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
| """Top-level package for spectre. | |
| Expose a small, stable public API here so users can do: | |
| from spectre import SpectreImageFeatureExtractor, models | |
| Keep implementations in subpackages; this file only re-exports the most | |
| important symbols and subpackages for convenience. | |
| """ | |
| from .model import SpectreImageFeatureExtractor, MODEL_CONFIGS | |
| from . import models | |
| from . import utils | |
| __version__ = "0.1.0" | |
| __author__ = "Cris Claessens" | |
| __email__ = "c.h.b.claessens@tue.nl" | |
| __all__ = [ | |
| "SpectreImageFeatureExtractor", | |
| "MODEL_CONFIGS", | |
| "models", | |
| "utils", | |
| "__version__", | |
| "__author__", | |
| "__email__", | |
| ] | |