Instructions to use timm/convnextv2_base.fcmae_ft_in22k_in1k_384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/convnextv2_base.fcmae_ft_in22k_in1k_384 with timm:
import timm model = timm.create_model("hf_hub:timm/convnextv2_base.fcmae_ft_in22k_in1k_384", pretrained=True) - Transformers
How to use timm/convnextv2_base.fcmae_ft_in22k_in1k_384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/convnextv2_base.fcmae_ft_in22k_in1k_384") 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_base.fcmae_ft_in22k_in1k_384", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f4ef3463af23205ac9285e7d030dc680b318c1c4f92f9bacd3798ce41d976fec
- Size of remote file:
- 355 MB
- SHA256:
- 972bcbb8d9965eca7a2f72d9592f54dfc890f9e6aef920cea4269963f46f2f42
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.