Image classification is the task of assigning a label to an image (classifying an image as one thing or another). Here we start you off with a basic example:
- Dogs and Cats:
Trains a model to classify an image as a cat or a dog using 25,000 images
(12,500 Cats and 12,500 Dogs).
- More Pets:
Trains a model to classify an image as a rabbit, mouse, hamster, fish, lizard, or snake.
- Poison Ivy:
Trains a model to classify a plant image as a type of poison ivy (or not).
- Azure Notebooks: While Notebooks are in mode (beta), some features might be buggy or not available. If you are having trouble with dependencies in your JLab environment. Try waiting a few minutes, restarting the Python 3.6 kernel, and trying again.
- Training Data: In order for an image classifier to identify a particular category, it must have trained on
images labeled as such. For example, if a model was trained on dogs and cats, and it is shown a plant, it will
identify that plant as either a dog or cat. To build an image classification model that identifies plants or other
types of objects, you would need to retrain the model, using labeled images of the type you want.
- Model Runtime: The out-of-the box model takes a long time to train on CPU. At this moment, Azure doesn't support GPU training on notebook instances. So if you need that, please check out some of these Google Colab examples.
- Model Size: In addition to the tips above, try using the model in the
function if you are worried about the size of the resulting model. This may also impact the classification accuracy of the model.
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Also checkout Turi Create's documentation on image classification basics.