Instructions to use facebook/convnextv2-nano-22k-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/convnextv2-nano-22k-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/convnextv2-nano-22k-224") 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-nano-22k-224") model = AutoModelForImageClassification.from_pretrained("facebook/convnextv2-nano-22k-224", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/convnextv2-nano-22k-224: direct link, hf CLI and curl.
- Browser
- Download file 62.5 MB
-
https://huggingface.co/facebook/convnextv2-nano-22k-224/resolve/refs%2Fpr%2F2/pytorch_model.bin
- Command line
-
hf download hf://facebook/convnextv2-nano-22k-224@refs/pr/2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/convnextv2-nano-22k-224/resolve/refs%2Fpr%2F2/pytorch_model.bin
62.5 MB
- Xet hash:
- ad5ac92875b789c892026b887a17d295b3f5d646db250e2c2c60a3a6e25b69fb
- Size of remote file:
- 62.5 MB
- SHA256:
- 81652c0b575263b005ba81d0355e42ff4fbf7ef158834b98b26ca4b47c4eebbc
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