Note: The scripts used in this project are only designed to work with GPUs if Apple Silicon is present.
Use conda to create new virtual environment:
conda create --name sys843_project
Activate virtual environment:
conda activate sys843_project
Install Python at version 3.12 to ensure compability with Pytorch libraries:
conda install python=3.12
Next, install Pythorch libraries:
pip install torch torchvision
Then install timm (library that has various pre-trained models):
pip install timm
Then install matplotlib to plot graphs:
pip install matplotlib
Follow instalation of coremltools as described in Installing Core ML Tools guide:
pip install coremltools
You can run the Python file train_hyperparameter_tuning.py under the virtual environment by making sure the environment is active and then:
python train_hyperparameter_tuning.py --model fastvit_t8
To find a set of optiomal hyperparameters for FastViT-T8.
You can run the Python file train.py under the virtual environment by making sure the environment is active and then:
python train.py --model fastvit_t8
To train FastViT-T8.
You can run the Python file train_plots.py under the virtual environment by making sure the environment is active and then:
python train_plots.py
You can run the Python file convert_coreml.py under the virtual environment by making sure the environment is active and then:
python convert_coreml.py --model fastvit_t8
To convert the model FastViT-T8 to Core ML.