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: 269 Bytes
8b41845 | 1 2 3 4 5 6 7 8 9 10 11 12 | from .patch_embed import PatchEmbed
from .attention import Attention
from .layernorm import LayerNorm3d
from .rotary_pos_embed import RotaryPositionEmbedding
__all__ = [
'PatchEmbed',
'Attention',
'LayerNorm3d',
'RotaryPositionEmbedding',
]
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