Instructions to use timm/convnext_nano.in12k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/convnext_nano.in12k with timm:
import timm model = timm.create_model("hf_hub:timm/convnext_nano.in12k", pretrained=True) - Transformers
How to use timm/convnext_nano.in12k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/convnext_nano.in12k") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/convnext_nano.in12k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1368a16fb68428efbac153411916ffd7855deb95094a7f9fdbd807c0c280cb89
- Size of remote file:
- 90.2 MB
- SHA256:
- 893d642c3b323ca168adc4c0489047d68ee338e997ae47e44e167cdbcdab9768
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