Feature Extraction
Transformers
Safetensors
English
spectre
medical-imaging
ct-scan
3d
vision-transformer
self-supervised-learning
foundation-model
radiology
custom_code
Instructions to use cclaess/SPECTRE-Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cclaess/SPECTRE-Large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="cclaess/SPECTRE-Large", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("cclaess/SPECTRE-Large", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 734 Bytes
8b41845 | 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 28 29 30 31 32 | from .vision_transformer import (
VisionTransformer,
vit_tiny_patch16_128,
vit_small_patch16_128,
vit_base_patch16_128,
vit_base_patch32_128,
vit_large_patch16_128,
vit_large_patch32_128,
)
from .vision_transformer_features import (
FeatureVisionTransformer,
feat_vit_tiny,
feat_vit_small,
feat_vit_base,
feat_vit_large,
)
__all__ = [
'VisionTransformer',
'vit_tiny_patch16_128',
'vit_small_patch16_128',
'vit_base_patch16_128',
'vit_base_patch32_128',
'vit_large_patch16_128',
'vit_large_patch32_128',
'FeatureVisionTransformer',
'feat_vit_tiny',
'feat_vit_small',
'feat_vit_base',
'feat_vit_large',
]
|