Instructions to use timm/convnext_large.fb_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/convnext_large.fb_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/convnext_large.fb_in1k", pretrained=True) - Transformers
How to use timm/convnext_large.fb_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/convnext_large.fb_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/convnext_large.fb_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f6f29ca83e390b40e684a947427bbc74f8be97b299b503a762befc474e1b27e7
- Size of remote file:
- 791 MB
- SHA256:
- f804cfea097857d13482396318e47df86adc1585e7bb24501589ad77afa9e95c
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