| Mettle usage | |
| ============ | |
| Install the dependencies in requirements.txt, then load a local checkout or | |
| Hugging Face repository with the standard Transformers auto classes: | |
| from PIL import Image | |
| from transformers import AutoImageProcessor, AutoModel | |
| repo = "slideflow-labs/Mettle" | |
| processor = AutoImageProcessor.from_pretrained(repo) | |
| model = AutoModel.from_pretrained( | |
| repo, | |
| trust_remote_code=True, | |
| ).eval() | |
| image = Image.open("tile.png").convert("RGB") | |
| pixel_values = processor(images=image, return_tensors="pt").pixel_values | |
| # Public benchmark representation: (batch, 3072) | |
| embedding = model.encode(pixel_values, feature_view="cls_mean") | |
| # Compatibility representation: (batch, 1536) | |
| cls_embedding = model.encode(pixel_values, feature_view="cls") | |
| The first 1536 coordinates of cls_mean are exactly cls_embedding. The remaining | |
| 1536 coordinates are the mean of spatial patch tokens; CLS and register tokens | |
| are excluded. |