Instructions to use MrPotato/ReferenceSegmentationV2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MrPotato/ReferenceSegmentationV2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="MrPotato/ReferenceSegmentationV2", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("MrPotato/ReferenceSegmentationV2", trust_remote_code=True) model = AutoModelForTokenClassification.from_pretrained("MrPotato/ReferenceSegmentationV2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 82a579391b0f4deb29381367fc87e3209ee0a704160fae43f7861198d835aee0
- Size of remote file:
- 1.11 GB
- SHA256:
- b084beb63e2011fd34eb19dde325fd75fa8d6141a80f68c6b98782e446526482
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