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AXERA-TECH/visdrone-yolov11s
visdrone-yolov11s is a object detection model from AXERA-TECH. Use it when you need objects located in an image. It is set up for axengine. The card lists the license as apache-2.0.
YOLO11s trained on VisDrone2019-DET, compiled to AX650 AXMODEL via Pulsar2.
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Updated Jul 13, 2026
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.axmodel10.7 MB · 54%
From the Hugging Face model README
YOLO11s trained on VisDrone2019-DET, compiled to AX650 AXMODEL via Pulsar2.

| Item | Value |
|---|---|
| Architecture | YOLO11s |
| Task | Object Detection |
| Classes | 11 (pedestrian, people, bicycle, car, van, truck, tricycle, awning-tricycle, bus, motor) |
| Input | 640×640 BGR, uint8 [0,255] → float [0,1] |
| Chip | AX650N (NPU3) |
| Quantization | INT8 (MinMax PerLayer) |
| Size | 10.2 MB |
| Latency | ~3.3 ms |
models/ AXMODEL + model_meta.json
demo/ 5 VisDrone test images
python/ Python SDK (pydet + libdet.axera)
cpp/ Pre-compiled C++ binaries
bin/ visdrone_detect (aarch64)
lib/ libdet.so
include/ Headers
cd cpp
chmod +x bin/visdrone_detect
LD_LIBRARY_PATH=./lib:/soc/lib ./bin/visdrone_detect \
../models/model.axmodel ../demo/demo_00.jpg 0.25
cd python
pip install -r requirements.txt
python example.py --model ../models/model.axmodel --image ../demo/demo_00.jpg
Requires pyaxengine on the target board.
| Output Layer | Cosine Similarity | MSE |
|---|---|---|
| output0 (80×80) | 0.99999 | 0.005 |
| output1 (40×40) | 1.00000 | 0.004 |
| output2 (20×20) | 0.99999 | 0.004 |
Input uint8 BGR [0,255] is normalized to float [0,1] via std=1/255, matching the original ONNX model input range.