Classification: 13 Kenyan food types

This is the second project of the Opencv University course "Deep Learning with PyTorch". It focuses on classifying images of 13 different Kenyan food classes.

Introduction

Classification in computer vision is a task where the objective is to determine the class that the image belongs to. This project focuses on classifying images from 13 classes.

Data

The dataset, containing 8174 images of various sizes, was split into 6536 training images and 1638 validation images. These images are categorized into the following classes:

Bhaji, Chapati, Githeri, Kachumbari, Kukuchoma, Mandazi, Masalachips, Matoke, Mukimo, Nyamachoma, Pilau, Sukumawiki, Ugali

The method used

Fine-tuning of a ResNet-50 backbone with a linear classifier using PyTorch Lightning to gain experience with high-level deep learning training.

• Various augmentations techniques were used to try to improve generalization:

  • Color jitter
  • Conversion to gray
  • Horizontal and vertical flips
  • Random shifting and rotation
  • Elastic transformations
  • Grid distortion

• The loss function used was Cross-Entropy.

• An SGD optimizer with weight decay.

• A learning rate scheduler that implements the 1-cycle policy. It adjusts the learning rate from an initial rate to a maximum, then decreases it to a much lower minimum.

Discussion

Training this model for ~200 epochs resulted in an accuracy of 75.2% on the test set.

See the notebook.