How Developers Can Fit AI Vision Tasks Into a Real-Time Frame Budget

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A technical guide published on DEV Community breaks down how on-device computer vision pipelines must be engineered around a strict millisecond-per-frame budget determined by the target frame rate. At 30 fps, only roughly 15–25 ms remain for model inference after accounting for camera callbacks, format conversion, resizing, and overlay rendering. Input resolution is identified as the single most expensive variable, with costs scaling quadratically, making it critical to run models at the lowest resolution that still detects the smallest required object. Tasks such as classification, object detection, landmark tracking, segmentation, and OCR each carry distinct cost structures that developers must weigh when designing pipelines. The guide recommends decoupling detection from display and using cropped regions of interest rather than upscaling full frames to stay within budget.

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