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) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5564bfbf70269e61a9c852d2b2d7fcf35f8eaa45eee7b5b30b575fe027a25808
- Size of remote file:
- 1.11 GB
- SHA256:
- ded8fc304d11eff8cd039e74f7200ce929b20d886ad0da252ebc06bfa738f334
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