Instructions to use hf-tiny-model-private/tiny-random-ConditionalDetrForObjectDetection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-ConditionalDetrForObjectDetection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="hf-tiny-model-private/tiny-random-ConditionalDetrForObjectDetection")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-ConditionalDetrForObjectDetection") model = AutoModelForObjectDetection.from_pretrained("hf-tiny-model-private/tiny-random-ConditionalDetrForObjectDetection") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 637410702de000e001a673e6c29e568e410d10eab14d6dd9e68d4d1f7b4cd675
- Size of remote file:
- 107 MB
- SHA256:
- fe06544a4231e7e0fcce5de485f0e1914e5d84cd0f7cf0722e7ba82cb1a34094
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