Download and run Arm-optimized object detection models from the Arm AI Portal on an Arm-powered Android phone or emulator using ExecuTorch, LiteRT, and XNNPACK.
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 | 457.03ms1018.84ms | −561.80 ms | |
| End to end latency p90 | 457.52ms1019.59ms | −562.07 ms | |
| End to end latency p99 | 460.90ms1021.21ms | −560.31 ms | |
| Peak memory | 90.28MB141.06MB | −50.78 MB | |
| Average memory | 89.34MB140.34MB | −51.00 MB | |
| Time to first inference | 471.89ms1046.32ms | −574.43 ms | |
| Frames per second | 2.19fps0.98fps | +1.21 fps | |
| mAP@0.50:0.95 | 43.86%44.31% | −0.45% | |
| mAP@0.50 | 60.39%60.70% | −0.31% | |
| mAP@0.75 | 47.49%47.97% | −0.48% |
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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 |