sirreajohn / F00d_recognition

Food recognition using streamlit with inception v3 backend

F00d_recognition

Food recognition using streamlit with inception v3 backend

Deployed at Streamlit

Every one likes food! This deployment recognizes 11 different classes of food using a SOTA Inception V3 Transfer Learning.\n

Inception V3

• The paper for Inception can be found here

• The paper implementation using pytorch can be found here

• Inception-v3 is a convolutional neural network architecture from the Inception family that makes several improvements including using

  • Label Smoothing,
  • Factorized 7 x 7 convolutions,\n and the use of an auxiliary classifer to propagate label information lower down the network (along with the use of batch normalization for layers in the sidehead).

• Training on 16,600 images yielded 90% accuracy on train and 76% accuracy on validation. over 50 epochs!

• This model is saved and used later

Model Architecture

Dataset Details and Classes

Data consists of 1.1GB of 16,600 images of different categories of food. the categories of food that can be classified are

- Bread
- Dairy Product
- Dessert
- Egg
- Fried Food
- Meat
- Noodles-pasta
- Rice
- Seafood
- Soup
- Vegetable-fruit

Dataset is obtained from kaggle