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I've seen many examples where networks are trained on thousands or tens of thousands. The most common example is digit recognition with the MNIST dataset. This
by dbecker 10y ago
I've seen many examples where networks are trained on thousands or tens of thousands.
The most common example is digit recognition with the MNIST dataset. This is a common problem given to beginners, and even many beginners to CNN's achieve human-level accuracy. That dataset is in the tens of thousands.
there should be a push towards adapting CNNs to calculate/predict how the object might look under different conditions
Data augmentation like rotations, horizontal flipping and random cropping are a widespread practice.
- cynicaldevil 10y agoI see. Could you link some articles about these techniques?
- dbecker 10y agoI don't have great links for this, but for something less technical, you might look at the blog posts from Kaggle competition winners. Here are a couple examples http://benanne.github.io/2015/03/17/plankton.html http://benanne.github.io/2015/03/17/plankton.html http://blog.kaggle.com/2016/04/13/diagnosing-heart-diseases-with-deep-neural-networks-2nd-place-ira-korshunova/ http://blog.kaggle.com/2016/04/13/diagnosing-heart-diseases-... or check out what's available in a deep learning library like keras http://keras.io/preprocessing/image/ http://keras.io/preprocessing/image/ Sorry I don't have better academic references.