Notebooks and code

The ipynb files are as follows:

1. RPC.ipynb: Simple image classification with fastai library, an introduction
2. RPC-Pixel-sim.ipynb: Image classification with Pixel Similarity Approach
3. RPC SGD.ipynb: Image classification with Stochastic Gradient Descent and Cross Entropy Loss function
4. RPC-Learning Rate, Progressive Resizing Approach.ipynb : Image Classification, state of the art training with learning rate finder, progressing resizing approach.
5. Text Sentiment Classification(Covid-19 Tweets).ipynb : Text Classification in just 20 lines of code

The Folder RockPaperScissorsClassification contains:

1. An interactive web app made with streamlit with the ability to run image classification on any uploaded image.
2. Contanerising the streamlit app with Docker.

The streamlit web app

The web app built through streamlit is now live. The code is located in RockPaperScissorsClassification folder.

Screenshot of the app in action

Steps to build and run the Streamlit web app

1. Install Docker on your machine.
2. In the root directory of the terminal, run to build the image:

docker build --tag rps:1.0 .

3. Lastly, run the app with:

docker run --publish 8501:8501 -it rps:1.0

Open your browser and go to http://localhost:8501/ and play with the app!