Instructions to use timm/convnextv2_nano.fcmae_ft_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/convnextv2_nano.fcmae_ft_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/convnextv2_nano.fcmae_ft_in1k", pretrained=True) - Transformers
How to use timm/convnextv2_nano.fcmae_ft_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/convnextv2_nano.fcmae_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/convnextv2_nano.fcmae_ft_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1b8604b59d42f66dec76c9dfaf74ec810c6020a802706532dcf6c494f8a51c0b
- Size of remote file:
- 62.6 MB
- SHA256:
- 545cb78be5a9c3128cc9d3de16d0c66c78802912739a5613c320f79cea602cc3
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