Kendryte Standalone SDK
Now available TrackingAnalyze
Train yolo model using darknet or this repo.
To conver your keras model to kmodel, MaixPy_scripts or nncase can be used.
First prepare your yolo.h5
file, then
tflite_convert --keras_model_file=yolo.h5 --output_file=yolo.tflite
./tflite2kmodel.sh yolo.tflite
- Ubuntu
Download RISC-V 64bit toolchain for Kendryte K210_ubuntu_amd64
from https://kendryte.com/downloads/.
Extract it to /opt/riscv-toolchain, and add export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/riscv-toolchain/bin/
to bashrc
.
mkdir build && cd build
cmake .. -DPROJ=yolo3_frame_test_public_maixpy -DTOOLCHAIN=/opt/riscv-toolchain/bin && make
- Windows
Download and install CMake and the latest toolchain.
mkdir build && cd build
cmake -G "MinGW Makefiles" .. -DPROJ=yolo3_frame_test_public_maixpy -DTOOLCHAIN=/path/to/toolchain/bin
make
You will get yolo3_frame_test_public_maixpy.bin
.
If you want to flash it in UOG, using yolo3_frame_test_public_maixpy.bin
, then using flash-tool(s) burn it to your flash.
sudo kflash yolo3_frame_test_public_maixpy.bin -B dockE -p /dev/ttyUSB0 -b 3000000 -t
You may want to flash your bin and model toghther with kfpkg
cp ../src/yolo3_frame_test_public_maixpy/kfpkg/kpu_yolov3.kfpkg .
zip kpu_yolov3.kfpkg yolo.kmodel yolo3_frame_test_public_maixpy.bin
sudo kflash kpu_yolov3.kfpkg -B dockE -p /dev/ttyUSB0 -b 3000000 -t
- Yolov3
- kendryte-standalone-sdk
- keras-yolo3
- K210_Yolo_framework
- M. Lorbach, E. I. Kyriakou, R. Poppe, E. A. van Dam, L. P. J. J. Noldus, and R. C. Veltkamp, “Learning to Recognize Rat Social Behavior: Novel Dataset and Cross-Dataset Application,” Journal of Neuroscience Methods, 2017. data
- MaixPy_scripts