Simple use

Training

Write your training script in train.py like:

from netspresso_trainer import train_cli

if __name__ == '__main__':
    logging_dir = train_cli()
    print(f"Training results are saved at: {logging_dir}")

Then, train your model with your own configuraiton:

python train.py\
  --data config/data/huggingface/beans.yaml\
  --augmentation config/augmentation/classification.yaml\
  --model config/model/resnet/resnet50-classification.yaml\
  --training config/training.yaml\
  --logging config/logging.yaml\
  --environment config/environment.yaml

Or you can start NetsPresso Trainer by just executing console script which has same feature.

netspresso-train\
  --data config/data/huggingface/beans.yaml\
  --augmentation config/augmentation/classification.yaml\
  --model config/model/resnet/resnet50-classification.yaml\
  --training config/training.yaml\
  --logging config/logging.yaml\
  --environment config/environment.yaml

Please refer to scripts/example_train.sh.

NetsPresso Trainer is compatible with NetsPresso service. We provide NetsPresso Trainer tutorial that contains whole procedure from model train to model compression and benchmark. Please refer to our colab tutorial.

Evaluation

Write your evaluation script in evaluation.py like:

from netspresso_trainer import evaluation_cli

if __name__ == '__main__':
    logging_dir = evaluation_cli()

    print(f"Evaluation results are saved at: {logging_dir}")

Then, evaluate your model with your own configuraiton:

python evaluation.py\
  --data config/data/huggingface/beans.yaml\
  --augmentation config/augmentation/classification.yaml\
  --model config/model/resnet/resnet50-classification.yaml\
  --logging config/logging.yaml\
  --environment config/environment.yaml

Inference

Write your inference script in inference.py like:

from netspresso_trainer import inference_cli

if __name__ == '__main__':
    logging_dir = inference_cli()

    print(f"Inference results are saved at: {logging_dir}")

Then, inference your dataset:

python inference.py\
  --data config/data/huggingface/beans.yaml\
  --augmentation config/augmentation/classification.yaml\
  --model config/model/resnet/resnet50-classification.yaml\
  --logging config/logging.yaml\
  --environment config/environment.yaml