Instructions to use facebook/convnextv2-nano-22k-384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/convnextv2-nano-22k-384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/convnextv2-nano-22k-384") 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-nano-22k-384") model = AutoModelForImageClassification.from_pretrained("facebook/convnextv2-nano-22k-384", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 509c17847aea99aa1cfd1fbcdf5f710e91b55c0df141f0d585db2eaf353f10c2
- Size of remote file:
- 62.5 MB
- SHA256:
- 7015e5110e0ec3cb9901b8ab290e63f0ee3718ec32002555a4072779df94926a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.