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")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("youngPhilosopher/drywall-qa-clipseg", dtype="auto") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- de5932a8cd8aa819eeb8ae7a5b0d43c3e9ec24d5149a476c10b25c85e78c8341
- Size of remote file:
- 189 kB
- SHA256:
- 4867045edbcc01f6c41f2d48ac81f8858dab680c3ba67603d2633764aa7f0ea1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.