Make sure to have Python3 installed. If you would like to use a gpu, install CUDA and CUDNN. Next, run:

pip install -U Cython==0.29.15 numpy==1.17.4
pip install -r requirements.txt

Then install either requirements-cpu.txt or requirements-gpu.txt depending on your device.

To create data to train an object detector on, run:

PYTHONPATH=$(pwd) data_generation/create_detection_data.py 

To create data to train a classifier, run:

PYTHONPATH=$(pwd) data_generation/create_clf_data.py 

Edit the data_generation/config.yaml to adjust the amount of data to create.

See example model configurations in models/configs for inspiration on a model architecture. Check out torchvision for all the possibilities. An example training command is:

PYTHONPATH=$(pwd) train/train_clf.py \
    --model_config configs/resnet18.yaml 

See example model configurations in models/configs for inspiration on a model architecture.

PYTHONPATH=$(pwd) train/train_det.py \
    --model_config configs/vovnet-det.yaml

Inference on an image will utilize both the classifier and object detector model. The inference script can be run as follows:

PYTHONPATH=$(pwd) inference/find_targets.py \
    --image_path /path/to/image \
    --visualization_dir /path/to/save

The command above will visualize and save the models' predictions. See inference/find_targets.py for the full list of available arguments.

Testing is performed by running tox at the repo root. The test in this repo consist of python unittests and doctests.

Updating submodules: git submodule update --remote --merge

• Devise a way to be able to create detection, classification, and feature data in separate independently.

• Finalize training pipelines.

• Finish inference pipeline.

• Work on Box Dev integration if platform available.

• Weights & Biases integration once project is granted.

• GitHub Actions CI and unittests