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 pytorch_model.bin from jsli96/ResNet-18-1: direct link, hf CLI and curl.
- Browser
- Download file 45.2 MB
-
https://huggingface.co/jsli96/ResNet-18-1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jsli96/ResNet-18-1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jsli96/ResNet-18-1/resolve/main/pytorch_model.bin
45.2 MB
- Xet hash:
- daa9a374e2599ce8419002f81819340789c36377b3ce8910cadfc079410d3550
- Size of remote file:
- 45.2 MB
- SHA256:
- 0aa8d5652935be63cda6defc0428de0798b7c6501ee1307eb5a6c531f8df92b3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.