Instructions to use timm/tf_efficientnet_b0.aa_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/tf_efficientnet_b0.aa_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/tf_efficientnet_b0.aa_in1k", pretrained=True) - Transformers
How to use timm/tf_efficientnet_b0.aa_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/tf_efficientnet_b0.aa_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/tf_efficientnet_b0.aa_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1da7ee987e40657e63b3d3b0ddd407b740dd2384609fc5dd42eb21040e6aae3e
- Size of remote file:
- 21.4 MB
- SHA256:
- 3e5fb680287a79de8c025933b9a9489ff68cce97e174a4ff6960c7e1df4d42d1
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