Check out how good is your selfie here(link will be available soon)!
In this article, we’ll see how to create a model (including it’s API) for classifying a selfie as ‘ugly’, ‘bad’,’normal’, ‘good’ and ‘excellent’ .
The modified dataset contains 46000+ different selfies of people all around the world which are labelled as ‘ugly’, ‘bad’,’normal’, ‘good’, ‘excellent’. The labelling is done based on the likes/comments received by the selfie on the platform where it was uploaded.
Note: We won’t code a single line! 😈
We’ll use Deep Learning Studio by Deepcognition.ai for creating and developing our model and API.
If you aren’t familiar with Deep Learning Studio, do checkout my article on it.
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Let’s get started.
1. Uploading the dataset on Deep Learning Studio
The dataset is greater than 1GB so you need to copy the data directly from your PC to Workspace as shown below:
It may take a while to extract the data inside ‘SelfieData’, so be patient.
We’ll use pre-trained WideResNet as the model architecture. Deep Learning studio makes this very easy for us. Just drag-drop ‘WideResNet’ from Pre-trained models(left pane) and we are done! Click on WideResNet layer and set trainable to 25. This means that we just want to train top 25% of the layers of WideResNet.
Training the model:
We got a decent accuracy just by using pre-trained WideResNet.
If you want to have a glance over my hyperparameters, they are here:
Let’s Deploy the Rocket!
After you click on ‘Deploy’, a link will be generated which will take you this :
Let’s see output Aishwarya Rai’s photo😁.
Thanks a lot guys for your time to read this article and have fun checking your selfie 😜
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Happy Selfies 😛.
Source: Deep Learning on Medium