Learning path
Get started with object detection using a Jetson Orin Nano
Learn how to set up a Jetson Orin Nano with a MIPI CSI-2 camera and perform real-time object detection from live video and image files using DetectNet and TensorRT.
Evaluation Conditions
Object Detection · Raspberry Pi 5 · INT8 · 640×640 · COCO 2017 (val2017)
| Metric | Resultsvs baseline | Change | |
|---|---|---|---|
| End to end latency p50 | 134.10ms169.36ms | −35.25 ms | |
| End to end latency p90 | 134.68ms170.12ms | −35.44 ms | |
| End to end latency p99 | 135.06ms171.62ms | −36.56 ms | |
| Peak memory | 59.17MB78.33MB | −19.16 MB | |
| Average memory | 57.98MB77.42MB | −19.44 MB | |
| Time to first inference | 150.83ms191.71ms | −40.88 ms | |
| Frames per second | 7.46fps5.90fps | +1.56 fps | |
| mAP@0.50:0.95 | 38.90%39.30% | −0.40% | |
| mAP@0.50 | 54.66%55.11% | −0.45% | |
| mAP@0.75 | 42.38%42.75% | −0.37% |
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| Configuration | Baseline | Arm Optimized |
|---|---|---|
| Dataset | COCO 2017 (val2017) | COCO 2017 (val2017) |
| Sample count | 5,000 | 5,000 |
| Batch size | 1 | 1 |
| Input resolution | 640x640 | 640x640 |
| Runs | 100 | 100 |
| Warmup runs | 10 | 10 |