Image Feature Extraction
Transformers
Safetensors
flexict
feature-extraction
medical-imaging
ct
vision
custom_code
Instructions to use ricklisz123/FlexiCT-2D with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ricklisz123/FlexiCT-2D with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="ricklisz123/FlexiCT-2D", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ricklisz123/FlexiCT-2D", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| # Copyright (c) Meta Platforms, Inc. and affiliates. | |
| # | |
| # This software may be used and distributed in accordance with | |
| # the terms of the DINOv3 License Agreement. | |
| from typing import Union | |
| import torch | |
| from torch import Tensor, nn | |
| class LayerScale(nn.Module): | |
| def __init__( | |
| self, | |
| dim: int, | |
| init_values: Union[float, Tensor] = 1e-5, | |
| inplace: bool = False, | |
| device=None, | |
| ) -> None: | |
| super().__init__() | |
| self.inplace = inplace | |
| self.gamma = nn.Parameter(torch.empty(dim, device=device)) | |
| self.init_values = init_values | |
| def reset_parameters(self): | |
| nn.init.constant_(self.gamma, self.init_values) | |
| def forward(self, x: Tensor) -> Tensor: | |
| return x.mul_(self.gamma) if self.inplace else x * self.gamma | |