Image Classification
timm
PyTorch
Safetensors
Transformers
normalization-free
efficient-channel-attention
Instructions to use timm/eca_nfnet_l0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/eca_nfnet_l0 with timm:
import timm model = timm.create_model("hf-hub:timm/eca_nfnet_l0", pretrained=True) - Transformers
How to use timm/eca_nfnet_l0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/eca_nfnet_l0") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/eca_nfnet_l0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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transformers tag
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Adding `safetensors` variant of this model (#1)
Verified signature- Adding `safetensors` variant of this model (d7affb2b3642cc928acdddec7e36ba21bf1a62cc) Co-authored-by: Lucain Pouget <Wauplin@users.noreply.huggingface.co>be75922 -
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add imagenet labels to config.json
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Initial commit w/ README, config, weights
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initial commit
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