Instructions to use Qdrant/resnet50-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Qdrant/resnet50-onnx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Qdrant/resnet50-onnx") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Qdrant/resnet50-onnx", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b52995af1b0292a71fcd45f7717b94e894239c0e8aed80dcbfde746fdf0e960c
- Size of remote file:
- 94 MB
- SHA256:
- 4f0558b775c85b568012f7174cadda33fb4b4e5a253ce17e5d839f123e0bc7e6
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