Building a Deep Learning Flower Classifier

Building a Deep Learning Flower Classifier

Building a Deep Learning Flower Classifier. How I built a Web App that can classify from five different flowers based on the uploaded image.

Introduction

After spending some time looking at Deep Learning with TensorFlow and training some models on my own, I decided to make the model meaningful. Making a model and training it is one thing, but deploying it and creating something that even people with no experience can work with is another.

That’s when I discovered Streamlit, it is an open source framework that let’s you create data apps in pure Python. I had little to no experience in creating Web Apps using JavaScript frameworks, but using Streamlit, I could do that in Python.

You can learn more about Streamlit on their [website_](https://www.streamlit.io/)._

You can find the source code on my GitHub. Any feedback and suggestions are welcome.

Libraries used

tensorflow==2.2.0                       
streamlit==0.65.2                       
numpy==1.19.1                       
opencv_python==4.4.0.42                       
Pillow==7.2.0

The Model

Due to the computation power required by Deep Learning, creating and training a new model from scratch would be hard and time consuming. So I used a concept called Transfer Learning.

deep-learning machine-learning python streamlit tensorflow

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