ismael-deka/food-classification-dl

A Deep-Learning model for image classification of a set of 10 food items. It uses the ResNet-50 pre-trained on ImageNet database.

Food Classification Deep-Learning Model

Introduction

This project is a deep learning model for image classification of a set of 10 food items. It uses the ResNet-50 pre-trained on ImageNet database.

Dataset

The dataset this model was trained on is composed of between 3000 images of a 10 standalone food items scraped from various sources (Pinterest, tumblr, reddit, etc). Each category is composed of 300 images to keep the dataset balanced. Categories of food included in this dataset are:

• Cheeseburger
• Cake
• Cookie
• Fries
• Hotdog
• Pizza
• Salad
• Shrimp
• Steak
• Sushi

The Image dataset is available for download here. Make sure to save the dataset folder in the same directory as train.py, and test.py

Requirements

This project requires the following packages:

• PyTorch 2.0.0
• TorchVision 0.15.1
• Matplotlib 3.5.1
• scikit-learn 1.1.2
• Numpy 1.23.5

Results

After training the model for 10 epochs with a batch size of 32 and a learning rate of 0.001, we achieved an accuracy of 93.33% on the train set, 93.67% on the validation set, and 92.83% on the test set(as of 04/19/2023). Class-wise accuracies following test are as follows:

Class	Accuracy
Burger	98.18%
Cake	81.48%
Cookie	95.31%
Fries	91.53%
Hotdog	95.31%
Pizza	89.36%
Salad	96.83%
Shrimp	90.91%
Steak	92.96%
Sushi	94.74%

License

This project is licensed under the MIT License.