Instructions to use timm/convnextv2_base.fcmae with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/convnextv2_base.fcmae with timm:
import timm model = timm.create_model("hf-hub:timm/convnextv2_base.fcmae", pretrained=True) - Transformers
How to use timm/convnextv2_base.fcmae with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="timm/convnextv2_base.fcmae")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/convnextv2_base.fcmae", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timm/convnextv2_base.fcmae: direct link, hf CLI and curl.
- Browser
- Download file 351 MB
-
https://huggingface.co/timm/convnextv2_base.fcmae/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://timm/convnextv2_base.fcmae/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/convnextv2_base.fcmae/resolve/main/pytorch_model.bin
351 MB
- Xet hash:
- 78b4c5c1f8f95cf58670fb22c5339ac0a1051c22ddf992a8c9571d2b798a00be
- Size of remote file:
- 351 MB
- SHA256:
- d03e31da718f7c0bdb2e79b9f003315ed14c7c0591d17a0cb7bce15c39cd53b7
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