Frames captured: 0
Frames processed: 0
Frames dropped: 0
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Every real-time camera feature on a phone — object detection, AR filters, document scanning — has to reconcile a fixed camera capture rate with a model inference time that rarely divides evenly into it. This simulator runs an actual discrete-event scheduler: frames spawn at the chosen camera rate, an inference "gate" holds each one for the chosen latency, and you pick between the three scheduling policies real mobile ML pipelines use — queue everything, drop frames while busy, or submit only every Nth frame. Watch the conveyor of frames turn orange while being processed, green once committed, or red when dropped, and read the resulting effective FPS, average staleness and delegate duty cycle live, exactly as an app developer would tune them against a battery and latency budget.