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 | 578.44ms909.74ms | −331.30 ms | |
| End to end latency p90 | 578.85ms910.58ms | −331.72 ms | |
| End to end latency p99 | 579.28ms912.40ms | −333.11 ms | |
| Peak memory | 101.02MB126.94MB | −25.92 MB | |
| Average memory | 100.14MB126.34MB | −26.20 MB | |
| Time to first inference | 601.14ms926.88ms | −325.74 ms | |
| Frames per second | 1.73fps1.10fps | +0.63 fps | |
| mAP@0.50:0.95 | 40.01%40.32% | −0.31% | |
| mAP@0.50 | 58.77%58.89% | −0.12% | |
| mAP@0.75 | 43.33%43.70% | −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 |