Instructions to use hf-internal-testing/tiny-random-OwlViTForObjectDetection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-OwlViTForObjectDetection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-object-detection", model="hf-internal-testing/tiny-random-OwlViTForObjectDetection")# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-OwlViTForObjectDetection") model = AutoModelForZeroShotObjectDetection.from_pretrained("hf-internal-testing/tiny-random-OwlViTForObjectDetection") - Notebooks
- Google Colab
- Kaggle
Upload ONNX weights (#2)
Browse files- [Awaiting approval] Upload ONNX weights (d7d0169ddaac50197862d1b65e2c8d52e61c074c)
- onnx/model.onnx +3 -0
onnx/model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:236b21ebeb6ab6125ad1722d6ce45540d672f9669be0186e4534db1ce26123fd
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size 2090622
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