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
Upload folder using huggingface_hub
Browse files- __trial_0.pt +3 -0
__trial_0.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:c24a8959ca4faafb09d4c99db562db7fcc494b41a294b599d726c21f06ef3c91
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size 87310
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