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
File size: 1,252 Bytes
0e83a2b | 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 34 35 36 37 38 39 | """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
|