Create a python virtual environment. Here is an example using conda.
conda create -n nutriseg python=3.10
conda activate nutriseg
Install torch and other requirements.
python3 -m pip install torch==1.13.1 torchvision==0.14.1 --extra-index-url https://download.pytorch.org/whl/cu113
python3 -m pip install -r OpenSeeD/requirements.txt
conda install -c conda-forge --file requirements.txt -y
conda run python3 -m pip install 'git+https://github.com/MaureenZOU/detectron2-xyz.git'
Follow the instruction from [Google Nutrition-5k]{https://github.com/google-research-datasets/Nutrition5k}.
Food region masks are generated using OpenSeeD instance segmentation module. Run the following command to generate the food region masks in prior to training. You may need to change the system paths to absolute paths in script/generate_openseed_mask.py.
CUDA_VISIBLE_DEVICES=0 python3 script/generate_openseed_mask.py <dataset_directory>
After running the command food region mask mask.pt should be created under every image directory.
Change the configuration file DATA.IMGS_DIR DATA.METADATAS_PATH DATA.SPLITS_TEST_PATH DATA.SPLITS_TRAIN_PATH should corresponds to the where the Nutrition-5k dataset was saved.
Train the model using the following command. The final model should be saved at models/base.pt
CUDA_VISIBLE_DEVICES=0 python3 src/main.py --cfg config/models/base.yaml
Use the following command to evaluate a pre-trained model.
CUDA_VISIBLE_DEVICES=0 python3 src/evaluate.py --cfg <path_to_model_config>