Instructions to use facebook/convnextv2-huge-1k-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/convnextv2-huge-1k-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/convnextv2-huge-1k-224") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("facebook/convnextv2-huge-1k-224") model = AutoModelForImageClassification.from_pretrained("facebook/convnextv2-huge-1k-224", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/convnextv2-huge-1k-224: direct link, hf CLI and curl.
- Browser
- Download file 2.64 GB
-
https://huggingface.co/facebook/convnextv2-huge-1k-224/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/convnextv2-huge-1k-224/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/convnextv2-huge-1k-224/resolve/main/pytorch_model.bin
2.64 GB
- Xet hash:
- 0632c8b959b67869062235c261f9a907cb39fb94d7cfcfdc44f03aee4ece5f29
- Size of remote file:
- 2.64 GB
- SHA256:
- 3c07089b19d22a6f24396c2b60c79daa1fd1836bd25a5f918b7fbba81353eefc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.