Instructions to use hf-internal-testing/tiny-random-ConditionalDetrForObjectDetection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-ConditionalDetrForObjectDetection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="hf-internal-testing/tiny-random-ConditionalDetrForObjectDetection")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-ConditionalDetrForObjectDetection") model = AutoModelForObjectDetection.from_pretrained("hf-internal-testing/tiny-random-ConditionalDetrForObjectDetection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-internal-testing/tiny-random-ConditionalDetrForObjectDetection: direct link, hf CLI and curl.
- Browser
- Download file 107 MB
-
https://huggingface.co/hf-internal-testing/tiny-random-ConditionalDetrForObjectDetection/resolve/964e000f329a9df2117e6111b056488b532a9f92/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-ConditionalDetrForObjectDetection@964e000f329a9df2117e6111b056488b532a9f92/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-ConditionalDetrForObjectDetection/resolve/964e000f329a9df2117e6111b056488b532a9f92/pytorch_model.bin
107 MB
- Xet hash:
- c88b0c462d1cc7c0faee866d6e39ed27f5bac76742b152fae0761ebd3bd67696
- Size of remote file:
- 107 MB
- SHA256:
- 656e0acb26987b7ebdd13c51ef4f48a04fc9f7fc4ac9335d01effa924ab6b562
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