Feature Extraction
Transformers
Safetensors
English
remote-sensing
earth-observation
self-supervised-learning
sentinel-2
sentinel-1
multispectral
sar
vision
ssl4eo
mae
moco
dino
data2vec
vit
resnet
Instructions to use BiliSakura/SSL4EO-S12-transformers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BiliSakura/SSL4EO-S12-transformers with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="BiliSakura/SSL4EO-S12-transformers")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("BiliSakura/SSL4EO-S12-transformers", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 577 Bytes
2999792 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | {
"architectures": [
"SSL4EOData2VecModel"
],
"drop_path_rate": 0.0,
"dtype": "float32",
"hidden_size": 384,
"id2label": {},
"image_size": 224,
"init_values": 0.1,
"label2id": {},
"layer_norm_eps": 1e-06,
"mlp_ratio": 4.0,
"modality": "s2c",
"model_type": "ssl4eo_data2vec",
"num_attention_heads": 6,
"num_channels": 13,
"num_hidden_layers": 12,
"patch_size": 16,
"qkv_bias": true,
"ssl_method": "data2vec",
"transformers_version": "5.0.0",
"use_abs_pos_emb": false,
"use_mean_pooling": true,
"use_shared_rel_pos_bias": true
}
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