metadata
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 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
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
@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}
}