Installation

This section provides a tutorial on building a working environment for LibFewShot from scratch.

Get the LibFewShot library

Use the following command to get LibFewShot:

cd ~
git clone https://github.com/RL-VIG/LibFewShot.git

Configure the LibFewShot environment

The environment can be configured in any of the following ways:

1. conda(recommend)

   cd <path-to-LibFewShot> # cd in `LibFewShot` directory
   conda env create -f requirements.yaml
   

2. pip

   cd <path-to-LibFewShot> # cd in `LibFewShot` directory
   pip install -r requirements.txt
   

3. or whatever works for you as long as the following package version conditions are meet:

   numpy >= 1.19.5
   pandas >= 1.1.5
   Pillow >= 8.1.2
   PyYAML >= 5.4.1
   scikit-learn >= 0.24.1
   scipy >= 1.5.4
   tensorboard >= 2.4.1
   torch >= 1.5.0
   torchvision >= 0.6.0
   python >= 3.6.0
   

Test the installation

1. set the config as follows in run_trainer.py:

   config = Config("./config/test_install.yaml").get_config_dict()
   

2. modify data_root in config/headers/data.yaml to the path of the dataset to be used.

3. run code

   python run_trainer.py
   

4. If the first output is correct, it means that LibFewShot has been successfully installed.

Next

For model training and code modification, please see the train/test methods already integrated in LibFewShot and other sections of the tutorial.