Instructions to use shi-labs/oneformer_coco_dinat_large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shi-labs/oneformer_coco_dinat_large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="shi-labs/oneformer_coco_dinat_large")# Load model directly from transformers import AutoProcessor, OneFormerForUniversalSegmentation processor = AutoProcessor.from_pretrained("shi-labs/oneformer_coco_dinat_large") model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_coco_dinat_large") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a3c8f5b3fb753ccbdb4058aeb879f29cb8f3bd35f8efc9054da90ed1cdde8297
- Size of remote file:
- 893 MB
- SHA256:
- a6cf9ddc4b115cffc4ce1325d4283df2977dc90f04bc754ccc5c50beab5bb5fa
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