ncnn-android-yolo11

The YOLO11 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://github.com/nihui/mesa-turnip-android-driver (mesa turnip driver)

android apk file download

https://github.com/nihui/ncnn-android-yolo11/releases/latest

how to build and run

step1

https://github.com/Tencent/ncnn/releases

• 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

• 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

https://github.com/nihui/mesa-turnip-android-driver

• Download mesa-turnip-android-XYZ.zip
• Create directory app/src/main/jniLibs/arm64-v8a if not exists
• Extract libvulkan_freedreno.so from mesa-turnip-android-XYZ.zip into app/src/main/jniLibs/arm64-v8a

step4

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

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

guidelines for converting YOLO11 models

1. install

pip3 install -U ultralytics pnnx ncnn

2. export yolo11 torchscript

yolo export model=yolo11n.pt format=torchscript
yolo export model=yolo11n-seg.pt format=torchscript
yolo export model=yolo11n-pose.pt format=torchscript
yolo export model=yolo11n-cls.pt format=torchscript
yolo export model=yolo11n-obb.pt format=torchscript

3. convert torchscript with static shape

For classification models, step 1-3 is enough.

pnnx yolo11n.torchscript
pnnx yolo11n-seg.torchscript
pnnx yolo11n-pose.torchscript
pnnx yolo11n-cls.torchscript
pnnx yolo11n-obb.torchscript

4. modify pnnx model script for dynamic shape inference manually

Edit yolo11n_pnnx.py / yolo11n_seg_pnnx.py / yolo11n_pose_pnnx.py / yolo11n_obb_pnnx.py

• modify reshape to support dynamic image sizes
• permute tensor before concat and adjust concat axis (permutations are faster on smaller tensors)
• drop post-process part (we implement the post-process externally to avoid invalid bounding box coordinate calculations below the threshold, which is faster)

model	before	after
det	v_235 = v_204.view(1, 144, 6400) v_236 = v_219.view(1, 144, 1600) v_237 = v_234.view(1, 144, 400) v_238 = torch.cat((v_235, v_236, v_237), dim=2) # ...	v_235 = v_204.view(1, 144, -1).transpose(1, 2) v_236 = v_219.view(1, 144, -1).transpose(1, 2) v_237 = v_234.view(1, 144, -1).transpose(1, 2) v_238 = torch.cat((v_235, v_236, v_237), dim=1) return v_238
seg	v_202 = v_201.view(1, 32, 6400) v_208 = v_207.view(1, 32, 1600) v_214 = v_213.view(1, 32, 400) v_215 = torch.cat((v_202, v_208, v_214), dim=2) # ... v_261 = v_230.view(1, 144, 6400) v_262 = v_245.view(1, 144, 1600) v_263 = v_260.view(1, 144, 400) v_264 = torch.cat((v_261, v_262, v_263), dim=2) # ... v_285 = (v_284, v_196, ) return v_285	v_202 = v_201.view(1, 32, -1).transpose(1, 2) v_208 = v_207.view(1, 32, -1).transpose(1, 2) v_214 = v_213.view(1, 32, -1).transpose(1, 2) v_215 = torch.cat((v_202, v_208, v_214), dim=1) # ... v_261 = v_230.view(1, 144, -1).transpose(1, 2) v_262 = v_245.view(1, 144, -1).transpose(1, 2) v_263 = v_260.view(1, 144, -1).transpose(1, 2) v_264 = torch.cat((v_261, v_262, v_263), dim=1) return v_264, v_215, v_196
pose	v_195 = v_194.view(1, 51, 6400) v_201 = v_200.view(1, 51, 1600) v_207 = v_206.view(1, 51, 400) v_208 = torch.cat((v_195, v_201, v_207), dim=-1) # ... v_254 = v_223.view(1, 65, 6400) v_255 = v_238.view(1, 65, 1600) v_256 = v_253.view(1, 65, 400) v_257 = torch.cat((v_254, v_255, v_256), dim=2) # ...	v_195 = v_194.view(1, 51, -1).transpose(1, 2) v_201 = v_200.view(1, 51, -1).transpose(1, 2) v_207 = v_206.view(1, 51, -1).transpose(1, 2) v_208 = torch.cat((v_195, v_201, v_207), dim=1) # ... v_254 = v_223.view(1, 65, -1).transpose(1, 2) v_255 = v_238.view(1, 65, -1).transpose(1, 2) v_256 = v_253.view(1, 65, -1).transpose(1, 2) v_257 = torch.cat((v_254, v_255, v_256), dim=1) return v_257, v_208
obb	v_195 = v_194.view(1, 1, 16384) v_201 = v_200.view(1, 1, 4096) v_207 = v_206.view(1, 1, 1024) v_208 = torch.cat((v_195, v_201, v_207), dim=2) # ... v_256 = v_225.view(1, 79, 16384) v_257 = v_240.view(1, 79, 4096) v_258 = v_255.view(1, 79, 1024) v_259 = torch.cat((v_256, v_257, v_258), dim=2) # ...	v_195 = v_194.view(1, 1, -1).transpose(1, 2) v_201 = v_200.view(1, 1, -1).transpose(1, 2) v_207 = v_206.view(1, 1, -1).transpose(1, 2) v_208 = torch.cat((v_195, v_201, v_207), dim=1) # ... v_256 = v_225.view(1, 79, -1).transpose(1, 2) v_257 = v_240.view(1, 79, -1).transpose(1, 2) v_258 = v_255.view(1, 79, -1).transpose(1, 2) v_259 = torch.cat((v_256, v_257, v_258), dim=1) return v_259, v_208

• modify area attention for dynamic shape inference

changes	det seg pose obb
beore	# ... v_95 = self.model_10_m_0_attn_qkv_conv(v_94) v_96 = v_95.view(1, 2, 128, 1024) v_97, v_98, v_99 = torch.split(tensor=v_96, dim=2, split_size_or_sections=(32,32,64)) v_100 = torch.transpose(input=v_97, dim0=-2, dim1=-1) v_101 = torch.matmul(input=v_100, other=v_98) v_102 = (v_101 * 0.176777) v_103 = F.softmax(input=v_102, dim=-1) v_104 = torch.transpose(input=v_103, dim0=-2, dim1=-1) v_105 = torch.matmul(input=v_99, other=v_104) v_106 = v_105.view(1, 128, 32, 32) v_107 = v_99.reshape(1, 128, 32, 32) v_108 = self.model_10_m_0_attn_pe_conv(v_107) v_109 = (v_106 + v_108) v_110 = self.model_10_m_0_attn_proj_conv(v_109) # ...
after	# ... v_95 = self.model_10_m_0_attn_qkv_conv(v_94) v_96 = v_95.view(1, 2, 128, -1) # <--- This line, note this v_95 v_97, v_98, v_99 = torch.split(tensor=v_96, dim=2, split_size_or_sections=(32,32,64)) v_100 = torch.transpose(input=v_97, dim0=-2, dim1=-1) v_101 = torch.matmul(input=v_100, other=v_98) v_102 = (v_101 * 0.176777) v_103 = F.softmax(input=v_102, dim=-1) v_104 = torch.transpose(input=v_103, dim0=-2, dim1=-1) v_105 = torch.matmul(input=v_99, other=v_104) v_106 = v_105.view(1, 128, v_95.size(2), v_95.size(3)) # <--- This line v_107 = v_99.reshape(1, 128, v_95.size(2), v_95.size(3)) # <--- This line v_108 = self.model_10_m_0_attn_pe_conv(v_107) v_109 = (v_106 + v_108) v_110 = self.model_10_m_0_attn_proj_conv(v_109) # ...

5. re-export yolo11 torchscript

python3 -c 'import yolo11n_pnnx; yolo11n_pnnx.export_torchscript()'
python3 -c 'import yolo11n_seg_pnnx; yolo11n_seg_pnnx.export_torchscript()'
python3 -c 'import yolo11n_pose_pnnx; yolo11n_pose_pnnx.export_torchscript()'
python3 -c 'import yolo11n_obb_pnnx; yolo11n_obb_pnnx.export_torchscript()'

6. convert new torchscript with dynamic shape

Note the shape difference for obb model

pnnx yolo11n_pnnx.py.pt inputshape=[1,3,640,640] inputshape2=[1,3,320,320]
pnnx yolo11n_seg_pnnx.py.pt inputshape=[1,3,640,640] inputshape2=[1,3,320,320]
pnnx yolo11n_pose_pnnx.py.pt inputshape=[1,3,640,640] inputshape2=[1,3,320,320]
pnnx yolo11n_obb_pnnx.py.pt inputshape=[1,3,1024,1024] inputshape2=[1,3,512,512]

7. now you get ncnn model files

mv yolo11n_pnnx.py.ncnn.param yolo11n.ncnn.param
mv yolo11n_pnnx.py.ncnn.bin yolo11n.ncnn.bin
mv yolo11n_seg_pnnx.py.ncnn.param yolo11n_seg.ncnn.param
mv yolo11n_seg_pnnx.py.ncnn.bin yolo11n_seg.ncnn.bin
mv yolo11n_pose_pnnx.py.ncnn.param yolo11n_pose.ncnn.param
mv yolo11n_pose_pnnx.py.ncnn.bin yolo11n_pose.ncnn.bin
mv yolo11n_obb_pnnx.py.ncnn.param yolo11n_obb.ncnn.param
mv yolo11n_obb_pnnx.py.ncnn.bin yolo11n_obb.ncnn.bin