Instructions to use timm/efficientnet_b1_pruned.in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/efficientnet_b1_pruned.in1k with timm:
import timm model = timm.create_model("hf_hub:timm/efficientnet_b1_pruned.in1k", pretrained=True) - Transformers
How to use timm/efficientnet_b1_pruned.in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/efficientnet_b1_pruned.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/efficientnet_b1_pruned.in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fcd39ce6eda2327819f5fe74c48509f87f2b90f32c836df020ed9671be325770
- Size of remote file:
- 25.7 MB
- SHA256:
- c09699e3c6cc995cc82a8470e4177906802061a566cf90ee931bc79649a8da37
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