Instructions to use Francesco/resnet26 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Francesco/resnet26 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Francesco/resnet26") 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("Francesco/resnet26") model = AutoModelForImageClassification.from_pretrained("Francesco/resnet26") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 29795b64c0c9364986eb1d49c93ca0b4114a8e3f496c665e5926c2de8909ad76
- Size of remote file:
- 64.2 MB
- SHA256:
- 3eac393f17e883dd4ecb5907492fba0169993b67fa63bbdadccdccc077a9b215
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