Masked Autoencoders (MAE): Train on Kaggle

This is an example of training MAE on Kaggle

Environments

• Kaggle: enabled GPU T4x2
• Python: 3.10
• Datasets: mae-v1 (8,025 images) or ImageNet1K (1,281,167 images)
• Log to wandb.ai

How to run

• Import ipynb mae-kaggle.ipynb into Kaggle.
• Choose one of these datasets: mae-v1 (8,025 images) or ImageNet1K (1,281,167 images)
• Use your own Wandb API token
• Update hyperparameters or leave them as default
• Run all and enjoy!

Key Takeaways

• I do not guarantee to reproduce the same results as in the paper (it takes too much time to train the entire ImageNet1K)
• I notice that we can set the batch size up to 130 per GPU (that means 260 in total) when training the base model (mae_vit_base_patch16)

Acknowledgement

This repository is inspired by MAE (Original paper Masked Autoencoders Are Scalable Vision Learners )

@inproceedings{he2022masked,
  title={Masked autoencoders are scalable vision learners},
  author={He, Kaiming and Chen, Xinlei and Xie, Saining and Li, Yanghao and Doll{\'a}r, Piotr and Girshick, Ross},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={16000--16009},
  year={2022}
}