Reproduce our results for PlantCLEF2022 challenge

• Make datasets, look and adapt make_dataset.sh, and then run the command.
• Visualize the dataset, use visualize_observation.py.
• Download MAE pretrained model ViT-Large and put it in checkpoint directory.
• Finetune the model in PlantCLEF2022 dataset, use finetune.sh.
• Test and predict the results, use test.sh, you can see a new directory in results directory.
• Make the submission result, use make_official_result.sh you can see a new .csv file in results directory.

Our results

Name	MA-MRR	Our model
official run 8: epoch 80 Single_high	0.62692	No
late submission epoch 100 Single_high	0.63668	Google Driver
late submission epoch 100 multi_sorted	0.64079	same with last line

Citation

@inproceedings{xu2022transfer,
  title={Transfer learning with self-supervised vision transformer for large-scale plant identification},
  author={Xu, Mingle and Yoon, Sook and Jeong, Yongchae and Lee, Jaesu and Park, Dong Sun},
  booktitle={International conference of the cross-language evaluation forum for European languages (Springer;)},
  pages={2253--2261},
  year={2022}
}
@article{xu2022transfer,
  title={Transfer learning for versatile plant disease recognition with limited data},
  author={Xu, Mingle and Yoon, Sook and Jeong, Yongchae and Park, Dong Sun},
  journal={Frontiers in Plant Science},
  volume={13},
  pages={4506},
  year={2022},
  publisher={Frontiers}
}

Acknowledgement

Our model is heavily based on MAE.