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: 636 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 | """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__",
]
|