The project consists of two parts. 1. Training the model 2. Interative visualization of results 1. Traing the model: Model takes in 10 classes as input. The following are the 10 classes: [['alarm_clock', 'tennis_racquet', 'cloud', 'eye', 'sword', 'book', 'laptop', 'star','spoon', 'coffee_cup' ] Model is trained using the nparray dataset downloaded from Google Quick Draw Dataset Model is a Simple CNN. Trained Model is now converted to a Tensorflowjs model. 2. Interactive Visualization of results: An app is developed: to detect the results. Javascript is used to for a interactive canvas image to be drawn and the results are posted right after detection. Steps to run the project: 1. Download WebPage_Model.zip 2. Create an environments with all the dependencies installed from all_requirements.txt 3. This code already contains a trained model which is converted to tensorflowjs 4. Now from the environment, run app.py. It should run the webpage on the localhost:5000 5. If it doesn't access the file. Run the whole training dataset too: 1. Create an environment for the project to be run 2. Install all the requirements from all_requirements.txt 3. Run the ML_Code.ipynb file 4. I am trying to convert the model to a tensorflowjs, it wasn't working on my machine. So I have converted it on Google Colab. 5. Download the webpage folder and unzip it. 6. Now from the environment, run app.py in the unzipped folder file 7. Access: localhost:5000 8. The program runs