Feature Extraction
Transformers
Safetensors
English
closp
remote-sensing
text-to-image-retrieval
multimodal
geospatial
SAR
multispectral
crisis-management
earth-observation
contrastive-learning
custom_code
Instructions to use DarthReca/CLOSP-RN with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DarthReca/CLOSP-RN with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="DarthReca/CLOSP-RN", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DarthReca/CLOSP-RN", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 617 Bytes
30f4336 e4914c6 30f4336 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | {
"_name_or_path": "closp/closp-rn",
"architectures": [
"CLOSPModel"
],
"location_embedding_dim": 512,
"logit_scale_init_value": 1.0,
"model_type": "closp",
"projection_dim": 384,
"s1_embedding_dim": 1000,
"s1_head_dim": 1000,
"s2_embedding_dim": 1000,
"s2_head_dim": 1000,
"text_model_name_or_path": "sentence-transformers/all-MiniLM-L6-v2",
"torch_dtype": "float32",
"transformers_version": "4.47.1",
"use_location_encoder": false,
"vision_model_key": "resnet50",
"auto_map": {
"AutoModel": "modeling_closp.CLOSPModel",
"AutoConfig": "modeling_closp.CLOSPConfig"
}
}
|