Instructions to use jsli96/ResNet-18-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jsli96/ResNet-18-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jsli96/ResNet-18-1") 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("jsli96/ResNet-18-1") model = AutoModelForImageClassification.from_pretrained("jsli96/ResNet-18-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from jsli96/ResNet-18-1: direct link, hf CLI and curl.
- Browser
- Download file 3.52 kB
-
https://huggingface.co/jsli96/ResNet-18-1/resolve/main/training_args.bin
- Command line
-
hf download hf://jsli96/ResNet-18-1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/jsli96/ResNet-18-1/resolve/main/training_args.bin
3.52 kB
- Xet hash:
- 95162b78d47bb881919265d3405477e124e2b7dca9aee29d57037449f78e5892
- Size of remote file:
- 3.52 kB
- SHA256:
- c48b7daf3d1583c8f860136303a6a248e4ac8d1083452c4af331e9f90a8477aa
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