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NanoDet NCNN Demo

This project provides NanoDet image inference, webcam inference and benchmark using Tencent's NCNN framework.

How to build

Windows

Step1.

Download and Install Visual Studio from https://visualstudio.microsoft.com/vs/community/

Step2.

Download and install OpenCV from https://github.com/opencv/opencv/releases

Step3 (Optional).

Download and install Vulkan SDK from https://vulkan.lunarg.com/sdk/home

Step4.

Clone NCNN repository

git clone --recursive https://github.com/Tencent/ncnn.git

Build NCNN following this tutorial: Build for Windows x64 using VS2017

Step5.

Add ncnn_DIR = YOUR_NCNN_PATH/build/install/lib/cmake/ncnn to system environment variables.

Build project: Open x64 Native Tools Command Prompt for VS 2019 or 2017

mkdir -p build
cd build
cmake ..
msbuild nanodet_demo.vcxproj /p:configuration=release /p:platform=x64

Linux

Step1.

Build and install OpenCV from https://github.com/opencv/opencv

Step2(Optional).

Download Vulkan SDK from https://vulkan.lunarg.com/sdk/home

Step3.

Clone NCNN repository

git clone --recursive https://github.com/Tencent/ncnn.git

Build NCNN following this tutorial: Build for Linux / NVIDIA Jetson / Raspberry Pi

Step4.

Set environment variables. Run:

export ncnn_DIR=YOUR_NCNN_PATH/build/install/lib/cmake/ncnn

Build project

mkdir build
cd build
cmake ..
make

Run demo

Download NanoDet ncnn model.

Unzip the file and rename the file to nanodet.param and nanodet.bin, then copy them to demo program folder (demo_ncnn/build).

Webcam

./nanodet_demo 0 0

Inference images

./nanodet_demo 1 ${IMAGE_FOLDER}/*.jpg

Inference video

./nanodet_demo 2 ${VIDEO_PATH}

Benchmark

./nanodet_demo 3 0

bench_mark

Notice:

If benchmark speed is slow, try to limit omp thread num.

Linux:

export OMP_THREAD_LIMIT=4
Model Resolution COCO mAP CPU Latency (i7-8700) ARM CPU Latency (4*A76) Vulkan GPU Latency (GTX1060)
NanoDet-Plus-m 320*320 27.0 10.32ms / 96.9FPS 11.97ms / 83.5FPS 3.40ms / 294.1FPS
NanoDet-Plus-m 416*416 30.4 17.98ms / 55.6FPS 19.77ms / 50.6FPS 4.27ms / 234.2FPS
NanoDet-Plus-m-1.5x 320*320 29.9 12.87ms / 77.7FPS 15.90ms / 62.9FPS 3.78ms / 264.6FPS
NanoDet-Plus-m-1.5x 416*416 34.1 22.53ms / 44.4FPS 25.49ms / 39.2FPS 4.79ms / 208.8FPS

Custom model

Export to ONNX

python tools/export_onnx.py --cfg_path ${CONFIG_PATH} --model_path ${PYTORCH_MODEL_PATH}

Convert to ncnn

Run onnx2ncnn in ncnn tools to generate ncnn .param and .bin file.

After that, using ncnnoptimize to optimize ncnn model.

If you have quentions about converting ncnn model, refer to ncnn wiki. https://github.com/Tencent/ncnn/wiki

You can also convert the model with an online tool https://convertmodel.com/ .

Modify hyperparameters

If you want to use custom model, please make sure the hyperparameters in nanodet.h are the same with your training config file.

int input_size[2] = {416, 416}; // input height and width
int num_class = 80; // number of classes. 80 for COCO
int reg_max = 7; // `reg_max` set in the training config. Default: 7.
std::vector<int> strides = { 8, 16, 32, 64 }; // strides of the multi-level feature.