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-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scrollprize/PHerc.1667-iteration-3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="scrollprize/PHerc.1667-iteration-3", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("scrollprize/PHerc.1667-iteration-3", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preview_l_2.png from scrollprize/PHerc.1667-iteration-3: direct link, hf CLI and curl.
- Browser
- Download file 913 kB
-
https://huggingface.co/scrollprize/PHerc.1667-iteration-3/resolve/main/preview_l_2.png
- Command line
-
hf download hf://scrollprize/PHerc.1667-iteration-3/preview_l_2.png
-
curl -L -o preview_l_2.png https://huggingface.co/scrollprize/PHerc.1667-iteration-3/resolve/main/preview_l_2.png
913 kB

- Xet hash:
- b60a772efbdd1ea745da2706b939992503277a86614268e03617b321ed940229
- Size of remote file:
- 913 kB
- SHA256:
- a8bb61669e63f7ed13c90da1e2fa4367df7f414801f0ae8c7432b30eadea8af2
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