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[D] Is it a good idea to use VGG16 for a image classification mobile app? (alien.top)

submitted 10 months ago by Rhet98@alien.top to c/machinelearning@academy.garden

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I'm new to image classification and ML and this is going to be my first project on those topics. I'm considering using VGG16 because I saw some studies showing that it has a generally great accuracy score (80-95%) but I'm worried that the model might not be fast enough or the app file size might get massive if I want the app to be usable without internet connection.

What do you guys think?

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[–] shubham0204_dev@alien.top 1 points 10 months ago

You may use MobileNet models as they use separable convolutions, which have lesser parameters and execution time than simple/regular convolutions. Moreover, MobileNets are easy to train and setup (tf.keras.applications.* has a pre-trained model) and can be used as a backbone model for fine-tuning on datasets other than the ImageNet.

Further, you can also explore quantization and weight pruning. These are some techniques that can be used to optimize models to have a smaller memory footprint and smaller execution time on embedded devices.