Instructions to use timm/convnextv2_base.fcmae_ft_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/convnextv2_base.fcmae_ft_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/convnextv2_base.fcmae_ft_in1k", pretrained=True) - Transformers
How to use timm/convnextv2_base.fcmae_ft_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/convnextv2_base.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_base.fcmae_ft_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e58ba2a6efed50e8a6cb01171cf5b4c63c0a44d8e91e5dc779ad528a4af57777
- Size of remote file:
- 355 MB
- SHA256:
- e3185a989fde75cd0d28e0ecf8a41a86b27efc71a8f3d985cf28b02560d7fd78
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.