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 | 673.12ms1782.47ms | −1109.35 ms | |
| End to end latency p90 | 692.72ms1805.59ms | −1112.88 ms | |
| End to end latency p99 | 702.15ms1811.62ms | −1109.47 ms | |
| Peak memory | 390.45MB540.14MB | −149.69 MB | |
| Average memory | 294.78MB447.11MB | −152.33 MB | |
| Time to first inference | 661.37ms1676.69ms | −1015.32 ms | |
| Frames per second | 1.48fps0.56fps | +0.92 fps | |
| mAP@0.50:0.95 | 50.75%50.92% | −0.17% | |
| mAP@0.50 | 69.44%69.63% | −0.19% | |
| mAP@0.75 | 54.53%54.76% | −0.23% |
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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 | 5000 | 5000 |