Instructions to use facebook/convnextv2-base-22k-384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/convnextv2-base-22k-384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/convnextv2-base-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-base-22k-384") model = AutoModelForImageClassification.from_pretrained("facebook/convnextv2-base-22k-384", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/convnextv2-base-22k-384: direct link, hf CLI and curl.
- Browser
- Download file 355 MB
-
https://huggingface.co/facebook/convnextv2-base-22k-384/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/convnextv2-base-22k-384/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/convnextv2-base-22k-384/resolve/main/pytorch_model.bin
355 MB
- Xet hash:
- ef5bcf2b091a94046cf91132029554b3da15228420668af3347e1bdf42fe1a2f
- Size of remote file:
- 355 MB
- SHA256:
- 7004bf3ae869324a6f974b8f3111347baf483ab55c07b9687d87a0f0bf0efe42
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