DL
davidbrai/deep-learning-traffic-lights
Code and files of the deep learning model used to win the Nexar Traffic Light Recognition challenge
482 160 +0/wk
GitHub
caffe deep-learning recognize-traffic-lights
Trend
0
Star & Fork Trend (19 data points)
Stars
Forks
Multi-Source Signals
Growth Velocity
davidbrai/deep-learning-traffic-lights has +0 stars this period . Velocity data will be available after more historical data is collected.
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| Metric | deep-learning-traffic-lights | Emotion | deepstock | Deep-learning-in-PHM |
|---|---|---|---|---|
| Stars | 482 | 482 | 481 | 483 |
| Forks | 160 | 170 | 157 | 101 |
| Weekly Growth | +0 | +0 | +0 | +0 |
| Language | Jupyter Notebook | Python | Python | N/A |
| Sources | 1 | 1 | 1 | 1 |
| License | BSD-2-Clause | MIT | NOASSERTION | MIT |
Capability Radar vs Emotion
deep-learning-traffic-lights
Emotion
Maintenance Activity 0
Last code push 3277 days ago.
Community Engagement 100
Fork-to-star ratio: 33.2%. Active community forking and contributing.
Issue Burden 70
Issue data not yet available.
Growth Momentum 30
No measurable growth in the current period (first-day cold start expected).
License Clarity 95
Licensed under BSD-2-Clause. Permissive — safe for commercial use.
Risk scores are computed from real-time repository data. Higher scores indicate healthier metrics.