Instructions to use fairportrobotics/spy-glass with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use fairportrobotics/spy-glass 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("fairportrobotics/spy-glass", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - YOLOv10
How to use fairportrobotics/spy-glass with YOLOv10:
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("fairportrobotics/spy-glass", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Spy Glass Object Detection Model
Created by FRC Team 578
Description
These YOLO models were trained for FRC competitions. It detects the bumper of red and blue robots, and game pieces.
Metrics
YOLO v11 (model11n.pt)
| Class | Images | Instances | Box | R | mAP50 | mAP50-95 |
|---|---|---|---|---|---|---|
| all | 191 | 846 | 0.870 | 0.772 | 0.850 | 0.600 |
| red robot | 117 | 176 | 0.870 | 0.847 | 0.912 | 0.697 |
| blue robot | 108 | 143 | 0.876 | 0.790 | 0.853 | 0.619 |
| game piece | 131 | 527 | 0.864 | 0.679 | 0.785 | 0.484 |
YOLO v10 (model10s.pt)
| Class | Images | Instances | Box | R | mAP50 | mAP50-95 |
|---|---|---|---|---|---|---|
| all | 191 | 846 | 0.898 | 0.737 | 0.832 | 0.596 |
| red robot | 117 | 176 | 0.913 | 0.830 | 0.909 | 0.703 |
| blue robot | 108 | 143 | 0.902 | 0.776 | 0.845 | 0.619 |
| game piece | 131 | 527 | 0.879 | 0.606 | 0.743 | 0.467 |
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