Instructions to use google/efficientnet-b2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/efficientnet-b2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="google/efficientnet-b2") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("google/efficientnet-b2") model = AutoModelForImageClassification.from_pretrained("google/efficientnet-b2", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f0eb8506d8b0c66b1abd7d2f21663b6e874ca074ae898da478f65e1cd1ac6319
- Size of remote file:
- 36.8 MB
- SHA256:
- 5e40d17c4f08dc3a8620c76a5520445b8e14b7da39af5d9982b24a40da6e6b23
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