Image Segmentation
Transformers
Safetensors
metpredict_dpt
feature-extraction
pathology
dpt
custom_code
Instructions to use RendeiroLab/MetPredict-lung-structure-segmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RendeiroLab/MetPredict-lung-structure-segmentation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="RendeiroLab/MetPredict-lung-structure-segmentation", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("RendeiroLab/MetPredict-lung-structure-segmentation", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from RendeiroLab/MetPredict-lung-structure-segmentation: direct link, hf CLI and curl.
- Browser
- Download file 4.75 GB
-
https://huggingface.co/RendeiroLab/MetPredict-lung-structure-segmentation/resolve/main/model.safetensors
- Command line
-
hf download hf://RendeiroLab/MetPredict-lung-structure-segmentation/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/RendeiroLab/MetPredict-lung-structure-segmentation/resolve/main/model.safetensors
4.75 GB
- Xet hash:
- 4f52c9745266c6935b510acc0b6b8a30a4a54733b666c0c659ca6d11f9db59e0
- Size of remote file:
- 4.75 GB
- SHA256:
- 5c7134fc04f842b056ebe5d4abe9113acaea76449642521549d2e6d4d8a3c49c
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