Instructions to use frgfm/rexnet2_0x with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use frgfm/rexnet2_0x with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="frgfm/rexnet2_0x") 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/rexnet2_0x", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bc99a365fd885751403fc346e1a51ccba445a26701c6018958599d28f43a3784
- Size of remote file:
- 55.8 MB
- SHA256:
- e9172e0667f03dd3444858bbb6bf2d063cae07d502021cfe4c8afa007e283ed4
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