1. System Summary (Sample)
We start by evaluating your specific constraints against the hardware reality. The full report is customized to your resolution, FPS goals, and thermal budget.
| Component | Example Target |
|---|---|
| Target Hardware | NVIDIA Jetson Nano / Raspberry Pi 4B |
| Camera Interface | USB 2.0 / CSI-2 (IMX219) |
| Resolution | 640×480 @ 30 FPS |
| Task | Object Detection (YOLOv8n) |
| Latency Budget | < 150ms end-to-end |
| Thermal Limit | 70-75°C (Passive Cooling) |
2. Vision Pipeline & Performance
We mathematically model the latency at every stage—from the camera driver to the NMS (Non-Maximum Suppression) output.
Camera
→Pre-Process
→Inference
→Post-Process
| Stage | Estimated Time |
|---|---|
| Frame Capture | 12 - 16 ms |
| Preprocessing | 4 - 7 ms |
| Inference (YOLOv8n) | 45 - 75 ms |
| Post-Processing | 3 - 6 ms |
| Total Latency | 64 - 104 ms |
* Note: These figures are sample estimates for illustration only. Your final report includes tailored FPS estimates for TensorRT INT8, FP16, and ONNX models.
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