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 .attention import CausalSelfAttention, LinearKMaskedBias, SelfAttention | |
| from .block import CausalSelfAttentionBlock, SelfAttentionBlock | |
| from .ffn_layers import Mlp, SwiGLUFFN | |
| from .layer_scale import LayerScale | |
| from .patch_embed import PatchEmbed | |
| from .rms_norm import RMSNorm | |
| from .rope_position_encoding import RopePositionEmbedding, RopePositionEmbedding3D | |