Basic DL pipeline for CV competition

For Upstage CV competition

Dataset

Upstage CV competition dataset(document image classification)

Quick setup

# clone project
git clone https://github.com/DimensionSTP/upstage-cv.git
cd upstage-cv

# [OPTIONAL] create conda environment
conda create -n myenv python=3.10 -y
conda activate myenv

# install requirements
pip install -r requirements.txt

.env file setting

PROJECT_DIR={PROJECT_DIR}
CONNECTED_DIR={CONNECTED_DIR}
DEVICES={DEVICES}
HF_HOME={HF_HOME}
USER_NAME={USER_NAME}

Model Hyper-Parameters Tuning

• end-to-end

python main.py mode=tune is_tuned=untuned num_trials={num_trials}

Training

• end-to-end

python main.py mode=train is_tuned={tuned or untuned} num_trials={num_trials}

Test

• end-to-end

python main.py mode=test is_tuned={tuned or untuned} num_trials={num_trials} epoch={ckpt epoch}

Prediction

• end-to-end

python main.py mode=predict is_tuned={tuned or untuned} num_trials={num_trials} epoch={ckpt epoch}

Examples of shell scipts

• train

bash scripts/train.sh

• predict

bash scripts/predict.sh

If you want to change main config, use --config-name={config_name}.

If you want to use main config as huggingface, set modality={modality}, upload_user={upload_user}, model_type={model_type}.

Also, you can use --multirun option.

You can set additional arguments through the command line.