0. Experimental environment settings.

## Run the following code before starting the experiment.

    conda env create -f requirements.yaml
    
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1. Training / Test

## Run the following code for training / test.

    python main.py --tol 1e-3 --epochs 10 --batch_size 64 --hidden_size 76 

## If you want to train and test in a different environmental setting, it can be done by changing the parsers below.

[ parser ]      [ Description of parser ]
 --tol          : DOPRI-5 error tolerance
 --epochs       : Number of epoch
 --batch_size   : Batch size
 --hidden_size  : Size of hidden vector

## We release code for image classification tasks (CIFAR-10 dataset).

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2. Test (Only)

## If you want to evaluate it quickly, run the following code :

    python test.py --path './model/10.pt'

[ parser ]      [ Description of parser ]
 --path         : The path where checkpoint exists



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3. Oher codes

[ code ]        [ Description of code ]
main.py         : Code for training and testing.
models.py       : Our model 
utils.py        : Modules required during training
test.py         : Code for testing.

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4. Checkpoint

## We provide checkpoint of trained BFNO-NODE(our model), which are located in the ./model folder.

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