Image Segmentation
Transformers
Safetensors
inkdetection_resnet3d
feature-extraction
vesuvius-challenge
ink-detection
herculaneum
resnet3d
u-net
3d-segmentation
volumetric-imaging
custom_code
Instructions to use scrollprize/PHerc.1667-iteration-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scrollprize/PHerc.1667-iteration-5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="scrollprize/PHerc.1667-iteration-5", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("scrollprize/PHerc.1667-iteration-5", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preview_l_2.png from scrollprize/PHerc.1667-iteration-5: direct link, hf CLI and curl.
- Browser
- Download file 877 kB
-
https://huggingface.co/scrollprize/PHerc.1667-iteration-5/resolve/main/preview_l_2.png
- Command line
-
hf download hf://scrollprize/PHerc.1667-iteration-5/preview_l_2.png
-
curl -L -o preview_l_2.png https://huggingface.co/scrollprize/PHerc.1667-iteration-5/resolve/main/preview_l_2.png
877 kB

- Xet hash:
- ae232d3d193d3be9d8f27bf58daa33fbc415fb6ae653c13f6f804b5b694cc787
- Size of remote file:
- 877 kB
- SHA256:
- 18985e77f97911d72c75f317120a3d5bcc0a633a2e7bee998db2ff3eeb4eb58b
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