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DINO-MC: Self-supervised Contrastive Learning for Remote Sensing Imagery with Multi-sized Local Crops

PyTorch implementation and pretrained models for DINO-MC and DINO-TP. For details, please see <a href="https://arxiv.org/abs/2303.06670">our paper</a>.

Pretrained models

Our models are pre-trained on SeCo-100K, and we list their k-nn and linear probing accuracy on EuroSAT. You can download the full checkpoint of pre-trained model with training infomation as well as weights of teacher and student networks used for the downstream tasks.

Training

Our codes refer to <a href="https://github.com/facebookresearch/dino">DINO</a> and <a href="https://github.com/ServiceNow/seasonal-contrast">SeCo</a>. If you want to pre-train DINO-MC based on your datasets:

python run_with_submitit.py --nodes 1 --ngpus 4 --arch vit_small --data_mode mc --data_path /path/to/dataset/train --output_dir /path/to/saving_dir

Fine-tuning

After pre-training, you can evaluate the representations on three end-to-end fine-tuning downstream tasks.

Citation

If you find this repository useful, please consider giving a star :star: and citation:

@misc{wanyan2023dinomc,
      title={DINO-MC: Self-supervised Contrastive Learning for Remote Sensing Imagery with Multi-sized Local Crops}, 
      author={Xinye Wanyan and Sachith Seneviratne and Shuchang Shen and Michael Kirley},
      year={2023},
      eprint={2303.06670},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}