Machine learning Tips for Audio, Image and Video Analysis
Original post :-
http://www.data-mania.com/blog/machine-learning-tips-for-image-video-and-audio/
By Lillian Peirson
Post is as follows : -
http://www.data-mania.com/blog/machine-learning-tips-for-image-video-and-audio/
By Lillian Peirson
Post is as follows : -
Neural networks are great in image,
video, and audio machine learning problems. For example, if you have an
image classification task, you can use convolutional neural nets.
First, you’ll need to normalize your image, and then downsample it to a
smaller size. Usually 16 – 64 pixels for each dimension is good.
After that you can build a simple
convolutional net to learn from these downsampled images. The most
important hyperparameter is the learning rate – tune it first. After
that you can play around with changing layer sizes, the convolutional
layer kernel, and pooling sizes. Try adding more layers and activation
functions. Definitely try using the dropout method.
If your dataset is not very large, use data augmentation.
Usually if you rotate your image or move it by a few pixels
horizontally or vertically, the class doesn’t change, right? Sometimes
you can even make a mirror image! Data augmentation can help you avoid
some overfitting, making it possible to try an even bigger net. Finally,
if you need a little better quality, you should definitely try to build
several models with similar hyperparameters and then build a voting
classifier on top of them.
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