Feature Extraction
Transformers
Safetensors
mettle
computational-pathology
histopathology
foundation-model
scanner-robustness
custom_code
Instructions to use slideflow-labs/Mettle with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use slideflow-labs/Mettle with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="slideflow-labs/Mettle", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("slideflow-labs/Mettle", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| """Configuration for the Mettle pathology tile encoder.""" | |
| from transformers import PretrainedConfig | |
| class MettleConfig(PretrainedConfig): | |
| """Configuration for a Mettle ViT-g/14 encoder.""" | |
| model_type = "mettle" | |
| def __init__( | |
| self, | |
| backbone_name="vit_giant_patch14_reg4_dinov2", | |
| image_size=224, | |
| hidden_size=1536, | |
| num_register_tokens=4, | |
| head_enabled=True, | |
| head_num_atoms=16, | |
| head_rank=8, | |
| head_hidden_size=256, | |
| default_feature_view="cls", | |
| **kwargs, | |
| ): | |
| super().__init__(**kwargs) | |
| if default_feature_view not in {"cls", "cls_mean"}: | |
| raise ValueError( | |
| "default_feature_view must be 'cls' or 'cls_mean', got " | |
| f"{default_feature_view!r}" | |
| ) | |
| self.backbone_name = backbone_name | |
| self.image_size = int(image_size) | |
| self.hidden_size = int(hidden_size) | |
| self.num_register_tokens = int(num_register_tokens) | |
| self.head_enabled = bool(head_enabled) | |
| self.head_num_atoms = int(head_num_atoms) | |
| self.head_rank = int(head_rank) | |
| self.head_hidden_size = int(head_hidden_size) | |
| self.default_feature_view = default_feature_view | |