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
| from .patch_embed import PatchEmbed | |
| from .attention import Attention | |
| from .layernorm import LayerNorm3d | |
| from .rotary_pos_embed import RotaryPositionEmbedding | |
| __all__ = [ | |
| 'PatchEmbed', | |
| 'Attention', | |
| 'LayerNorm3d', | |
| 'RotaryPositionEmbedding', | |
| ] | |