Instructions to use ajcasagrande/rc-race-vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use ajcasagrande/rc-race-vision with ultralytics:
# Couldn't find a valid YOLO version tag. # Replace XX with the correct version. from ultralytics import YOLOvXX model = YOLOvXX.from_pretrained("ajcasagrande/rc-race-vision") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
File size: 476 Bytes
53bb602 f4cabcf 0f18ad7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ---
license: agpl-3.0
base_model:
- Ultralytics/YOLO11
pipeline_tag: object-detection
tags:
- rc
- racing
- yolo
- tracking
- vision
library_name: ultralytics
---
# RC Race Vision
RC Car detection and tracking using Computer Vision and YOLOv11.
**[Blog Post](https://ajcasagrande.medium.com/your-next-pit-crew-enhancing-rc-racing-with-computer-vision-bee25c494c69)** | **[GitHub Repo](https://github.com/ajcasagrande/rc-race-vision)**
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