Instructions to use timm/tf_efficientnetv2_b0.in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/tf_efficientnetv2_b0.in1k with timm:
import timm model = timm.create_model("hf_hub:timm/tf_efficientnetv2_b0.in1k", pretrained=True) - Transformers
How to use timm/tf_efficientnetv2_b0.in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/tf_efficientnetv2_b0.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_efficientnetv2_b0.in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 68176f94f62e06a5db24b9c90947139c15f9403eb8e22a1706036373d66dd8b9
- Size of remote file:
- 28.9 MB
- SHA256:
- bb490c8473ca877a73de663f9f361c4337203aff8ad4e402e8b461fc4cef70e1
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