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On The Right Food - Training

The goal of this repository is to create a prediction model using a subset of preprocessed images described by the annotations.json file.

Here we describe both the preprocessing and training methods that we conducted on a virtual machine (VM) on the Google Cloud Platform.

To set up your VM you can clone and install this repository directly to your VM.

1. Installation

We recommend to set up a virtual environment.

  1. Set the local python version 3.9.8.

    pyenv local 3.9.8

  2. Create and activate the virtual ennvironment

    python -m venv .venv

    source .venv/bin/activate

Make sure you use a pip version <= 23.0.1. Otherwise the installation of detectron2 will fail!

python -m pip install --upgrade pip==23.0.1

  1. Install the required python packages:

    python -m pip install -r requirements.txt

  2. Install detectron2:

    python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'

2. Preprocessing

To modify the images upload the original set of images (we used the dataset from the AICrowd Food Recognition Competition) to /home/user/on-the-right-food-training/data/images/ and the annotations.json file to the /home/user/on-the-right-food-training/data/annotations.json folder on your VM. You can change the paths in settings_preprocessing.py.

  1. bboxcorrection.py checks and corrects whether bounding boxes around annotated images are fitting the segmentation boundaries.

  2. file_comb_random.py combines four images into one and creates in total 10000 random files (the number of files can be changed in the script) from the original dataset.

  3. annotations_3.py selects files with 3 or more different food categories.

  4. As a very particular task for the food recognition training dataset simple_label.py was created to reduce different labels for obviously similar classes of food.

  5. subset_train_val.py adjusts the labelling of the training and validation datasets so that they have the same categories present after training dataset subsetting. Each of this scripts returns a new .json files with annotations that should be used in trainer_gcp.py.

3. Training

  1. Run trainer_gcp.py.

    When the training is completed you can find the prediction model file model_final.pth in the /home/user/on-the-right-food-training/output/ folder.

    If you want to add a timestamp to your model name, uncomment the TIMESTAMP block in trainer_gcp-py.

    If you want to dump the config values as yaml, uncomment the YAML block in trainer_gcp-py.

    If you want to archive your training output, uncomment the ARCHIVE block in trainer_gcp-py.

4. Transfer to your local machine

  1. Now you can follow the instructions described under on-the-right-food#2-web-app-installation.