Instructions to use hvlgo/resnet18 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hvlgo/resnet18 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="hvlgo/resnet18")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hvlgo/resnet18") model = AutoModel.from_pretrained("hvlgo/resnet18", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hvlgo/resnet18: direct link, hf CLI and curl.
- Browser
- Download file 94.4 MB
-
https://huggingface.co/hvlgo/resnet18/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hvlgo/resnet18/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hvlgo/resnet18/resolve/main/pytorch_model.bin
94.4 MB
- Xet hash:
- fc76bb2ea2dcac3fb7827f71b6eef321ed076733fea1d526ee515acb17fff231
- Size of remote file:
- 94.4 MB
- SHA256:
- 888f1f81086b8aaba25cb31bc2d302622cf2a710b48970ca16bcc787af95c113
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