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
| 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 |