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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 334 Bytes
ba1e0da | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"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"
}
}
|