# nihui-ncnn-android-yolo11 **Repository Path**: atari/nihui-ncnn-android-yolo11 ## Basic Information - **Project Name**: nihui-ncnn-android-yolo11 - **Description**: 同步 https://github.com/nihui/ncnn-android-yolo11 - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-05-12 - **Last Updated**: 2025-05-12 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # ncnn-android-yolo11 ![download](https://img.shields.io/github/downloads/nihui/ncnn-android-yolo11/total.svg) 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 ![](screenshot0.jpg) ![](screenshot1.jpg) ![](screenshot2.jpg) ## guidelines for converting YOLO11 models ### 1. install ```shell pip3 install -U ultralytics pnnx ncnn ``` ### 2. export yolo11 torchscript ```shell 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.** ```shell 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)
modelbeforeafter
det ```python 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) # ... ``` ```python 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 ```python 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 ``` ```python 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 ```python 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) # ... ``` ```python 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 ```python 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) # ... ``` ```python 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
changesdet seg pose obb
beore ```python # ... 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 ```python # ... 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 ```shell 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** ```shell 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 ```shell 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 ```