Instructions to use tustoz/resnet50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tustoz/resnet50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="tustoz/resnet50") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("tustoz/resnet50") model = AutoModelForImageClassification.from_pretrained("tustoz/resnet50", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a2acd48677ab9d00f3966e369b411319b03b119da1c779b0a7e9a96798fb9a00
- Size of remote file:
- 5.2 kB
- SHA256:
- a9de96feafcb47680a596b29a34250b6857e58fbe457dfa52ac28e6c25918fe1
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