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")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Qdrant/resnet50-onnx", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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model = ImageEmbedding(model_name="Qdrant/resnet50-onnx")
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embeddings = list(
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# [
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# array([-0.1115, 0.0097, 0.0052, 0.0195, ...], dtype=float32),
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model = ImageEmbedding(model_name="Qdrant/resnet50-onnx")
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embeddings = list(model.embed(images))
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# [
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# array([-0.1115, 0.0097, 0.0052, 0.0195, ...], dtype=float32),
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