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: 229 Bytes
b39aef7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 | license: cc-by-nc-sa-4.0
language:
- en
tags:
- medical-imaging
- ct-scan
- 3d
- vision-transformer
- self-supervised-learning
- foundation-model
- radiology
library_name: transformers
pipeline_tag: feature-extraction |