Instructions to use frgfm/rexnet1_5x with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use frgfm/rexnet1_5x with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="frgfm/rexnet1_5x") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("frgfm/rexnet1_5x", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a7659da9c53fa60259f03e32bcaadf2e44e3cfa8c7a48980fe55385f471babaf
- Size of remote file:
- 31.7 MB
- SHA256:
- 3930c84da0f05a6c34f5ff7308e628ee8f3fdd719c82929a8bbcba5ac2759280
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