Feature Extraction
Transformers
English
remote-sensing
earth-observation
self-supervised-learning
multispectral
sar
rgb
depth
decur
resnet
vit
segformer
Instructions to use BiliSakura/DECUR-transformers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BiliSakura/DECUR-transformers with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="BiliSakura/DECUR-transformers")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("BiliSakura/DECUR-transformers", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "image_processor_type": "DeCURImageProcessor", | |
| "size": { | |
| "height": 224, | |
| "width": 224 | |
| }, | |
| "do_resize": false, | |
| "do_rescale": true, | |
| "do_normalize": false, | |
| "do_convert_rgb": false, | |
| "rescale_factor": 0.00392156862745098, | |
| "auto_map": { | |
| "AutoImageProcessor": "image_processing_decur.DeCURImageProcessor" | |
| } | |
| } | |