Download and run Arm-optimized object detection models from the Arm AI Portal on an Arm-powered Android phone or emulator using ExecuTorch 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 | 463.89ms596.09ms | −132.21 ms | |
| End to end latency p90 | 465.00ms597.27ms | −132.27 ms | |
| End to end latency p99 | 466.87ms599.93ms | −133.06 ms | |
| Peak memory | 108.97MB145.42MB | −36.45 MB | |
| Average memory | 106.45MB143.42MB | −36.97 MB | |
| Time to first inference | 487.94ms648.43ms | −160.49 ms | |
| Frames per second | 2.16fps1.68fps | +0.48 fps | |
| mAP@0.50:0.95 | 44.00%46.77% | −2.77% | |
| mAP@0.50 | 61.90%63.10% | −1.20% | |
| mAP@0.75 | 48.50%50.93% | −2.43% |
The Optimized Model is provided as a reference implementation to demonstrate and evaluate execution and performance on Arm-based systems. It is not a production-ready or supported solution.
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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 | 50 | 50 |
| Warmup runs | 10 | 10 |