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
| { | |
| "architectures": [ | |
| "SpectreModel" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_spectre.SpectreConfig", | |
| "AutoModel": "modeling_spectre.SpectreModel" | |
| }, | |
| "backbone_kwargs": { | |
| "global_pool": "", | |
| "init_values": 1.0, | |
| "num_classes": 0, | |
| "pos_embed": "rope", | |
| "rope_kwargs": { | |
| "base": 1000.0 | |
| } | |
| }, | |
| "backbone_name": "vit_large_patch16_128", | |
| "dtype": "float32", | |
| "feature_combiner_kwargs": { | |
| "global_pool": "", | |
| "init_values": 1.0, | |
| "num_classes": 0, | |
| "pos_embed": "rope", | |
| "rope_kwargs": { | |
| "base": 100.0 | |
| } | |
| }, | |
| "feature_combiner_name": "feat_vit_large", | |
| "model_type": "spectre", | |
| "transformers_version": "5.3.0" | |
| } | |