Instructions to use microsoft/resnet-50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/resnet-50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/resnet-50") 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("microsoft/resnet-50") model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-50") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6d33f5b07938703377df5002f5f4fcdf336fbf0ce5d182d36462e4535058e3e4
- Size of remote file:
- 103 MB
- SHA256:
- 57afa67ca9f9aa1657f38f100f8e5c7fc49550dafa7226793b48dae4426f5a69
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