Instructions to use lukasiktar11/raspberry-pi-elements-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use lukasiktar11/raspberry-pi-elements-detector with ultralytics:
from huggingface_hub import hf_hub_download from ultralytics import YOLO # pick the weights file from this repo's "Files and versions" tab weights = hf_hub_download("lukasiktar11/raspberry-pi-elements-detector", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
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Download README.md from lukasiktar11/raspberry-pi-elements-detector: direct link, hf CLI and curl.
- Browser
- Download file 219 Bytes
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https://huggingface.co/lukasiktar11/raspberry-pi-elements-detector/resolve/main/README.md
- Command line
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hf download hf://lukasiktar11/raspberry-pi-elements-detector/README.md
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curl -L -o README.md https://huggingface.co/lukasiktar11/raspberry-pi-elements-detector/resolve/main/README.md
219 Bytes
metadata
tags:
- detection
- yolo
- ultralytics
license: agpl-3.0
raspberry-pi-elements-detector
YOLO26 model trained to perform the Raspberry Pi element detection on images. Part of the ComputerVisionAIHub catalog.