yolov5 训练一个模型，转成fp16用NCNN部署到移动端，发现ncnn侧得分超低，并位置都不对 #3625

ouxiand · 2022-03-16T05:40:22Z

1.yolov5 训练一个车牌检测模型，转成fp16用NCNN部署到移动端
python3 export.py --weights /yolo/yolov5/weights/best.pt --include onnx --train
python3 export.py --weights /yolo/yolov5/weights/best.pt --img 320 --include onnx --train
python3 -m onnxsim /yolo/yolov5/weights/best.onnx /yolo/yolov5/weights/best-sim.onnx
./weights/onnx2ncnn /yolo/yolov5/weights/best-sim.onnx /yolo/yolov5/weights/best.param /yolo/yolov5/weights/best.bin
./weights/ncnnoptimize /yolo/yolov5/weights/best.param /yolo/yolov5/weights/best.bin /yolo/yolov5/weights/best-opt.param /yolo/yolov5/weights/best-opt.bin 65536

2.在安卓用NCNN部署
修改了：以下参数匹配模型输出参数
ex.input("images", in_pad);
ex.extract("output", out);
ex.extract("353", out);
ex.extract("367", out);
参数匹配yolov5n的训练参数

发现同一张图片（车牌做了打码处理）在ncnn侧得分超低，并位置都不对！
云端检测得分

NCNN检测得分

ouxiand · 2022-03-16T05:51:54Z

补充说明：
1.云端是用1080P的图片训练 imgsize是320
2.NCNN用320检测1080P的图片
3. param如下：

7767517
173 197
Input images Convolution Conv_0 Swish Mul_2 Convolution Conv_3 Swish Mul_5 Split splitncnn_0 Convolution Conv_6 Swish Mul_8 Split splitncnn_1 Convolution Conv_9 Swish Mul_11 Convolution Conv_12 Swish Mul_14 BinaryOp Add_15 Convolution Conv_16 Swish Mul_18 Concat Concat_19 Convolution Conv_20 Swish Mul_22 Convolution Conv_23 Swish Mul_25 Split splitncnn_2 Convolution Conv_26 Swish Mul_28 Split splitncnn_3 Convolution Conv_29 Swish Mul_31 Convolution Conv_32 Swish Mul_34 BinaryOp Add_35 Split splitncnn_4 Convolution Conv_36 Swish Mul_38 Convolution Conv_39 Swish Mul_41 BinaryOp Add_42 Convolution Conv_43 Swish Mul_45 Concat Concat_46 Convolution Conv_47 Swish Mul_49 Split splitncnn_5 Convolution Conv_50 Swish Mul_52 Split splitncnn_6 Convolution Conv_53 Swish Mul_55 Split splitncnn_7 Convolution Conv_56 Swish Mul_58 Convolution Conv_59 Swish Mul_61 BinaryOp Add_62 Split splitncnn_8 Convolution Conv_63 Swish Mul_65 Convolution Conv_66 Swish Mul_68 BinaryOp Add_69 Split splitncnn_9 Convolution Conv_70 Swish Mul_72 Convolution Conv_73 Swish Mul_75 BinaryOp Add_76 Convolution Conv_77 Swish Mul_79 Concat Concat_80 Convolution Conv_81 Swish Mul_83 Split splitncnn_10 Convolution Conv_84 Swish Mul_86 Split splitncnn_11 Convolution Conv_87 Swish Mul_89 Split splitncnn_12 Convolution Conv_90 Swish Mul_92 Convolution Conv_93 Swish Mul_95 BinaryOp Add_96 Convolution Conv_97 Swish Mul_99 Concat Concat_100 Convolution Conv_101 Swish Mul_103 Convolution Conv_104 Swish Mul_106 Split splitncnn_13 Pooling MaxPool_107 Split splitncnn_14 Pooling MaxPool_108 Split splitncnn_15 Pooling MaxPool_109 Concat Concat_110 Convolution Conv_111 Swish Mul_113 Convolution Conv_114 Swish Mul_116 Split splitncnn_16 Interp Resize_121 Concat Concat_122 Split splitncnn_17 Convolution Conv_123 Swish Mul_125 Convolution Conv_126 Swish Mul_128 Convolution Conv_129 Swish Mul_131 Convolution Conv_132 Swish Mul_134 Concat Concat_135 Convolution Conv_136 Swish Mul_138 Convolution Conv_139 Swish Mul_141 Split splitncnn_18 Interp Resize_146 Concat Concat_147 Split splitncnn_19 Convolution Conv_148 Swish Mul_150 Convolution Conv_151 Swish Mul_153 Convolution Conv_154 Swish Mul_156 Convolution Conv_157 Swish Mul_159 Concat Concat_160 Convolution Conv_161 Swish Mul_163 Split splitncnn_20 Convolution Conv_164 Swish Mul_166 Concat Concat_167 Split splitncnn_21 Convolution Conv_168 Swish Mul_170 Convolution Conv_171 Swish Mul_173 Convolution Conv_174 Swish Mul_176 Convolution Conv_177 Swish Mul_179 Concat Concat_180 Convolution Conv_181 Swish Mul_183 Split splitncnn_22 Convolution Conv_184 Swish Mul_186 Concat Concat_187 Split splitncnn_23 Convolution Conv_188 Swish Mul_190 Convolution Conv_191 Swish Mul_193 Convolution Conv_194 Swish Mul_196 Convolution Conv_197 Swish Mul_199 Concat Concat_200 Convolution Conv_201 Swish Mul_203 Convolution Conv_204 Reshape Reshape_216 Permute Transpose_217 Convolution Conv_218 Reshape Reshape_230 Permute Transpose_231 Convolution Conv_232 Reshape Reshape_244 Permute Transpose_245 0 1 images
1 1 images 122 0=16 1=6 3=2 4=2 5=1 6=1728
1 1 122 124
1 1 124 125 0=32 1=3 3=2 4=1 5=1 6=4608
1 1 125 127
1 2 127 127_splitncnn_0 127_splitncnn_1
1 1 127_splitncnn_1 128 0=16 1=1 5=1 6=512
1 1 128 130
1 2 130 130_splitncnn_0 130_splitncnn_1
1 1 130_splitncnn_1 131 0=16 1=1 5=1 6=256
1 1 131 133
1 1 133 134 0=16 1=3 4=1 5=1 6=2304
1 1 134 136
2 1 130_splitncnn_0 136 137
1 1 127_splitncnn_0 138 0=16 1=1 5=1 6=512
1 1 138 140
2 1 137 140 141
1 1 141 142 0=32 1=1 5=1 6=1024
1 1 142 144
1 1 144 145 0=64 1=3 3=2 4=1 5=1 6=18432
1 1 145 147
1 2 147 147_splitncnn_0 147_splitncnn_1
1 1 147_splitncnn_1 148 0=32 1=1 5=1 6=2048
1 1 148 150
1 2 150 150_splitncnn_0 150_splitncnn_1
1 1 150_splitncnn_1 151 0=32 1=1 5=1 6=1024
1 1 151 153
1 1 153 154 0=32 1=3 4=1 5=1 6=9216
1 1 154 156
2 1 150_splitncnn_0 156 157
1 2 157 157_splitncnn_0 157_splitncnn_1
1 1 157_splitncnn_1 158 0=32 1=1 5=1 6=1024
1 1 158 160
1 1 160 161 0=32 1=3 4=1 5=1 6=9216
1 1 161 163
2 1 157_splitncnn_0 163 164
1 1 147_splitncnn_0 165 0=32 1=1 5=1 6=2048
1 1 165 167
2 1 164 167 168
1 1 168 169 0=64 1=1 5=1 6=4096
1 1 169 171
1 2 171 171_splitncnn_0 171_splitncnn_1
1 1 171_splitncnn_1 172 0=128 1=3 3=2 4=1 5=1 6=73728
1 1 172 174
1 2 174 174_splitncnn_0 174_splitncnn_1
1 1 174_splitncnn_1 175 0=64 1=1 5=1 6=8192
1 1 175 177
1 2 177 177_splitncnn_0 177_splitncnn_1
1 1 177_splitncnn_1 178 0=64 1=1 5=1 6=4096
1 1 178 180
1 1 180 181 0=64 1=3 4=1 5=1 6=36864
1 1 181 183
2 1 177_splitncnn_0 183 184
1 2 184 184_splitncnn_0 184_splitncnn_1
1 1 184_splitncnn_1 185 0=64 1=1 5=1 6=4096
1 1 185 187
1 1 187 188 0=64 1=3 4=1 5=1 6=36864
1 1 188 190
2 1 184_splitncnn_0 190 191
1 2 191 191_splitncnn_0 191_splitncnn_1
1 1 191_splitncnn_1 192 0=64 1=1 5=1 6=4096
1 1 192 194
1 1 194 195 0=64 1=3 4=1 5=1 6=36864
1 1 195 197
2 1 191_splitncnn_0 197 198
1 1 174_splitncnn_0 199 0=64 1=1 5=1 6=8192
1 1 199 201
2 1 198 201 202
1 1 202 203 0=128 1=1 5=1 6=16384
1 1 203 205
1 2 205 205_splitncnn_0 205_splitncnn_1
1 1 205_splitncnn_1 206 0=256 1=3 3=2 4=1 5=1 6=294912
1 1 206 208
1 2 208 208_splitncnn_0 208_splitncnn_1
1 1 208_splitncnn_1 209 0=128 1=1 5=1 6=32768
1 1 209 211
1 2 211 211_splitncnn_0 211_splitncnn_1
1 1 211_splitncnn_1 212 0=128 1=1 5=1 6=16384
1 1 212 214
1 1 214 215 0=128 1=3 4=1 5=1 6=147456
1 1 215 217
2 1 211_splitncnn_0 217 218
1 1 208_splitncnn_0 219 0=128 1=1 5=1 6=32768
1 1 219 221
2 1 218 221 222
1 1 222 223 0=256 1=1 5=1 6=65536
1 1 223 225
1 1 225 226 0=128 1=1 5=1 6=32768
1 1 226 228
1 2 228 228_splitncnn_0 228_splitncnn_1
1 1 228_splitncnn_1 229 1=5 3=2 5=1
1 2 229 229_splitncnn_0 229_splitncnn_1
1 1 229_splitncnn_1 230 1=5 3=2 5=1
1 2 230 230_splitncnn_0 230_splitncnn_1
1 1 230_splitncnn_1 231 1=5 3=2 5=1
4 1 228_splitncnn_0 229_splitncnn_0 230_splitncnn_0 231 232
1 1 232 233 0=256 1=1 5=1 6=131072
1 1 233 235
1 1 235 236 0=128 1=1 5=1 6=32768
1 1 236 238
1 2 238 238_splitncnn_0 238_splitncnn_1
1 1 238_splitncnn_1 243 0=1 1=2.000000e+00 2=2.000000e+00
2 1 243 205_splitncnn_0 244
1 2 244 244_splitncnn_0 244_splitncnn_1
1 1 244_splitncnn_1 245 0=64 1=1 5=1 6=16384
1 1 245 247
1 1 247 248 0=64 1=1 5=1 6=4096
1 1 248 250
1 1 250 251 0=64 1=3 4=1 5=1 6=36864
1 1 251 253
1 1 244_splitncnn_0 254 0=64 1=1 5=1 6=16384
1 1 254 256
2 1 253 256 257
1 1 257 258 0=128 1=1 5=1 6=16384
1 1 258 260
1 1 260 261 0=64 1=1 5=1 6=8192
1 1 261 263
1 2 263 263_splitncnn_0 263_splitncnn_1
1 1 263_splitncnn_1 268 0=1 1=2.000000e+00 2=2.000000e+00
2 1 268 171_splitncnn_0 269
1 2 269 269_splitncnn_0 269_splitncnn_1
1 1 269_splitncnn_1 270 0=32 1=1 5=1 6=4096
1 1 270 272
1 1 272 273 0=32 1=1 5=1 6=1024
1 1 273 275
1 1 275 276 0=32 1=3 4=1 5=1 6=9216
1 1 276 278
1 1 269_splitncnn_0 279 0=32 1=1 5=1 6=4096
1 1 279 281
2 1 278 281 282
1 1 282 283 0=64 1=1 5=1 6=4096
1 1 283 285
1 2 285 285_splitncnn_0 285_splitncnn_1
1 1 285_splitncnn_1 286 0=64 1=3 3=2 4=1 5=1 6=36864
1 1 286 288
2 1 288 263_splitncnn_0 289
1 2 289 289_splitncnn_0 289_splitncnn_1
1 1 289_splitncnn_1 290 0=64 1=1 5=1 6=8192
1 1 290 292
1 1 292 293 0=64 1=1 5=1 6=4096
1 1 293 295
1 1 295 296 0=64 1=3 4=1 5=1 6=36864
1 1 296 298
1 1 289_splitncnn_0 299 0=64 1=1 5=1 6=8192
1 1 299 301
2 1 298 301 302
1 1 302 303 0=128 1=1 5=1 6=16384
1 1 303 305
1 2 305 305_splitncnn_0 305_splitncnn_1
1 1 305_splitncnn_1 306 0=128 1=3 3=2 4=1 5=1 6=147456
1 1 306 308
2 1 308 238_splitncnn_0 309
1 2 309 309_splitncnn_0 309_splitncnn_1
1 1 309_splitncnn_1 310 0=128 1=1 5=1 6=32768
1 1 310 312
1 1 312 313 0=128 1=1 5=1 6=16384
1 1 313 315
1 1 315 316 0=128 1=3 4=1 5=1 6=147456
1 1 316 318
1 1 309_splitncnn_0 319 0=128 1=1 5=1 6=32768
1 1 319 321
2 1 318 321 322
1 1 322 323 0=256 1=1 5=1 6=65536
1 1 323 325
1 1 285_splitncnn_0 326 0=36 1=1 5=1 6=2304
1 1 326 338 0=-1 1=12 2=3
1 1 338 output 0=1
1 1 305_splitncnn_0 340 0=36 1=1 5=1 6=4608
1 1 340 352 0=-1 1=12 2=3
1 1 352 353 0=1
1 1 325 354 0=36 1=1 5=1 6=9216
1 1 354 366 0=-1 1=12 2=3
1 1 366 367 0=1

deepage · 2022-03-25T01:46:10Z

现在强烈建议采用pnnx来转换pytorch模型，这里贴一下nihui新写的使用教程https://zhuanlan.zhihu.com/p/471357671

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yolov5 训练一个模型，转成fp16用NCNN部署到移动端，发现ncnn侧得分超低，并位置都不对 #3625

yolov5 训练一个模型，转成fp16用NCNN部署到移动端，发现ncnn侧得分超低，并位置都不对 #3625

ouxiand commented Mar 16, 2022 •

edited

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ouxiand commented Mar 16, 2022

deepage commented Mar 25, 2022

yolov5 训练一个模型，转成fp16用NCNN部署到移动端，发现ncnn侧得分超低，并位置都不对 #3625

yolov5 训练一个模型，转成fp16用NCNN部署到移动端，发现ncnn侧得分超低，并位置都不对 #3625

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ouxiand commented Mar 16, 2022 • edited Loading

ouxiand commented Mar 16, 2022

deepage commented Mar 25, 2022

ouxiand commented Mar 16, 2022 •

edited

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