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: 976 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 | from transformers import PretrainedConfig
class SpectreConfig(PretrainedConfig):
model_type = "spectre"
def __init__(
self,
backbone_name="vit_large_patch16_128",
backbone_kwargs={
"num_classes": 0,
"global_pool": '',
"pos_embed": "rope",
"rope_kwargs": {"base": 1000.0},
"init_values": 1.0,
},
feature_combiner_name="feat_vit_large",
feature_combiner_kwargs={
"num_classes": 0,
"global_pool": "",
"pos_embed": "rope",
"rope_kwargs": {"base": 100.0},
"init_values": 1.0,
},
**kwargs,
):
super().__init__(**kwargs)
self.backbone_name = backbone_name
self.backbone_kwargs = backbone_kwargs or {}
self.feature_combiner_name = feature_combiner_name
self.feature_combiner_kwargs = feature_combiner_kwargs or {} |