doing the same thing - some notes on making it go.

Conclusion

The future

    Voice recognition.

Cool links

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