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.
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
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
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% |
This project is licensed under the MIT License.