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
File size: 798 Bytes
fcefac1 | 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 | # 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
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