Instructions to use Francesco/resnet18 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Francesco/resnet18 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Francesco/resnet18") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Francesco/resnet18") model = AutoModelForImageClassification.from_pretrained("Francesco/resnet18", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Francesco/resnet18: direct link, hf CLI and curl.
- Browser
- Download file 46.8 MB
-
https://huggingface.co/Francesco/resnet18/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Francesco/resnet18/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Francesco/resnet18/resolve/main/pytorch_model.bin
46.8 MB
- Xet hash:
- b3cbad993dc78afdae96432cf2c625d5d6edd605ca0d8e3562b063586bbad005
- Size of remote file:
- 46.8 MB
- SHA256:
- ee4ae2824b3c6047b2d8f5ec4cb4d3fdb8c745b0ffb615fb02bf96b51a5396ad
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