Instructions to use SentiSenti/resnet-18 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SentiSenti/resnet-18 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="SentiSenti/resnet-18") 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("SentiSenti/resnet-18") model = AutoModelForImageClassification.from_pretrained("SentiSenti/resnet-18", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e74eb256b02dbc6114f54f06f04be39ca4cde846aa050f2f3151a78c11fc3f55
- Size of remote file:
- 46.8 MB
- SHA256:
- 197a1c78db3d3041e2a8c0260d96305484d292f00290deedfb490162e7d55ea1
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