Instructions to use nvidia/groupvit-gcc-yfcc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/groupvit-gcc-yfcc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="nvidia/groupvit-gcc-yfcc")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("nvidia/groupvit-gcc-yfcc") model = AutoModel.from_pretrained("nvidia/groupvit-gcc-yfcc", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
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Add TF weights (#1)
- Add TF weights (e604b5b254f35889423451abec4d5820b1bd0ab2) Co-authored-by: Aritra Roy Gosthipaty <ariG23498@users.noreply.huggingface.co>751f6d9
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add readme
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Revert id2label and label2id additions
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Add id2label and label2id mappings
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Upload preprocessor_config.json
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Upload tokenizer_config.json
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Upload special_tokens_map.json
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Upload vocab.json
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add model
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initial commit
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