Instructions to use timm/tf_efficientnet_lite0.in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/tf_efficientnet_lite0.in1k with timm:
import timm model = timm.create_model("hf_hub:timm/tf_efficientnet_lite0.in1k", pretrained=True) - Transformers
How to use timm/tf_efficientnet_lite0.in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/tf_efficientnet_lite0.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_lite0.in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 09551c8fcd9142adc5384ec403fe3977902605b16824dd8f6fc99a7e0fc638db
- Size of remote file:
- 18.9 MB
- SHA256:
- 4ab8d79b1efeb76c18a2b2b5ecbdf2982a0311d07af491e8a16d10df70025989
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