Image Segmentation
Transformers
English
clipseg
segmentation
construction
drywall
quality-assurance
text-conditioned
binary-mask
Instructions to use youngPhilosopher/drywall-qa-clipseg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use youngPhilosopher/drywall-qa-clipseg with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="youngPhilosopher/drywall-qa-clipseg")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("youngPhilosopher/drywall-qa-clipseg", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download reports/figures/visual_comparison.png from youngPhilosopher/drywall-qa-clipseg: direct link, hf CLI and curl.
- Browser
- Download file 1.24 MB
-
https://huggingface.co/youngPhilosopher/drywall-qa-clipseg/resolve/main/reports/figures/visual_comparison.png
- Command line
-
hf download hf://youngPhilosopher/drywall-qa-clipseg/reports/figures/visual_comparison.png
-
curl -L -o visual_comparison.png https://huggingface.co/youngPhilosopher/drywall-qa-clipseg/resolve/main/reports/figures/visual_comparison.png
1.24 MB

- Xet hash:
- ce46b3eb57902802b02752e55a8efb8932e9e475c9ad3f6234b86c17122bb907
- Size of remote file:
- 1.24 MB
- SHA256:
- 5381e3793a0299aa1ed357d2d12381128f6e1adabfbf20b75bf1b64f519c6107
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