Instructions to use AmineAllo/margin-element-detector-fm-pretty-wind-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AmineAllo/margin-element-detector-fm-pretty-wind-5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="AmineAllo/margin-element-detector-fm-pretty-wind-5")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("AmineAllo/margin-element-detector-fm-pretty-wind-5") model = AutoModelForObjectDetection.from_pretrained("AmineAllo/margin-element-detector-fm-pretty-wind-5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from AmineAllo/margin-element-detector-fm-pretty-wind-5: direct link, hf CLI and curl.
- Browser
- Download file 115 MB
-
https://huggingface.co/AmineAllo/margin-element-detector-fm-pretty-wind-5/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://AmineAllo/margin-element-detector-fm-pretty-wind-5/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/AmineAllo/margin-element-detector-fm-pretty-wind-5/resolve/main/pytorch_model.bin
115 MB
- Xet hash:
- 894a0698c248c0a1025bce77c41b62828edf8c8a2476be8ced38bb4f7111b69d
- Size of remote file:
- 115 MB
- SHA256:
- c4216e783c5f26ee5a0a1b325369ac9fd1c5d1ea8dec56d97c6a397d288aee03
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