Instructions to use google/efficientnet-b3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/efficientnet-b3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="google/efficientnet-b3") 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-b3") model = AutoModelForImageClassification.from_pretrained("google/efficientnet-b3") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bb2b18620d23481f0946db95a9d2771e0edd9ac3164c422febe3223d0c686e89
- Size of remote file:
- 49.5 MB
- SHA256:
- d16416551981fdc160561ee54ef33d6664f2546b4244e289b12fddccd764d9ba
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