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 AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("timm/convnextv2_base.fcmae") model = AutoModel.from_pretrained("timm/convnextv2_base.fcmae", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors 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/model.safetensors
- Command line
-
hf download hf://timm/convnextv2_base.fcmae/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/timm/convnextv2_base.fcmae/resolve/main/model.safetensors
351 MB
- Xet hash:
- 40d5980adf41531c70a84fbf5db951b4bea406e74fd8091f6aa01e2af958f2f6
- Size of remote file:
- 351 MB
- SHA256:
- 4c2c0ab209f18d9dc1d4cf85e92dda8c834f377d27c5485c284e38daa9c060db
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.