| --- |
| license: cc-by-4.0 |
| tags: |
| - remote-sensing |
| - earth-observation |
| - self-supervised-learning |
| - ssl4eo-s12 |
| - sentinel-2 |
| - vit-small |
| datasets: |
| - wangyi111/SSL4EO-S12 |
| base_model: wangyi111/SSL4EO-S12 |
| --- |
| |
| # SSL4EO-S12 — ViT-S/16 pre-trained with Data2Vec |
|
|
| Pre-trained backbone from the [SSL4EO-S12](https://github.com/zhu-xlab/SSL4EO-S12) project. |
|
|
| | Property | Value | |
| |---|---| |
| | SSL method | **Data2Vec** | |
| | Architecture | ViT-S/16 | |
| | Input | S2-L1C 13 bands | |
| | Pre-training epochs | 100 | |
| | Normalisation | clip [0, 1] by dividing 10 000 | |
| | Checkpoint | `B13_vits16_data2vec_0099_ckpt.pth` | |
|
|
| ## Load the backbone |
|
|
| ```python |
| import torch |
| import timm |
| |
| model = timm.create_model("vit_small_patch16_224", num_classes=0) |
| state = torch.load("B13_vits16_data2vec_0099_ckpt.pth", map_location="cpu") |
| |
| # key may be "model", "state_dict", or "teacher" depending on the method |
| backbone_state = state.get("model", state.get("state_dict", state)) |
| model.load_state_dict(backbone_state, strict=False) |
| model.eval() |
| ``` |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{wang2022ssl4eo, |
| title={SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset |
| for Self-Supervised Learning in Earth Observation}, |
| author={Wang, Yi and Braham, Nassim Ait Ali and Xiong, Zhitong and |
| Liu, Chenying and Albrecht, Conrad M and Zhu, Xiao Xiang}, |
| journal={arXiv preprint arXiv:2211.07044}, |
| year={2022} |
| } |
| ``` |
|
|
