Instructions to use timm/convnext_tiny.fb_in22k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/convnext_tiny.fb_in22k with timm:
import timm model = timm.create_model("hf_hub:timm/convnext_tiny.fb_in22k", pretrained=True) - Transformers
How to use timm/convnext_tiny.fb_in22k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/convnext_tiny.fb_in22k") 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_tiny.fb_in22k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6eb6aad1ab2402df719de2ed5ad88ca48b914a35ce46e7e6ea99fd2187c1455e
- Size of remote file:
- 179 MB
- SHA256:
- fc2911bb741382f4e32750c9146a0ca423d918d14043e5cbce6eedb5ad046067
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.