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-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scrollprize/PHerc.1667-iteration-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="scrollprize/PHerc.1667-iteration-1", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("scrollprize/PHerc.1667-iteration-1", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preview_l_5.png from scrollprize/PHerc.1667-iteration-1: direct link, hf CLI and curl.
- Browser
- Download file 3.4 MB
-
https://huggingface.co/scrollprize/PHerc.1667-iteration-1/resolve/main/preview_l_5.png
- Command line
-
hf download hf://scrollprize/PHerc.1667-iteration-1/preview_l_5.png
-
curl -L -o preview_l_5.png https://huggingface.co/scrollprize/PHerc.1667-iteration-1/resolve/main/preview_l_5.png
3.4 MB

- Xet hash:
- 079ed2e8a45e1c915512c1094df72ab51fa5dd4eb49a10226fcf6e5250868f2e
- Size of remote file:
- 3.4 MB
- SHA256:
- 83e7fe7e328ef83eb59762c9c101df582db66d9bc5626faae0fce6ba4170b2ae
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