Instructions to use timm/convnextv2_tiny.fcmae_ft_in22k_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/convnextv2_tiny.fcmae_ft_in22k_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/convnextv2_tiny.fcmae_ft_in22k_in1k", pretrained=True) - Transformers
How to use timm/convnextv2_tiny.fcmae_ft_in22k_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/convnextv2_tiny.fcmae_ft_in22k_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_tiny.fcmae_ft_in22k_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d21ee58d4081ced05f7d30a3bd6fb23e342fd9f3617b99e93cd84110f0865128
- Size of remote file:
- 115 MB
- SHA256:
- 575d9bb89e70e30af9b2a822118f4cca2a443255b0259034716bb7164ea5d3e8
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