Instructions to use AmineAllo/margin-element-detector-fm-clean-oath-16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AmineAllo/margin-element-detector-fm-clean-oath-16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="AmineAllo/margin-element-detector-fm-clean-oath-16")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("AmineAllo/margin-element-detector-fm-clean-oath-16") model = AutoModelForObjectDetection.from_pretrained("AmineAllo/margin-element-detector-fm-clean-oath-16", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from AmineAllo/margin-element-detector-fm-clean-oath-16: direct link, hf CLI and curl.
- Browser
- Download file 115 MB
-
https://huggingface.co/AmineAllo/margin-element-detector-fm-clean-oath-16/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://AmineAllo/margin-element-detector-fm-clean-oath-16/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/AmineAllo/margin-element-detector-fm-clean-oath-16/resolve/main/pytorch_model.bin
115 MB
- Xet hash:
- 4d893aea5e8d69d128d3387092903aafd98679e3f6f71d568ee70bf0f5d897ba
- Size of remote file:
- 115 MB
- SHA256:
- 7b0530caebe00bf003374ae71abe1ac9bf206013fa74ef82d93a34e493640de4
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