The transfer learning experience with VGG16 and Cifar 10 dataset

The transfer learning experience with VGG16 and Cifar 10 dataset

In this blog, I’m going to talk about how I have gotten an accuracy greater than 88% (92% epoch 22) with Cifar-10 using transfer learning, I used VGG16 and I applied a very low constant learning rate and I implemented the function upsampling to get more data points for processing

Abstract In this blog, I’m going to talk about how I have gotten an accuracy greater than 88% (92% epoch 22) with Cifar-10 using transfer learning, I used VGG16 and I applied a very low constant learning rate and I implemented the function upsampling to get more data points for processing

Introduction Nowadays we are having a very good time for machine learning, we have a lot of famous models with great results that make predictions fast and with high accuracy. Consequently, we should use those tools to apply in our daily predictions focusing on the goals of our models and not only in the footprint of it. For this reason, we need to understand our dataset and try to apply the correct model, doing the necessary preprocessing of the dataset and the corrections in those famous model if it’s necessary.

Materials and Methods

In this practice I used:

Keras 1.12

Colab using GPU: For me is the best option (cost-effective) that I have seen to compile and train a model. It’s Jupyter saving in drive or uploading to GitHub.

VGG16 model: I have chosen this model because I thought in the time that I spent if I used a deeper model like dense121 or resnet50 and the accuracy of this model is not bad and the results in this practice were very nice, I compared with dense121 and the accuracy difference between them is only 0.08%.

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supervised-learning learning-rate transfer-learning

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