Instructions to use XYZ9843/GOOSE-M2F with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use XYZ9843/GOOSE-M2F with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="XYZ9843/GOOSE-M2F")# Load model directly from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation processor = AutoImageProcessor.from_pretrained("XYZ9843/GOOSE-M2F") model = Mask2FormerForUniversalSegmentation.from_pretrained("XYZ9843/GOOSE-M2F") - Notebooks
- Google Colab
- Kaggle
Add pipeline tag and library name to metadata
#2
by nielsr HF Staff - opened
Hi! I'm Niels from the Hugging Face community science team. I've opened this PR to improve the model card metadata:
- Added
pipeline_tag: image-segmentationto help users discover the model in the Hub. - Added
library_name: transformersas the model uses the Mask2Former architecture and includes compatible configuration files. - Updated the BibTeX citation to include the author names and year as per the paper information.
Thanks for the update! It looks like there is a merge conflict in the README.md file right now. Could you please resolve the conflict on your end so I can merge your changes?
Thanks!
nielsr changed pull request status to closed