├── MobileNetSSD_deploy.caffemodel ├── MobileNetSSD_deploy.prototxt ├── README.md ├── color_00050.jpg ├── main.cpp ├── mobileNet_ssd_demo.pro ├── mobilenetssd.cpp └── mobilenetssd.h /MobileNetSSD_deploy.caffemodel: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/maomaoyuchengzi/MobileNetSSD-detect/90a3e8d552fe61511c564b265163a5bdf77f5685/MobileNetSSD_deploy.caffemodel -------------------------------------------------------------------------------- /MobileNetSSD_deploy.prototxt: -------------------------------------------------------------------------------- 1 | name: "MobileNet-SSD" 2 | input: "data" 3 | input_shape { 4 | dim: 1 5 | dim: 3 6 | dim: 300 7 | dim: 300 8 | } 9 | layer { 10 | name: "conv0" 11 | type: "Convolution" 12 | bottom: "data" 13 | top: "conv0" 14 | param { 15 | lr_mult: 1.0 16 | decay_mult: 1.0 17 | } 18 | param { 19 | lr_mult: 2.0 20 | decay_mult: 0.0 21 | } 22 | convolution_param { 23 | num_output: 32 24 | pad: 1 25 | kernel_size: 3 26 | stride: 2 27 | weight_filler { 28 | type: "msra" 29 | } 30 | bias_filler { 31 | type: "constant" 32 | value: 0.0 33 | } 34 | } 35 | } 36 | layer { 37 | name: "conv0/relu" 38 | type: "ReLU" 39 | bottom: "conv0" 40 | top: "conv0" 41 | } 42 | layer { 43 | name: "conv1/dw" 44 | type: "DepthwiseConvolution" 45 | bottom: "conv0" 46 | top: "conv1/dw" 47 | param { 48 | lr_mult: 1.0 49 | decay_mult: 1.0 50 | } 51 | param { 52 | lr_mult: 2.0 53 | decay_mult: 0.0 54 | } 55 | convolution_param { 56 | num_output: 32 57 | pad: 1 58 | kernel_size: 3 59 | group: 32 60 | engine: CAFFE 61 | weight_filler { 62 | type: "msra" 63 | } 64 | bias_filler { 65 | type: "constant" 66 | value: 0.0 67 | } 68 | } 69 | } 70 | layer { 71 | name: "conv1/dw/relu" 72 | type: "ReLU" 73 | bottom: "conv1/dw" 74 | top: "conv1/dw" 75 | } 76 | layer { 77 | name: "conv1" 78 | type: "Convolution" 79 | bottom: "conv1/dw" 80 | top: "conv1" 81 | param { 82 | lr_mult: 1.0 83 | decay_mult: 1.0 84 | } 85 | param { 86 | lr_mult: 2.0 87 | decay_mult: 0.0 88 | } 89 | convolution_param { 90 | num_output: 64 91 | kernel_size: 1 92 | weight_filler { 93 | type: "msra" 94 | } 95 | bias_filler { 96 | type: "constant" 97 | value: 0.0 98 | } 99 | } 100 | } 101 | layer { 102 | name: "conv1/relu" 103 | type: "ReLU" 104 | bottom: "conv1" 105 | top: "conv1" 106 | } 107 | layer { 108 | name: "conv2/dw" 109 | type: "DepthwiseConvolution" 110 | bottom: "conv1" 111 | top: "conv2/dw" 112 | param { 113 | lr_mult: 1.0 114 | decay_mult: 1.0 115 | } 116 | param { 117 | lr_mult: 2.0 118 | decay_mult: 0.0 119 | } 120 | convolution_param { 121 | num_output: 64 122 | pad: 1 123 | kernel_size: 3 124 | stride: 2 125 | group: 64 126 | engine: CAFFE 127 | weight_filler { 128 | type: "msra" 129 | } 130 | bias_filler { 131 | type: "constant" 132 | value: 0.0 133 | } 134 | } 135 | } 136 | layer { 137 | name: "conv2/dw/relu" 138 | type: "ReLU" 139 | bottom: "conv2/dw" 140 | top: "conv2/dw" 141 | } 142 | layer { 143 | name: "conv2" 144 | type: "Convolution" 145 | bottom: "conv2/dw" 146 | top: "conv2" 147 | param { 148 | lr_mult: 1.0 149 | decay_mult: 1.0 150 | } 151 | param { 152 | lr_mult: 2.0 153 | decay_mult: 0.0 154 | } 155 | convolution_param { 156 | num_output: 128 157 | kernel_size: 1 158 | weight_filler { 159 | type: "msra" 160 | } 161 | bias_filler { 162 | type: "constant" 163 | value: 0.0 164 | } 165 | } 166 | } 167 | layer { 168 | name: "conv2/relu" 169 | type: "ReLU" 170 | bottom: "conv2" 171 | top: "conv2" 172 | } 173 | layer { 174 | name: "conv3/dw" 175 | type: "DepthwiseConvolution" 176 | bottom: "conv2" 177 | top: "conv3/dw" 178 | param { 179 | lr_mult: 1.0 180 | decay_mult: 1.0 181 | } 182 | param { 183 | lr_mult: 2.0 184 | decay_mult: 0.0 185 | } 186 | convolution_param { 187 | num_output: 128 188 | pad: 1 189 | kernel_size: 3 190 | group: 128 191 | engine: CAFFE 192 | weight_filler { 193 | type: "msra" 194 | } 195 | bias_filler { 196 | type: "constant" 197 | value: 0.0 198 | } 199 | } 200 | } 201 | layer { 202 | name: "conv3/dw/relu" 203 | type: "ReLU" 204 | bottom: "conv3/dw" 205 | top: "conv3/dw" 206 | } 207 | layer { 208 | name: "conv3" 209 | type: "Convolution" 210 | bottom: "conv3/dw" 211 | top: "conv3" 212 | param { 213 | lr_mult: 1.0 214 | decay_mult: 1.0 215 | } 216 | param { 217 | lr_mult: 2.0 218 | decay_mult: 0.0 219 | } 220 | convolution_param { 221 | num_output: 128 222 | kernel_size: 1 223 | weight_filler { 224 | type: "msra" 225 | } 226 | bias_filler { 227 | type: "constant" 228 | value: 0.0 229 | } 230 | } 231 | } 232 | layer { 233 | name: "conv3/relu" 234 | type: "ReLU" 235 | bottom: "conv3" 236 | top: "conv3" 237 | } 238 | layer { 239 | name: "conv4/dw" 240 | type: "DepthwiseConvolution" 241 | bottom: "conv3" 242 | top: "conv4/dw" 243 | param { 244 | lr_mult: 1.0 245 | decay_mult: 1.0 246 | } 247 | param { 248 | lr_mult: 2.0 249 | decay_mult: 0.0 250 | } 251 | convolution_param { 252 | num_output: 128 253 | pad: 1 254 | kernel_size: 3 255 | stride: 2 256 | group: 128 257 | engine: CAFFE 258 | weight_filler { 259 | type: "msra" 260 | } 261 | bias_filler { 262 | type: "constant" 263 | value: 0.0 264 | } 265 | } 266 | } 267 | layer { 268 | name: "conv4/dw/relu" 269 | type: "ReLU" 270 | bottom: "conv4/dw" 271 | top: "conv4/dw" 272 | } 273 | layer { 274 | name: "conv4" 275 | type: "Convolution" 276 | bottom: "conv4/dw" 277 | top: "conv4" 278 | param { 279 | lr_mult: 1.0 280 | decay_mult: 1.0 281 | } 282 | param { 283 | lr_mult: 2.0 284 | decay_mult: 0.0 285 | } 286 | convolution_param { 287 | num_output: 256 288 | kernel_size: 1 289 | weight_filler { 290 | type: "msra" 291 | } 292 | bias_filler { 293 | type: "constant" 294 | value: 0.0 295 | } 296 | } 297 | } 298 | layer { 299 | name: "conv4/relu" 300 | type: "ReLU" 301 | bottom: "conv4" 302 | top: "conv4" 303 | } 304 | layer { 305 | name: "conv5/dw" 306 | type: "DepthwiseConvolution" 307 | bottom: "conv4" 308 | top: "conv5/dw" 309 | param { 310 | lr_mult: 1.0 311 | decay_mult: 1.0 312 | } 313 | param { 314 | lr_mult: 2.0 315 | decay_mult: 0.0 316 | } 317 | convolution_param { 318 | num_output: 256 319 | pad: 1 320 | kernel_size: 3 321 | group: 256 322 | engine: CAFFE 323 | weight_filler { 324 | type: "msra" 325 | } 326 | bias_filler { 327 | type: "constant" 328 | value: 0.0 329 | } 330 | } 331 | } 332 | layer { 333 | name: "conv5/dw/relu" 334 | type: "ReLU" 335 | bottom: "conv5/dw" 336 | top: "conv5/dw" 337 | } 338 | layer { 339 | name: "conv5" 340 | type: "Convolution" 341 | bottom: "conv5/dw" 342 | top: "conv5" 343 | param { 344 | lr_mult: 1.0 345 | decay_mult: 1.0 346 | } 347 | param { 348 | lr_mult: 2.0 349 | decay_mult: 0.0 350 | } 351 | convolution_param { 352 | num_output: 256 353 | kernel_size: 1 354 | weight_filler { 355 | type: "msra" 356 | } 357 | bias_filler { 358 | type: "constant" 359 | value: 0.0 360 | } 361 | } 362 | } 363 | layer { 364 | name: "conv5/relu" 365 | type: "ReLU" 366 | bottom: "conv5" 367 | top: "conv5" 368 | } 369 | layer { 370 | name: "conv6/dw" 371 | type: "DepthwiseConvolution" 372 | bottom: "conv5" 373 | top: "conv6/dw" 374 | param { 375 | lr_mult: 1.0 376 | decay_mult: 1.0 377 | } 378 | param { 379 | lr_mult: 2.0 380 | decay_mult: 0.0 381 | } 382 | convolution_param { 383 | num_output: 256 384 | pad: 1 385 | kernel_size: 3 386 | stride: 2 387 | group: 256 388 | engine: CAFFE 389 | weight_filler { 390 | type: "msra" 391 | } 392 | bias_filler { 393 | type: "constant" 394 | value: 0.0 395 | } 396 | } 397 | } 398 | layer { 399 | name: "conv6/dw/relu" 400 | type: "ReLU" 401 | bottom: "conv6/dw" 402 | top: "conv6/dw" 403 | } 404 | layer { 405 | name: "conv6" 406 | type: "Convolution" 407 | bottom: "conv6/dw" 408 | top: "conv6" 409 | param { 410 | lr_mult: 1.0 411 | decay_mult: 1.0 412 | } 413 | param { 414 | lr_mult: 2.0 415 | decay_mult: 0.0 416 | } 417 | convolution_param { 418 | num_output: 512 419 | kernel_size: 1 420 | weight_filler { 421 | type: "msra" 422 | } 423 | bias_filler { 424 | type: "constant" 425 | value: 0.0 426 | } 427 | } 428 | } 429 | layer { 430 | name: "conv6/relu" 431 | type: "ReLU" 432 | bottom: "conv6" 433 | top: "conv6" 434 | } 435 | layer { 436 | name: "conv7/dw" 437 | type: "DepthwiseConvolution" 438 | bottom: "conv6" 439 | top: "conv7/dw" 440 | param { 441 | lr_mult: 1.0 442 | decay_mult: 1.0 443 | } 444 | param { 445 | lr_mult: 2.0 446 | decay_mult: 0.0 447 | } 448 | convolution_param { 449 | num_output: 512 450 | pad: 1 451 | kernel_size: 3 452 | group: 512 453 | engine: CAFFE 454 | weight_filler { 455 | type: "msra" 456 | } 457 | bias_filler { 458 | type: "constant" 459 | value: 0.0 460 | } 461 | } 462 | } 463 | layer { 464 | name: "conv7/dw/relu" 465 | type: "ReLU" 466 | bottom: "conv7/dw" 467 | top: "conv7/dw" 468 | } 469 | layer { 470 | name: "conv7" 471 | type: "Convolution" 472 | bottom: "conv7/dw" 473 | top: "conv7" 474 | param { 475 | lr_mult: 1.0 476 | decay_mult: 1.0 477 | } 478 | param { 479 | lr_mult: 2.0 480 | decay_mult: 0.0 481 | } 482 | convolution_param { 483 | num_output: 512 484 | kernel_size: 1 485 | weight_filler { 486 | type: "msra" 487 | } 488 | bias_filler { 489 | type: "constant" 490 | value: 0.0 491 | } 492 | } 493 | } 494 | layer { 495 | name: "conv7/relu" 496 | type: "ReLU" 497 | bottom: "conv7" 498 | top: "conv7" 499 | } 500 | layer { 501 | name: "conv8/dw" 502 | type: "DepthwiseConvolution" 503 | bottom: "conv7" 504 | top: "conv8/dw" 505 | param { 506 | lr_mult: 1.0 507 | decay_mult: 1.0 508 | } 509 | param { 510 | lr_mult: 2.0 511 | decay_mult: 0.0 512 | } 513 | convolution_param { 514 | num_output: 512 515 | pad: 1 516 | kernel_size: 3 517 | group: 512 518 | engine: CAFFE 519 | weight_filler { 520 | type: "msra" 521 | } 522 | bias_filler { 523 | type: "constant" 524 | value: 0.0 525 | } 526 | } 527 | } 528 | layer { 529 | name: "conv8/dw/relu" 530 | type: "ReLU" 531 | bottom: "conv8/dw" 532 | top: "conv8/dw" 533 | } 534 | layer { 535 | name: "conv8" 536 | type: "Convolution" 537 | bottom: "conv8/dw" 538 | top: "conv8" 539 | param { 540 | lr_mult: 1.0 541 | decay_mult: 1.0 542 | } 543 | param { 544 | lr_mult: 2.0 545 | decay_mult: 0.0 546 | } 547 | convolution_param { 548 | num_output: 512 549 | kernel_size: 1 550 | weight_filler { 551 | type: "msra" 552 | } 553 | bias_filler { 554 | type: "constant" 555 | value: 0.0 556 | } 557 | } 558 | } 559 | layer { 560 | name: "conv8/relu" 561 | type: "ReLU" 562 | bottom: "conv8" 563 | top: "conv8" 564 | } 565 | layer { 566 | name: "conv9/dw" 567 | type: "DepthwiseConvolution" 568 | bottom: "conv8" 569 | top: "conv9/dw" 570 | param { 571 | lr_mult: 1.0 572 | decay_mult: 1.0 573 | } 574 | param { 575 | lr_mult: 2.0 576 | decay_mult: 0.0 577 | } 578 | convolution_param { 579 | num_output: 512 580 | pad: 1 581 | kernel_size: 3 582 | group: 512 583 | engine: CAFFE 584 | weight_filler { 585 | type: "msra" 586 | } 587 | bias_filler { 588 | type: "constant" 589 | value: 0.0 590 | } 591 | } 592 | } 593 | layer { 594 | name: "conv9/dw/relu" 595 | type: "ReLU" 596 | bottom: "conv9/dw" 597 | top: "conv9/dw" 598 | } 599 | layer { 600 | name: "conv9" 601 | type: "Convolution" 602 | bottom: "conv9/dw" 603 | top: "conv9" 604 | param { 605 | lr_mult: 1.0 606 | decay_mult: 1.0 607 | } 608 | param { 609 | lr_mult: 2.0 610 | decay_mult: 0.0 611 | } 612 | convolution_param { 613 | num_output: 512 614 | kernel_size: 1 615 | weight_filler { 616 | type: "msra" 617 | } 618 | bias_filler { 619 | type: "constant" 620 | value: 0.0 621 | } 622 | } 623 | } 624 | layer { 625 | name: "conv9/relu" 626 | type: "ReLU" 627 | bottom: "conv9" 628 | top: "conv9" 629 | } 630 | layer { 631 | name: "conv10/dw" 632 | type: "DepthwiseConvolution" 633 | bottom: "conv9" 634 | top: "conv10/dw" 635 | param { 636 | lr_mult: 1.0 637 | decay_mult: 1.0 638 | } 639 | param { 640 | lr_mult: 2.0 641 | decay_mult: 0.0 642 | } 643 | convolution_param { 644 | num_output: 512 645 | pad: 1 646 | kernel_size: 3 647 | group: 512 648 | engine: CAFFE 649 | weight_filler { 650 | type: "msra" 651 | } 652 | bias_filler { 653 | type: "constant" 654 | value: 0.0 655 | } 656 | } 657 | } 658 | layer { 659 | name: "conv10/dw/relu" 660 | type: "ReLU" 661 | bottom: "conv10/dw" 662 | top: "conv10/dw" 663 | } 664 | layer { 665 | name: "conv10" 666 | type: "Convolution" 667 | bottom: "conv10/dw" 668 | top: "conv10" 669 | param { 670 | lr_mult: 1.0 671 | decay_mult: 1.0 672 | } 673 | param { 674 | lr_mult: 2.0 675 | decay_mult: 0.0 676 | } 677 | convolution_param { 678 | num_output: 512 679 | kernel_size: 1 680 | weight_filler { 681 | type: "msra" 682 | } 683 | bias_filler { 684 | type: "constant" 685 | value: 0.0 686 | } 687 | } 688 | } 689 | layer { 690 | name: "conv10/relu" 691 | type: "ReLU" 692 | bottom: "conv10" 693 | top: "conv10" 694 | } 695 | layer { 696 | name: "conv11/dw" 697 | type: "DepthwiseConvolution" 698 | bottom: "conv10" 699 | top: "conv11/dw" 700 | param { 701 | lr_mult: 1.0 702 | decay_mult: 1.0 703 | } 704 | param { 705 | lr_mult: 2.0 706 | decay_mult: 0.0 707 | } 708 | convolution_param { 709 | num_output: 512 710 | pad: 1 711 | kernel_size: 3 712 | group: 512 713 | engine: CAFFE 714 | weight_filler { 715 | type: "msra" 716 | } 717 | bias_filler { 718 | type: "constant" 719 | value: 0.0 720 | } 721 | } 722 | } 723 | layer { 724 | name: "conv11/dw/relu" 725 | type: "ReLU" 726 | bottom: "conv11/dw" 727 | top: "conv11/dw" 728 | } 729 | layer { 730 | name: "conv11" 731 | type: "Convolution" 732 | bottom: "conv11/dw" 733 | top: "conv11" 734 | param { 735 | lr_mult: 1.0 736 | decay_mult: 1.0 737 | } 738 | param { 739 | lr_mult: 2.0 740 | decay_mult: 0.0 741 | } 742 | convolution_param { 743 | num_output: 512 744 | kernel_size: 1 745 | weight_filler { 746 | type: "msra" 747 | } 748 | bias_filler { 749 | type: "constant" 750 | value: 0.0 751 | } 752 | } 753 | } 754 | layer { 755 | name: "conv11/relu" 756 | type: "ReLU" 757 | bottom: "conv11" 758 | top: "conv11" 759 | } 760 | layer { 761 | name: "conv12/dw" 762 | type: "DepthwiseConvolution" 763 | bottom: "conv11" 764 | top: "conv12/dw" 765 | param { 766 | lr_mult: 1.0 767 | decay_mult: 1.0 768 | } 769 | param { 770 | lr_mult: 2.0 771 | decay_mult: 0.0 772 | } 773 | convolution_param { 774 | num_output: 512 775 | pad: 1 776 | kernel_size: 3 777 | stride: 2 778 | group: 512 779 | engine: CAFFE 780 | weight_filler { 781 | type: "msra" 782 | } 783 | bias_filler { 784 | type: "constant" 785 | value: 0.0 786 | } 787 | } 788 | } 789 | layer { 790 | name: "conv12/dw/relu" 791 | type: "ReLU" 792 | bottom: "conv12/dw" 793 | top: "conv12/dw" 794 | } 795 | layer { 796 | name: "conv12" 797 | type: "Convolution" 798 | bottom: "conv12/dw" 799 | top: "conv12" 800 | param { 801 | lr_mult: 1.0 802 | decay_mult: 1.0 803 | } 804 | param { 805 | lr_mult: 2.0 806 | decay_mult: 0.0 807 | } 808 | convolution_param { 809 | num_output: 1024 810 | kernel_size: 1 811 | weight_filler { 812 | type: "msra" 813 | } 814 | bias_filler { 815 | type: "constant" 816 | value: 0.0 817 | } 818 | } 819 | } 820 | layer { 821 | name: "conv12/relu" 822 | type: "ReLU" 823 | bottom: "conv12" 824 | top: "conv12" 825 | } 826 | layer { 827 | name: "conv13/dw" 828 | type: "DepthwiseConvolution" 829 | bottom: "conv12" 830 | top: "conv13/dw" 831 | param { 832 | lr_mult: 1.0 833 | decay_mult: 1.0 834 | } 835 | param { 836 | lr_mult: 2.0 837 | decay_mult: 0.0 838 | } 839 | convolution_param { 840 | num_output: 1024 841 | pad: 1 842 | kernel_size: 3 843 | group: 1024 844 | engine: CAFFE 845 | weight_filler { 846 | type: "msra" 847 | } 848 | bias_filler { 849 | type: "constant" 850 | value: 0.0 851 | } 852 | } 853 | } 854 | layer { 855 | name: "conv13/dw/relu" 856 | type: "ReLU" 857 | bottom: "conv13/dw" 858 | top: "conv13/dw" 859 | } 860 | layer { 861 | name: "conv13" 862 | type: "Convolution" 863 | bottom: "conv13/dw" 864 | top: "conv13" 865 | param { 866 | lr_mult: 1.0 867 | decay_mult: 1.0 868 | } 869 | param { 870 | lr_mult: 2.0 871 | decay_mult: 0.0 872 | } 873 | convolution_param { 874 | num_output: 1024 875 | kernel_size: 1 876 | weight_filler { 877 | type: "msra" 878 | } 879 | bias_filler { 880 | type: "constant" 881 | value: 0.0 882 | } 883 | } 884 | } 885 | layer { 886 | name: "conv13/relu" 887 | type: "ReLU" 888 | bottom: "conv13" 889 | top: "conv13" 890 | } 891 | layer { 892 | name: "conv14_1" 893 | type: "Convolution" 894 | bottom: "conv13" 895 | top: "conv14_1" 896 | param { 897 | lr_mult: 1.0 898 | decay_mult: 1.0 899 | } 900 | param { 901 | lr_mult: 2.0 902 | decay_mult: 0.0 903 | } 904 | convolution_param { 905 | num_output: 256 906 | kernel_size: 1 907 | weight_filler { 908 | type: "msra" 909 | } 910 | bias_filler { 911 | type: "constant" 912 | value: 0.0 913 | } 914 | } 915 | } 916 | layer { 917 | name: "conv14_1/relu" 918 | type: "ReLU" 919 | bottom: "conv14_1" 920 | top: "conv14_1" 921 | } 922 | layer { 923 | name: "conv14_2" 924 | type: "Convolution" 925 | bottom: "conv14_1" 926 | top: "conv14_2" 927 | param { 928 | lr_mult: 1.0 929 | decay_mult: 1.0 930 | } 931 | param { 932 | lr_mult: 2.0 933 | decay_mult: 0.0 934 | } 935 | convolution_param { 936 | num_output: 512 937 | pad: 1 938 | kernel_size: 3 939 | stride: 2 940 | weight_filler { 941 | type: "msra" 942 | } 943 | bias_filler { 944 | type: "constant" 945 | value: 0.0 946 | } 947 | } 948 | } 949 | layer { 950 | name: "conv14_2/relu" 951 | type: "ReLU" 952 | bottom: "conv14_2" 953 | top: "conv14_2" 954 | } 955 | layer { 956 | name: "conv15_1" 957 | type: "Convolution" 958 | bottom: "conv14_2" 959 | top: "conv15_1" 960 | param { 961 | lr_mult: 1.0 962 | decay_mult: 1.0 963 | } 964 | param { 965 | lr_mult: 2.0 966 | decay_mult: 0.0 967 | } 968 | convolution_param { 969 | num_output: 128 970 | kernel_size: 1 971 | weight_filler { 972 | type: "msra" 973 | } 974 | bias_filler { 975 | type: "constant" 976 | value: 0.0 977 | } 978 | } 979 | } 980 | layer { 981 | name: "conv15_1/relu" 982 | type: "ReLU" 983 | bottom: "conv15_1" 984 | top: "conv15_1" 985 | } 986 | layer { 987 | name: "conv15_2" 988 | type: "Convolution" 989 | bottom: "conv15_1" 990 | top: "conv15_2" 991 | param { 992 | lr_mult: 1.0 993 | decay_mult: 1.0 994 | } 995 | param { 996 | lr_mult: 2.0 997 | decay_mult: 0.0 998 | } 999 | convolution_param { 1000 | num_output: 256 1001 | pad: 1 1002 | kernel_size: 3 1003 | stride: 2 1004 | weight_filler { 1005 | type: "msra" 1006 | } 1007 | bias_filler { 1008 | type: "constant" 1009 | value: 0.0 1010 | } 1011 | } 1012 | } 1013 | layer { 1014 | name: "conv15_2/relu" 1015 | type: "ReLU" 1016 | bottom: "conv15_2" 1017 | top: "conv15_2" 1018 | } 1019 | layer { 1020 | name: "conv16_1" 1021 | type: "Convolution" 1022 | bottom: "conv15_2" 1023 | top: "conv16_1" 1024 | param { 1025 | lr_mult: 1.0 1026 | decay_mult: 1.0 1027 | } 1028 | param { 1029 | lr_mult: 2.0 1030 | decay_mult: 0.0 1031 | } 1032 | convolution_param { 1033 | num_output: 128 1034 | kernel_size: 1 1035 | weight_filler { 1036 | type: "msra" 1037 | } 1038 | bias_filler { 1039 | type: "constant" 1040 | value: 0.0 1041 | } 1042 | } 1043 | } 1044 | layer { 1045 | name: "conv16_1/relu" 1046 | type: "ReLU" 1047 | bottom: "conv16_1" 1048 | top: "conv16_1" 1049 | } 1050 | layer { 1051 | name: "conv16_2" 1052 | type: "Convolution" 1053 | bottom: "conv16_1" 1054 | top: "conv16_2" 1055 | param { 1056 | lr_mult: 1.0 1057 | decay_mult: 1.0 1058 | } 1059 | param { 1060 | lr_mult: 2.0 1061 | decay_mult: 0.0 1062 | } 1063 | convolution_param { 1064 | num_output: 256 1065 | pad: 1 1066 | kernel_size: 3 1067 | stride: 2 1068 | weight_filler { 1069 | type: "msra" 1070 | } 1071 | bias_filler { 1072 | type: "constant" 1073 | value: 0.0 1074 | } 1075 | } 1076 | } 1077 | layer { 1078 | name: "conv16_2/relu" 1079 | type: "ReLU" 1080 | bottom: "conv16_2" 1081 | top: "conv16_2" 1082 | } 1083 | layer { 1084 | name: "conv17_1" 1085 | type: "Convolution" 1086 | bottom: "conv16_2" 1087 | top: "conv17_1" 1088 | param { 1089 | lr_mult: 1.0 1090 | decay_mult: 1.0 1091 | } 1092 | param { 1093 | lr_mult: 2.0 1094 | decay_mult: 0.0 1095 | } 1096 | convolution_param { 1097 | num_output: 64 1098 | kernel_size: 1 1099 | weight_filler { 1100 | type: "msra" 1101 | } 1102 | bias_filler { 1103 | type: "constant" 1104 | value: 0.0 1105 | } 1106 | } 1107 | } 1108 | layer { 1109 | name: "conv17_1/relu" 1110 | type: "ReLU" 1111 | bottom: "conv17_1" 1112 | top: "conv17_1" 1113 | } 1114 | layer { 1115 | name: "conv17_2" 1116 | type: "Convolution" 1117 | bottom: "conv17_1" 1118 | top: "conv17_2" 1119 | param { 1120 | lr_mult: 1.0 1121 | decay_mult: 1.0 1122 | } 1123 | param { 1124 | lr_mult: 2.0 1125 | decay_mult: 0.0 1126 | } 1127 | convolution_param { 1128 | num_output: 128 1129 | pad: 1 1130 | kernel_size: 3 1131 | stride: 2 1132 | weight_filler { 1133 | type: "msra" 1134 | } 1135 | bias_filler { 1136 | type: "constant" 1137 | value: 0.0 1138 | } 1139 | } 1140 | } 1141 | layer { 1142 | name: "conv17_2/relu" 1143 | type: "ReLU" 1144 | bottom: "conv17_2" 1145 | top: "conv17_2" 1146 | } 1147 | layer { 1148 | name: "conv11_mbox_loc" 1149 | type: "Convolution" 1150 | bottom: "conv11" 1151 | top: "conv11_mbox_loc" 1152 | param { 1153 | lr_mult: 1.0 1154 | decay_mult: 1.0 1155 | } 1156 | param { 1157 | lr_mult: 2.0 1158 | decay_mult: 0.0 1159 | } 1160 | convolution_param { 1161 | num_output: 12 1162 | kernel_size: 1 1163 | weight_filler { 1164 | type: "msra" 1165 | } 1166 | bias_filler { 1167 | type: "constant" 1168 | value: 0.0 1169 | } 1170 | } 1171 | } 1172 | layer { 1173 | name: "conv11_mbox_loc_perm" 1174 | type: "Permute" 1175 | bottom: "conv11_mbox_loc" 1176 | top: "conv11_mbox_loc_perm" 1177 | permute_param { 1178 | order: 0 1179 | order: 2 1180 | order: 3 1181 | order: 1 1182 | } 1183 | } 1184 | layer { 1185 | name: "conv11_mbox_loc_flat" 1186 | type: "Flatten" 1187 | bottom: "conv11_mbox_loc_perm" 1188 | top: "conv11_mbox_loc_flat" 1189 | flatten_param { 1190 | axis: 1 1191 | } 1192 | } 1193 | layer { 1194 | name: "conv11_mbox_conf" 1195 | type: "Convolution" 1196 | bottom: "conv11" 1197 | top: "conv11_mbox_conf" 1198 | param { 1199 | lr_mult: 1.0 1200 | decay_mult: 1.0 1201 | } 1202 | param { 1203 | lr_mult: 2.0 1204 | decay_mult: 0.0 1205 | } 1206 | convolution_param { 1207 | num_output: 63 1208 | kernel_size: 1 1209 | weight_filler { 1210 | type: "msra" 1211 | } 1212 | bias_filler { 1213 | type: "constant" 1214 | value: 0.0 1215 | } 1216 | } 1217 | } 1218 | layer { 1219 | name: "conv11_mbox_conf_perm" 1220 | type: "Permute" 1221 | bottom: "conv11_mbox_conf" 1222 | top: "conv11_mbox_conf_perm" 1223 | permute_param { 1224 | order: 0 1225 | order: 2 1226 | order: 3 1227 | order: 1 1228 | } 1229 | } 1230 | layer { 1231 | name: "conv11_mbox_conf_flat" 1232 | type: "Flatten" 1233 | bottom: "conv11_mbox_conf_perm" 1234 | top: "conv11_mbox_conf_flat" 1235 | flatten_param { 1236 | axis: 1 1237 | } 1238 | } 1239 | layer { 1240 | name: "conv11_mbox_priorbox" 1241 | type: "PriorBox" 1242 | bottom: "conv11" 1243 | bottom: "data" 1244 | top: "conv11_mbox_priorbox" 1245 | prior_box_param { 1246 | min_size: 60.0 1247 | aspect_ratio: 2.0 1248 | flip: true 1249 | clip: false 1250 | variance: 0.1 1251 | variance: 0.1 1252 | variance: 0.2 1253 | variance: 0.2 1254 | offset: 0.5 1255 | } 1256 | } 1257 | layer { 1258 | name: "conv13_mbox_loc" 1259 | type: "Convolution" 1260 | bottom: "conv13" 1261 | top: "conv13_mbox_loc" 1262 | param { 1263 | lr_mult: 1.0 1264 | decay_mult: 1.0 1265 | } 1266 | param { 1267 | lr_mult: 2.0 1268 | decay_mult: 0.0 1269 | } 1270 | convolution_param { 1271 | num_output: 24 1272 | kernel_size: 1 1273 | weight_filler { 1274 | type: "msra" 1275 | } 1276 | bias_filler { 1277 | type: "constant" 1278 | value: 0.0 1279 | } 1280 | } 1281 | } 1282 | layer { 1283 | name: "conv13_mbox_loc_perm" 1284 | type: "Permute" 1285 | bottom: "conv13_mbox_loc" 1286 | top: "conv13_mbox_loc_perm" 1287 | permute_param { 1288 | order: 0 1289 | order: 2 1290 | order: 3 1291 | order: 1 1292 | } 1293 | } 1294 | layer { 1295 | name: "conv13_mbox_loc_flat" 1296 | type: "Flatten" 1297 | bottom: "conv13_mbox_loc_perm" 1298 | top: "conv13_mbox_loc_flat" 1299 | flatten_param { 1300 | axis: 1 1301 | } 1302 | } 1303 | layer { 1304 | name: "conv13_mbox_conf" 1305 | type: "Convolution" 1306 | bottom: "conv13" 1307 | top: "conv13_mbox_conf" 1308 | param { 1309 | lr_mult: 1.0 1310 | decay_mult: 1.0 1311 | } 1312 | param { 1313 | lr_mult: 2.0 1314 | decay_mult: 0.0 1315 | } 1316 | convolution_param { 1317 | num_output: 126 1318 | kernel_size: 1 1319 | weight_filler { 1320 | type: "msra" 1321 | } 1322 | bias_filler { 1323 | type: "constant" 1324 | value: 0.0 1325 | } 1326 | } 1327 | } 1328 | layer { 1329 | name: "conv13_mbox_conf_perm" 1330 | type: "Permute" 1331 | bottom: "conv13_mbox_conf" 1332 | top: "conv13_mbox_conf_perm" 1333 | permute_param { 1334 | order: 0 1335 | order: 2 1336 | order: 3 1337 | order: 1 1338 | } 1339 | } 1340 | layer { 1341 | name: "conv13_mbox_conf_flat" 1342 | type: "Flatten" 1343 | bottom: "conv13_mbox_conf_perm" 1344 | top: "conv13_mbox_conf_flat" 1345 | flatten_param { 1346 | axis: 1 1347 | } 1348 | } 1349 | layer { 1350 | name: "conv13_mbox_priorbox" 1351 | type: "PriorBox" 1352 | bottom: "conv13" 1353 | bottom: "data" 1354 | top: "conv13_mbox_priorbox" 1355 | prior_box_param { 1356 | min_size: 105.0 1357 | max_size: 150.0 1358 | aspect_ratio: 2.0 1359 | aspect_ratio: 3.0 1360 | flip: true 1361 | clip: false 1362 | variance: 0.1 1363 | variance: 0.1 1364 | variance: 0.2 1365 | variance: 0.2 1366 | offset: 0.5 1367 | } 1368 | } 1369 | layer { 1370 | name: "conv14_2_mbox_loc" 1371 | type: "Convolution" 1372 | bottom: "conv14_2" 1373 | top: "conv14_2_mbox_loc" 1374 | param { 1375 | lr_mult: 1.0 1376 | decay_mult: 1.0 1377 | } 1378 | param { 1379 | lr_mult: 2.0 1380 | decay_mult: 0.0 1381 | } 1382 | convolution_param { 1383 | num_output: 24 1384 | kernel_size: 1 1385 | weight_filler { 1386 | type: "msra" 1387 | } 1388 | bias_filler { 1389 | type: "constant" 1390 | value: 0.0 1391 | } 1392 | } 1393 | } 1394 | layer { 1395 | name: "conv14_2_mbox_loc_perm" 1396 | type: "Permute" 1397 | bottom: "conv14_2_mbox_loc" 1398 | top: "conv14_2_mbox_loc_perm" 1399 | permute_param { 1400 | order: 0 1401 | order: 2 1402 | order: 3 1403 | order: 1 1404 | } 1405 | } 1406 | layer { 1407 | name: "conv14_2_mbox_loc_flat" 1408 | type: "Flatten" 1409 | bottom: "conv14_2_mbox_loc_perm" 1410 | top: "conv14_2_mbox_loc_flat" 1411 | flatten_param { 1412 | axis: 1 1413 | } 1414 | } 1415 | layer { 1416 | name: "conv14_2_mbox_conf" 1417 | type: "Convolution" 1418 | bottom: "conv14_2" 1419 | top: "conv14_2_mbox_conf" 1420 | param { 1421 | lr_mult: 1.0 1422 | decay_mult: 1.0 1423 | } 1424 | param { 1425 | lr_mult: 2.0 1426 | decay_mult: 0.0 1427 | } 1428 | convolution_param { 1429 | num_output: 126 1430 | kernel_size: 1 1431 | weight_filler { 1432 | type: "msra" 1433 | } 1434 | bias_filler { 1435 | type: "constant" 1436 | value: 0.0 1437 | } 1438 | } 1439 | } 1440 | layer { 1441 | name: "conv14_2_mbox_conf_perm" 1442 | type: "Permute" 1443 | bottom: "conv14_2_mbox_conf" 1444 | top: "conv14_2_mbox_conf_perm" 1445 | permute_param { 1446 | order: 0 1447 | order: 2 1448 | order: 3 1449 | order: 1 1450 | } 1451 | } 1452 | layer { 1453 | name: "conv14_2_mbox_conf_flat" 1454 | type: "Flatten" 1455 | bottom: "conv14_2_mbox_conf_perm" 1456 | top: "conv14_2_mbox_conf_flat" 1457 | flatten_param { 1458 | axis: 1 1459 | } 1460 | } 1461 | layer { 1462 | name: "conv14_2_mbox_priorbox" 1463 | type: "PriorBox" 1464 | bottom: "conv14_2" 1465 | bottom: "data" 1466 | top: "conv14_2_mbox_priorbox" 1467 | prior_box_param { 1468 | min_size: 150.0 1469 | max_size: 195.0 1470 | aspect_ratio: 2.0 1471 | aspect_ratio: 3.0 1472 | flip: true 1473 | clip: false 1474 | variance: 0.1 1475 | variance: 0.1 1476 | variance: 0.2 1477 | variance: 0.2 1478 | offset: 0.5 1479 | } 1480 | } 1481 | layer { 1482 | name: "conv15_2_mbox_loc" 1483 | type: "Convolution" 1484 | bottom: "conv15_2" 1485 | top: "conv15_2_mbox_loc" 1486 | param { 1487 | lr_mult: 1.0 1488 | decay_mult: 1.0 1489 | } 1490 | param { 1491 | lr_mult: 2.0 1492 | decay_mult: 0.0 1493 | } 1494 | convolution_param { 1495 | num_output: 24 1496 | kernel_size: 1 1497 | weight_filler { 1498 | type: "msra" 1499 | } 1500 | bias_filler { 1501 | type: "constant" 1502 | value: 0.0 1503 | } 1504 | } 1505 | } 1506 | layer { 1507 | name: "conv15_2_mbox_loc_perm" 1508 | type: "Permute" 1509 | bottom: "conv15_2_mbox_loc" 1510 | top: "conv15_2_mbox_loc_perm" 1511 | permute_param { 1512 | order: 0 1513 | order: 2 1514 | order: 3 1515 | order: 1 1516 | } 1517 | } 1518 | layer { 1519 | name: "conv15_2_mbox_loc_flat" 1520 | type: "Flatten" 1521 | bottom: "conv15_2_mbox_loc_perm" 1522 | top: "conv15_2_mbox_loc_flat" 1523 | flatten_param { 1524 | axis: 1 1525 | } 1526 | } 1527 | layer { 1528 | name: "conv15_2_mbox_conf" 1529 | type: "Convolution" 1530 | bottom: "conv15_2" 1531 | top: "conv15_2_mbox_conf" 1532 | param { 1533 | lr_mult: 1.0 1534 | decay_mult: 1.0 1535 | } 1536 | param { 1537 | lr_mult: 2.0 1538 | decay_mult: 0.0 1539 | } 1540 | convolution_param { 1541 | num_output: 126 1542 | kernel_size: 1 1543 | weight_filler { 1544 | type: "msra" 1545 | } 1546 | bias_filler { 1547 | type: "constant" 1548 | value: 0.0 1549 | } 1550 | } 1551 | } 1552 | layer { 1553 | name: "conv15_2_mbox_conf_perm" 1554 | type: "Permute" 1555 | bottom: "conv15_2_mbox_conf" 1556 | top: "conv15_2_mbox_conf_perm" 1557 | permute_param { 1558 | order: 0 1559 | order: 2 1560 | order: 3 1561 | order: 1 1562 | } 1563 | } 1564 | layer { 1565 | name: "conv15_2_mbox_conf_flat" 1566 | type: "Flatten" 1567 | bottom: "conv15_2_mbox_conf_perm" 1568 | top: "conv15_2_mbox_conf_flat" 1569 | flatten_param { 1570 | axis: 1 1571 | } 1572 | } 1573 | layer { 1574 | name: "conv15_2_mbox_priorbox" 1575 | type: "PriorBox" 1576 | bottom: "conv15_2" 1577 | bottom: "data" 1578 | top: "conv15_2_mbox_priorbox" 1579 | prior_box_param { 1580 | min_size: 195.0 1581 | max_size: 240.0 1582 | aspect_ratio: 2.0 1583 | aspect_ratio: 3.0 1584 | flip: true 1585 | clip: false 1586 | variance: 0.1 1587 | variance: 0.1 1588 | variance: 0.2 1589 | variance: 0.2 1590 | offset: 0.5 1591 | } 1592 | } 1593 | layer { 1594 | name: "conv16_2_mbox_loc" 1595 | type: "Convolution" 1596 | bottom: "conv16_2" 1597 | top: "conv16_2_mbox_loc" 1598 | param { 1599 | lr_mult: 1.0 1600 | decay_mult: 1.0 1601 | } 1602 | param { 1603 | lr_mult: 2.0 1604 | decay_mult: 0.0 1605 | } 1606 | convolution_param { 1607 | num_output: 24 1608 | kernel_size: 1 1609 | weight_filler { 1610 | type: "msra" 1611 | } 1612 | bias_filler { 1613 | type: "constant" 1614 | value: 0.0 1615 | } 1616 | } 1617 | } 1618 | layer { 1619 | name: "conv16_2_mbox_loc_perm" 1620 | type: "Permute" 1621 | bottom: "conv16_2_mbox_loc" 1622 | top: "conv16_2_mbox_loc_perm" 1623 | permute_param { 1624 | order: 0 1625 | order: 2 1626 | order: 3 1627 | order: 1 1628 | } 1629 | } 1630 | layer { 1631 | name: "conv16_2_mbox_loc_flat" 1632 | type: "Flatten" 1633 | bottom: "conv16_2_mbox_loc_perm" 1634 | top: "conv16_2_mbox_loc_flat" 1635 | flatten_param { 1636 | axis: 1 1637 | } 1638 | } 1639 | layer { 1640 | name: "conv16_2_mbox_conf" 1641 | type: "Convolution" 1642 | bottom: "conv16_2" 1643 | top: "conv16_2_mbox_conf" 1644 | param { 1645 | lr_mult: 1.0 1646 | decay_mult: 1.0 1647 | } 1648 | param { 1649 | lr_mult: 2.0 1650 | decay_mult: 0.0 1651 | } 1652 | convolution_param { 1653 | num_output: 126 1654 | kernel_size: 1 1655 | weight_filler { 1656 | type: "msra" 1657 | } 1658 | bias_filler { 1659 | type: "constant" 1660 | value: 0.0 1661 | } 1662 | } 1663 | } 1664 | layer { 1665 | name: "conv16_2_mbox_conf_perm" 1666 | type: "Permute" 1667 | bottom: "conv16_2_mbox_conf" 1668 | top: "conv16_2_mbox_conf_perm" 1669 | permute_param { 1670 | order: 0 1671 | order: 2 1672 | order: 3 1673 | order: 1 1674 | } 1675 | } 1676 | layer { 1677 | name: "conv16_2_mbox_conf_flat" 1678 | type: "Flatten" 1679 | bottom: "conv16_2_mbox_conf_perm" 1680 | top: "conv16_2_mbox_conf_flat" 1681 | flatten_param { 1682 | axis: 1 1683 | } 1684 | } 1685 | layer { 1686 | name: "conv16_2_mbox_priorbox" 1687 | type: "PriorBox" 1688 | bottom: "conv16_2" 1689 | bottom: "data" 1690 | top: "conv16_2_mbox_priorbox" 1691 | prior_box_param { 1692 | min_size: 240.0 1693 | max_size: 285.0 1694 | aspect_ratio: 2.0 1695 | aspect_ratio: 3.0 1696 | flip: true 1697 | clip: false 1698 | variance: 0.1 1699 | variance: 0.1 1700 | variance: 0.2 1701 | variance: 0.2 1702 | offset: 0.5 1703 | } 1704 | } 1705 | layer { 1706 | name: "conv17_2_mbox_loc" 1707 | type: "Convolution" 1708 | bottom: "conv17_2" 1709 | top: "conv17_2_mbox_loc" 1710 | param { 1711 | lr_mult: 1.0 1712 | decay_mult: 1.0 1713 | } 1714 | param { 1715 | lr_mult: 2.0 1716 | decay_mult: 0.0 1717 | } 1718 | convolution_param { 1719 | num_output: 24 1720 | kernel_size: 1 1721 | weight_filler { 1722 | type: "msra" 1723 | } 1724 | bias_filler { 1725 | type: "constant" 1726 | value: 0.0 1727 | } 1728 | } 1729 | } 1730 | layer { 1731 | name: "conv17_2_mbox_loc_perm" 1732 | type: "Permute" 1733 | bottom: "conv17_2_mbox_loc" 1734 | top: "conv17_2_mbox_loc_perm" 1735 | permute_param { 1736 | order: 0 1737 | order: 2 1738 | order: 3 1739 | order: 1 1740 | } 1741 | } 1742 | layer { 1743 | name: "conv17_2_mbox_loc_flat" 1744 | type: "Flatten" 1745 | bottom: "conv17_2_mbox_loc_perm" 1746 | top: "conv17_2_mbox_loc_flat" 1747 | flatten_param { 1748 | axis: 1 1749 | } 1750 | } 1751 | layer { 1752 | name: "conv17_2_mbox_conf" 1753 | type: "Convolution" 1754 | bottom: "conv17_2" 1755 | top: "conv17_2_mbox_conf" 1756 | param { 1757 | lr_mult: 1.0 1758 | decay_mult: 1.0 1759 | } 1760 | param { 1761 | lr_mult: 2.0 1762 | decay_mult: 0.0 1763 | } 1764 | convolution_param { 1765 | num_output: 126 1766 | kernel_size: 1 1767 | weight_filler { 1768 | type: "msra" 1769 | } 1770 | bias_filler { 1771 | type: "constant" 1772 | value: 0.0 1773 | } 1774 | } 1775 | } 1776 | layer { 1777 | name: "conv17_2_mbox_conf_perm" 1778 | type: "Permute" 1779 | bottom: "conv17_2_mbox_conf" 1780 | top: "conv17_2_mbox_conf_perm" 1781 | permute_param { 1782 | order: 0 1783 | order: 2 1784 | order: 3 1785 | order: 1 1786 | } 1787 | } 1788 | layer { 1789 | name: "conv17_2_mbox_conf_flat" 1790 | type: "Flatten" 1791 | bottom: "conv17_2_mbox_conf_perm" 1792 | top: "conv17_2_mbox_conf_flat" 1793 | flatten_param { 1794 | axis: 1 1795 | } 1796 | } 1797 | layer { 1798 | name: "conv17_2_mbox_priorbox" 1799 | type: "PriorBox" 1800 | bottom: "conv17_2" 1801 | bottom: "data" 1802 | top: "conv17_2_mbox_priorbox" 1803 | prior_box_param { 1804 | min_size: 285.0 1805 | max_size: 300.0 1806 | aspect_ratio: 2.0 1807 | aspect_ratio: 3.0 1808 | flip: true 1809 | clip: false 1810 | variance: 0.1 1811 | variance: 0.1 1812 | variance: 0.2 1813 | variance: 0.2 1814 | offset: 0.5 1815 | } 1816 | } 1817 | layer { 1818 | name: "mbox_loc" 1819 | type: "Concat" 1820 | bottom: "conv11_mbox_loc_flat" 1821 | bottom: "conv13_mbox_loc_flat" 1822 | bottom: "conv14_2_mbox_loc_flat" 1823 | bottom: "conv15_2_mbox_loc_flat" 1824 | bottom: "conv16_2_mbox_loc_flat" 1825 | bottom: "conv17_2_mbox_loc_flat" 1826 | top: "mbox_loc" 1827 | concat_param { 1828 | axis: 1 1829 | } 1830 | } 1831 | layer { 1832 | name: "mbox_conf" 1833 | type: "Concat" 1834 | bottom: "conv11_mbox_conf_flat" 1835 | bottom: "conv13_mbox_conf_flat" 1836 | bottom: "conv14_2_mbox_conf_flat" 1837 | bottom: "conv15_2_mbox_conf_flat" 1838 | bottom: "conv16_2_mbox_conf_flat" 1839 | bottom: "conv17_2_mbox_conf_flat" 1840 | top: "mbox_conf" 1841 | concat_param { 1842 | axis: 1 1843 | } 1844 | } 1845 | layer { 1846 | name: "mbox_priorbox" 1847 | type: "Concat" 1848 | bottom: "conv11_mbox_priorbox" 1849 | bottom: "conv13_mbox_priorbox" 1850 | bottom: "conv14_2_mbox_priorbox" 1851 | bottom: "conv15_2_mbox_priorbox" 1852 | bottom: "conv16_2_mbox_priorbox" 1853 | bottom: "conv17_2_mbox_priorbox" 1854 | top: "mbox_priorbox" 1855 | concat_param { 1856 | axis: 2 1857 | } 1858 | } 1859 | layer { 1860 | name: "mbox_conf_reshape" 1861 | type: "Reshape" 1862 | bottom: "mbox_conf" 1863 | top: "mbox_conf_reshape" 1864 | reshape_param { 1865 | shape { 1866 | dim: 0 1867 | dim: -1 1868 | dim: 21 1869 | } 1870 | } 1871 | } 1872 | layer { 1873 | name: "mbox_conf_softmax" 1874 | type: "Softmax" 1875 | bottom: "mbox_conf_reshape" 1876 | top: "mbox_conf_softmax" 1877 | softmax_param { 1878 | axis: 2 1879 | } 1880 | } 1881 | layer { 1882 | name: "mbox_conf_flatten" 1883 | type: "Flatten" 1884 | bottom: "mbox_conf_softmax" 1885 | top: "mbox_conf_flatten" 1886 | flatten_param { 1887 | axis: 1 1888 | } 1889 | } 1890 | layer { 1891 | name: "detection_out" 1892 | type: "DetectionOutput" 1893 | bottom: "mbox_loc" 1894 | bottom: "mbox_conf_flatten" 1895 | bottom: "mbox_priorbox" 1896 | top: "detection_out" 1897 | include { 1898 | phase: TEST 1899 | } 1900 | detection_output_param { 1901 | num_classes: 21 1902 | share_location: true 1903 | background_label_id: 0 1904 | nms_param { 1905 | nms_threshold: 0.45 1906 | top_k: 100 1907 | } 1908 | code_type: CENTER_SIZE 1909 | keep_top_k: 100 1910 | confidence_threshold: 0.25 1911 | } 1912 | } 1913 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # MobileNetSSD-detect 2 | mobileNet SSD 基于caffe的前向检测 3 | -------------------------------------------------------------------------------- /color_00050.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/maomaoyuchengzi/MobileNetSSD-detect/90a3e8d552fe61511c564b265163a5bdf77f5685/color_00050.jpg -------------------------------------------------------------------------------- /main.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | 5 | using namespace cv; 6 | 7 | int main() 8 | { 9 | string modelfile = "/home/fuxueping/sdb/svn2018/model_acceleration/tensorRT/weight/mobileNetSSD/MobileNetSSD_deploy_ori.prototxt"; 10 | string weights = "/home/fuxueping/sdb/svn2018/model_acceleration/tensorRT/weight/mobileNetSSD/MobileNetSSD_deploy.caffemodel"; 11 | string imgDir = "/home/fuxueping/sdb/svn2018/model_acceleration/tensorRT/test_img/color_00050.jpg"; 12 | 13 | 14 | 15 | // Blob *input_data_blobs = net->input_blobs()[0]; 16 | 17 | Mat img =imread(imgDir); 18 | MobileNetSSD mobileNetSSD; 19 | mobileNetSSD.Initnet(modelfile,weights); 20 | mobileNetSSD.detect(img); 21 | // mobileNetSSD.destroydetect(); 22 | return 0; 23 | } 24 | -------------------------------------------------------------------------------- /mobileNet_ssd_demo.pro: -------------------------------------------------------------------------------- 1 | QT += core 2 | QT -= gui 3 | 4 | TARGET = mobileNet_ssd_demo 5 | CONFIG += console 6 | CONFIG -= app_bundle 7 | 8 | TEMPLATE = app 9 | 10 | SOURCES += main.cpp \ 11 | mobilenetssd.cpp 12 | 13 | HEADERS += \ 14 | mobilenetssd.h 15 | 16 | 17 | #opencv 18 | INCLUDEPATH += /home/fuxueping/sdb/Installation/conda/include/ 19 | LIBS += -L/home/fuxueping/sdb/Installation/conda/lib/ \ 20 | -lopencv_dnn -lopencv_ml -lopencv_objdetect \ 21 | -lopencv_shape -lopencv_stitching \ 22 | -lopencv_superres -lopencv_videostab \ 23 | -lopencv_calib3d -lopencv_features2d \ 24 | -lopencv_highgui -lopencv_videoio \ 25 | -lopencv_imgcodecs -lopencv_video \ 26 | -lopencv_photo -lopencv_imgproc -lopencv_flann -lopencv_core 27 | 28 | 29 | #cuda 30 | INCLUDEPATH += /usr/local/cuda-8.0/include/ 31 | LIBS += -L/usr/local/cuda-8.0/lib64 \ 32 | -lcublas -lcufft -lcurand -lnppc -lnvToolsExt \ 33 | -lcudart -lcufftw \ 34 | -lcusparse -lnpps 35 | 36 | #caffe 37 | #caffe 38 | INCLUDEPATH += /home/fuxueping/sdb/Caffe_Project_Train/caffe-ssd2/include 39 | INCLUDEPATH += /home/fuxueping/sdb/Caffe_Project_Train/caffe-ssd2/build/include 40 | INCLUDEPATH +=/home/fuxueping/sdb/Caffe_Project_Train/caffe-ssd2/build/src 41 | LIBS +=-L/home/fuxueping/sdb/Caffe_Project_Train/caffe-ssd2/build/lib \ 42 | -lcaffe 43 | 44 | 45 | CUDA_SDK = "/usr/local/cuda" # Path to cuda SDK install 46 | CUDA_DIR = "/usr/local/cuda" # Path to cuda toolkit install 47 | 48 | 49 | LIBS += -L/usr/lib/x86_64-linux-gnu \ 50 | -lglog -lboost_system -lboost_thread -lgflags -lprotobuf 51 | -------------------------------------------------------------------------------- /mobilenetssd.cpp: -------------------------------------------------------------------------------- 1 | #include "mobilenetssd.h" 2 | #include 3 | 4 | MobileNetSSD::MobileNetSSD() 5 | { 6 | 7 | } 8 | 9 | 10 | void MobileNetSSD::Initnet(string modelfile, string weights) 11 | { 12 | this->model_file = modelfile; 13 | this->weights_file = weights; 14 | } 15 | 16 | 17 | 18 | vector MobileNetSSD::detect(cv::Mat &image) 19 | { 20 | // vector boxes; 21 | vector boxes; 22 | 23 | Mat img = image.clone(); 24 | img.convertTo(img, CV_32FC3); //转为浮点图 25 | Caffe::set_mode(Caffe::GPU); 26 | net_.reset(new Net(model_file, TEST)); 27 | net_->CopyTrainedLayersFrom(weights_file); 28 | 29 | Blob* inputBlob = net_->input_blobs()[0]; 30 | int width = inputBlob->width(); //网络规定的输入图片的宽度和高度 31 | int height = inputBlob->height(); 32 | cv::resize(img, img, cv::Size(width, height)); //将测试图片进行调整大小 48 * 48 33 | inputBlob->Reshape(1, 3, height, width); 34 | net_->Reshape(); 35 | 36 | img = (img - 127.5)*0.007843; 37 | float* data = inputBlob->mutable_cpu_data(); //将图片的像素值，复制进网络的输入Blob 38 | for (int k = 0; k<3; ++k){ 39 | for (int i = 0; i(i, j)[k]; 43 | } 44 | } 45 | } 46 | FILE *fp = fopen("/home/fuxueping/sdb/svn2018/model_acceleration/tensorRT/result/output/data_demo.txt","w"); 47 | int data_count = 3*300*300; 48 | for(int i = 0; i < data_count;i++) 49 | { 50 | // fprintf(fp,"%f\n",malloc_image_ptr[i]); 51 | fprintf(fp,"%f\n",data[i] ); 52 | } 53 | fclose(fp); 54 | 55 | net_->ForwardFrom(0); 56 | Blob* result_blob = net_->output_blobs()[0]; 57 | const float* result = result_blob->cpu_data(); 58 | const int num_det = result_blob->height(); 59 | 60 | for (int k = 0; k < num_det; ++k) 61 | { 62 | Obj_Info obj; 63 | if (result[0] == -1) { 64 | // Skip invalid detection. 65 | result += 7; 66 | continue; 67 | } 68 | if( (result[1] == 15) && (result[2] > 0.5) ) 69 | { 70 | obj.obj_class = result[1]; 71 | obj.obj_prob = result[2]; 72 | int minx = int(result[3] * image.cols); 73 | int miny = int(result[4] * image.rows); 74 | int maxx = int(result[5] * image.cols); 75 | int maxy = int(result[6] * image.rows); 76 | 77 | minx = max(0, minx); 78 | miny = max(0, miny); 79 | maxx = min(image.cols - 1, maxx); 80 | maxy = min(image.rows - 1, maxy); 81 | 82 | Rect box; 83 | box.x = minx; 84 | box.y = miny; 85 | box.width = maxx - minx + 1; 86 | box.height = maxy - miny + 1; 87 | obj.obj_box = box; 88 | boxes.push_back(obj); 89 | } 90 | result += 7; 91 | } 92 | return boxes; 93 | } 94 | 95 | 96 | 97 | -------------------------------------------------------------------------------- /mobilenetssd.h: -------------------------------------------------------------------------------- 1 | #ifndef MOBILENETSSD_H 2 | #define MOBILENETSSD_H 3 | 4 | #include 5 | #include 6 | #include 7 | #include 8 | #include 9 | #include 10 | using namespace caffe; 11 | using namespace std; 12 | using namespace cv; 13 | 14 | typedef struct Obj_Info_ 15 | { 16 | Rect obj_box; 17 | float obj_prob; 18 | int obj_class; 19 | }Obj_Info; 20 | 21 | class MobileNetSSD 22 | { 23 | public: 24 | MobileNetSSD(); 25 | //初始化模型 26 | void Initnet(string modelfile, string weights); 27 | //行人检测 28 | vector detect(cv::Mat &image); 29 | std::vector result; 30 | void destroydetect(); 31 | 32 | boost::shared_ptr > net_; 33 | std::string model_file; 34 | std::string weights_file; 35 | }; 36 | 37 | 38 | 39 | #endif // MOBILENETSSD_H 40 | --------------------------------------------------------------------------------