Instructions to use facebook/convnextv2-tiny-22k-384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/convnextv2-tiny-22k-384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/convnextv2-tiny-22k-384") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("facebook/convnextv2-tiny-22k-384") model = AutoModelForImageClassification.from_pretrained("facebook/convnextv2-tiny-22k-384", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
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Renaming *.grn.gamma -> *.grn.weight, *.grn.beta -> *.grn.bias and adding `safetensors` variant of this model
Verified signatureRenaming *.grn.gamma -> *.grn.weight, *.grn.beta -> *.grn.bias and adding `safetensors` variant of this modele272265
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Add TF weights (#3)
- Add TF weights (3677e74b9982ac6d5908ac60c6ec6b085609d628) Co-authored-by: neggles <neggles@users.noreply.huggingface.co>a94fa89
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Update README.md
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Create README.md
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Upload processor
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Upload ConvNextV2ForImageClassification
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initial commit
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