Instructions to use timm/convnext_tiny.in12k_ft_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/convnext_tiny.in12k_ft_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/convnext_tiny.in12k_ft_in1k", pretrained=True) - Transformers
How to use timm/convnext_tiny.in12k_ft_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/convnext_tiny.in12k_ft_in1k") 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.in12k_ft_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c15e02617a9e0326a5e4ea8eee6ae09518e2082f00c1d23478582b885bc51903
- Size of remote file:
- 114 MB
- SHA256:
- 1abc6ff66688819cab2f7f8b1d9e14f7830c947ad663bd3c1f6681c0d31fcce6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.