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") - Inference
- Notebooks
- Google Colab
- Kaggle
Upload processor
Browse files- preprocessor_config.json +1 -0
preprocessor_config.json
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0.4732864,
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0.47434163
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],
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"resample": 0,
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"rescale_factor": 0.00392156862745098,
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"rescale_offset": false,
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],
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"include_top": true,
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"resample": 0,
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"rescale_factor": 0.00392156862745098,
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"rescale_offset": false,
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