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 | 166.05ms167.38ms | −1.33 ms | |
| End to end latency p90 | 173.91ms175.32ms | −1.41 ms | |
| End to end latency p99 | 179.88ms182.60ms | −2.72 ms | |
| Peak memory | 154.22MB128.72MB | +25.50 MB | |
| Average memory | 154.14MB128.61MB | +25.53 MB | |
| Time to first inference | 133.85ms129.37ms | +4.48 ms | |
| Frames per second | 6.02fps5.97fps | +0.05 fps | |
| mAP@0.50:0.95 | 39.49%40.25% | −0.76% | |
| mAP@0.50 | 55.17%55.78% | −0.61% | |
| mAP@0.75 | 42.79%43.62% | −0.83% |
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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 | 100 | 100 |