How I Built a Neural Network to Auto-Tune FPV Drone PIDs from Blackbox Logs

If you fly FPV drones, you know the pain: hours of test flights, tweaking PIDs by hand, crashing, reflashing, repeat. I got tired of it, so I built a tool that uses neural networks to analyze Betaflight blackbox logs and suggest optimized PID values automatically. The Problem with Manual PID Tuning Betaflight's default PIDs work okay for most builds, but if you want tight, locked-in freestyle or racing performance, you need to tune. The traditional approach looks like this: Fly with default PIDs Record blackbox data Open PIDtoolbox, stare at gyro traces Guess which values to change Reflash, fly again Repeat 20+ times Most pilots give up after step 4. The learning curve is steep, and even experienced tuners rely heavily on intuition built over years of practice. My Approach: Let the Data Decide Instead of relying on human intuition, I trained a neural network on thousands of blackbox logs paired with their "before and after" PID configurations. The model learned patterns that correlate