SeeFood

SeeFood is a food image classifier trained on image data of hot dogs and other food items to classify whether food in the picture is a hotdog or not.This project was inspired by the seefood App in HBO's Silicon Valley show . To build this I used a pre-trained Densenet121 model and applied transfer learning with the help of GPUs in cloud to fine tune the model to the hot-dogs dataset. This was made using PyTorch with Python. Made into a web application with Flask

Deployed on Heroku : Gradio demo

Model

• Classifier built upon a densenet121 model using transfer learning using PyTorch.
• Dataset , Hot Dog - Not Hot Dog from kaggle
• The model gave a accuracy of 87% unseen test data.

Run locally

Clone the repo

git clone https://github.com/vinayakj02/SEEFOOD-classifier.git

Change the working directory to SEEFOOD-classifier

cd SEEFOOD-classifier

Install the requirements

python3 -m pip install -r requirements.txt

Start the server

python3 app.py

Run with docker

Clone the repo

git clone https://github.com/vinayakj02/SEEFOOD-classifier.git

Change the working directory to SEEFOOD-classifier

cd SEEFOOD-classifier

Build the image

sudo docker build -t seefood .

Run the container

sudo docker run -it -d -p 7000:7000  seefood

Visit localhost:7000 to view the site.