ncnn-android-yolov8

The yolov8 object detection

This is a sample ncnn android project, it depends on ncnn library and opencv

https://github.com/Tencent/ncnn

https://github.com/nihui/opencv-mobile

https://blog.halashuo.cn/index.php/archives/118/

how to build and run

step1

https://github.com/Tencent/ncnn/releases https://github.com/Tencent/ncnn/releases/latest/download/ncnn-20231027-android-vulkan.zip

• Download ncnn-YYYYMMDD-android-vulkan.zip or build ncnn for android yourself
• Extract ncnn-YYYYMMDD-android-vulkan.zip into app/src/main/jni and change the ncnn_DIR path to yours in app/src/main/jni/CMakeLists.txt

step2

https://github.com/nihui/opencv-mobile 建议下载opencv android sdk 版本4.8.1 https://github.com/opencv/opencv/releases

• Download opencv-mobile-XYZ-android.zip
• Extract opencv-mobile-XYZ-android.zip into app/src/main/jni and change the OpenCV_DIR path to yours in app/src/main/jni/CMakeLists.txt

step3

• Open this project with Android Studio, build it and enjoy!

https://convertmodel.com/

YOLOV8训练的模型 转为ncnn 在Android中使用 需要一下两步，然后导出模型 1.修改Python39\Lib\site-packages\ultralytics\nn\modules\head.py class Detect(nn.Module): ... def forward(self, x): """Concatenates and returns predicted bounding boxes and class probabilities.""" shape = x[0].shape # BCHW for i in range(self.nl): x[i] = torch.cat((self.cv2i, self.cv3i), 1) if self.training: return x elif self.dynamic or self.shape != shape: self.anchors, self.strides = (x.transpose(0, 1) for x in make_anchors(x, self.stride, 0.5)) self.shape = shape pred = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2).permute(0, 2, 1) return pred

2.修改Python39\Lib\site-packages\ultralytics\nn\modules\block.py class C2f(nn.Module): ... def forward(self, x): """Forward pass through C2f layer.""" x=self.cv1(x) x=[x,x[:,self.c:,...]] x.extend(m(x[-1]) for m in self.m) x.pop(1) return self.cv2(torch.cat(x,1)) #y = list(self.cv1(x).chunk(2, 1)) #y.extend(m(y[-1]) for m in self.m) #return self.cv2(torch.cat(y, 1)) 3. 导出模型 bird_model = YOLO(r'birds_200.pt') success = bird_model.export(format="onnx", simplify=True, opset=12) 4. 转换模型类型 https://convertmodel.com/

some notes

• Android ndk camera is used for best efficiency
• Crash may happen on very old devices for lacking HAL3 camera interface
• All models are manually modified to accept dynamic input shape
• Most small models run slower on GPU than on CPU, this is common
• FPS may be lower in dark environment because of longer camera exposure time

screenshot

Reference：

https://github.com/nihui/ncnn-android-nanodet
https://github.com/Tencent/ncnn
https://github.com/ultralytics/assets/releases/tag/v0.0.0