Instructions to use shingguy1/fine_tuned_efficientnet_b0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shingguy1/fine_tuned_efficientnet_b0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="shingguy1/fine_tuned_efficientnet_b0", device_map="auto") 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("shingguy1/fine_tuned_efficientnet_b0") model = AutoModelForImageClassification.from_pretrained("shingguy1/fine_tuned_efficientnet_b0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ae242dae1840abe6c4883c18c5aef872459c094afe4593c654826b9036820a2b
- Size of remote file:
- 16.3 MB
- SHA256:
- d692fb0a2a908280a6f3b87be2525ee9654c1c677b17d909a452b4aba5a9e10c
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