Instructions to use google/efficientnet-b0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/efficientnet-b0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="google/efficientnet-b0") 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-b0") model = AutoModelForImageClassification.from_pretrained("google/efficientnet-b0") - Inference
- Notebooks
- Google Colab
- Kaggle
Upload EfficientNetForImageClassification
Browse files- config.json +1 -0
config.json
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"batch_norm_momentum": 0.99,
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"depth_coefficient": 1.0,
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"depth_divisor": 8,
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"drop_connect_rate": 0.2,
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"dropout_rate": 0.2,
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"expand_ratios": [
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"batch_norm_momentum": 0.99,
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"depth_coefficient": 1.0,
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"depth_divisor": 8,
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"depthwise_padding": [],
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"drop_connect_rate": 0.2,
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"dropout_rate": 0.2,
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"expand_ratios": [
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