The following packages are required:

• PyTorch
• Pandas
• scikit-learn
• matplotlib
• tqdm
• numpy
• kaggle

For PyTorch, please follow the instructions here (for pip or conda): https://pytorch.org/get-started/locally/

pip install numpy pandas matplotlib scikit-learn tqdm kaggle torch torchvision torchaudio

There are a few ways to obtain the Food-101 dataset. One is from the author's website here. Another option is to download it from Kaggle here.

Alternatively, if you decide to run the Notebook, it already contains code to download the zip from either the dataset website or from kaggle. For the kaggle option via the Notebook, you will need a kaggle.json file in the same directory as the notebook. More info here.

The repository contains both Jupyter notebooks and Python scripts. The Jupyter notebooks have commands to execute any additional libraries that may be required for installation. Included is also the experiment code that was run for training the Food-11 dataset as well in food11-training.ipynb. All the notebooks can also be run in Google Colab as-is.

The python scripts have the same code as the notebooks, just more cleaned up and better organized. You will get the same results regardless of which option you use.

This script will execute the entire training pipeline for all 5 models, both pretrained and non-pretrained. Two CSV files and pickle files will be saved at the end with the results on the test set and model metrics respectively. All the trained models will be stored in the Trained_Models folder in the same directory as the script.

python train_classifier.py

You will need to set the input image path in the main() function. The script will print out the predicted class.

python predict_image.py

The jupyter notebook plots.ipynb contains the plotting code to generate plots for training and testing loss and accuracy for both pretrained and non-pretrained models. You will need to specify the path to the pickle files for the pretrained and non-pretrained model metrics in order to generate the figures.

You can download the pickle files obtained from the training experiment here: https://drive.google.com/file/d/1cp-0ytNgz7hh4NbRV1jCWYsJ46QIf9YE/view?usp=sharing

The following packages are required:

• PyTorch
• Pillow
• matplotlib
• tqdm
• datasets
• diffusers
• accelerate

For up-to-date PyTorch instructions, please see here: https://pytorch.org/get-started/locally/

pip install torch torchvision torchaudio Pillow matplotlib tqdm datasets diffusers accelerate

Similar to classification, both Jupyter notebooks train_diffusion.ipynb, pizza_gen.ipynb and a script train_diffusion.py are provided.

This script will execute the training pipeline for the diffusion model for 100 epochs. The trained model will be saved as pizza-diffusion.pt in the same directory as the script or notebook. The model directory containing the model config, weights, and logs will be created based on the configuration specified in TrainingConfig.

Output images will be saved in the configured output directory at the path: output_dir/samples/. Model configuration can be changed in the TrainingConfig class.

python train_diffusion.py

This script will generate a Pizza image. You will need to specify the model directory path in the code.

python generate_pizza_image.py

The trained model is available at this link: https://drive.google.com/file/d/1FHntB14ZtXYLPNTH2J693Z0NEzxheiqe/view?usp=sharing