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 training_args.bin from AmineAllo/margin-element-detector-fm-clean-oath-16: direct link, hf CLI and curl.
- Browser
- Download file 4.47 kB
-
https://huggingface.co/AmineAllo/margin-element-detector-fm-clean-oath-16/resolve/main/training_args.bin
- Command line
-
hf download hf://AmineAllo/margin-element-detector-fm-clean-oath-16/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/AmineAllo/margin-element-detector-fm-clean-oath-16/resolve/main/training_args.bin
4.47 kB
- Xet hash:
- 5913e77f3c27b1f101606e81e969460a3f89e1e1d1b87972e36d4998b77ea3c0
- Size of remote file:
- 4.47 kB
- SHA256:
- 75cd9c14c927a39b29dd0ad237d620ed681ff9933a45eda1f613ad95b36d21dc
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