Bi-FRN-extension

Code relaese for Bi-Directional Ensemble Network for Few-Shot Fine-Grained Classification.

Code environment

• You can create a conda environment with the correct dependencies using the following command lines:

  conda env create -f environment.yml
  conda activate BiFRN

Dataset

The official link of CUB-200-2011 is here. The preprocessing of the cropped CUB-200-2011 is the same as FRN, but the categories of train, val, and test follows split.txt. And then move the processed dataset to directory ./data.

Train

• To train Bi-FRN on CUB_fewshot_cropped with Conv-4 backbone under the 1/5-shot setting, run the following command lines:

  cd experiments/CUB_fewshot_cropped/BiFRN_snapshot/Conv-4
  chmod +x ./train.sh
  ./train.sh

• For ResNet-12 backbone, run the following command lines:

  cd experiments/CUB_fewshot_cropped/BiFRN_snapshot/ResNet-12
  chmod +x ./train.sh
  ./train.sh

Test

    cd experiments/CUB_fewshot_cropped/BiFRN_sanpshot/Conv-4
    python ./test.py
    
    cd experiments/CUB_fewshot_cropped/BiFRN_snapshot/ResNet-12
    python ./test.py

References

Thanks to Davis, Phil and Yassine, for the preliminary implementations.

Contact

Thanks for your attention! If you have any suggestion or question, you can leave a message here or contact us directly:

• jijie@lut.edu.cn