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:
- 4446ff644717d76a8bede3a09efa7380cd850e6c2aa90e7e3e6e44e8f3690d94
- Size of remote file:
- 311 kB
- SHA256:
- bb77ea5a3c6980bf0f389ec2f4d49331ac6126f98307021ad2531aa002b1b1c9
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