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: 727 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 28 29 30 31 32 33 | {
"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"
}
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