├── .gitignore
├── CustomNetwork.txt
├── LogoDetection.py
├── README.md
├── convert-to-kitti.py
├── deploy.prototxt
├── process-video-rt.py
├── resize-img.py
└── video-to-frames.py


/.gitignore:
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1 | env/
2 | models/adidas.caffemodel
3 | models/enterprise.caffemodel
4 | models/fedex.caffemodel
5 | models/hankook.caffemodel
6 | models/unicredit.caffemodel
7 | models/amstel.caffemodel
8 | 
9 | 


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/CustomNetwork.txt:
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   1 | # DetectNet network
   2 | 
   3 | # Data/Input layers
   4 | name: "DetectNet"
   5 | layer {
   6 |   name: "train_data"
   7 |   type: "Data"
   8 |   top: "data"
   9 |   include: { phase: TRAIN }
  10 | }
  11 | layer {
  12 |   name: "train_label"
  13 |   type: "Data"
  14 |   top: "label"
  15 |   include: { phase: TRAIN }
  16 | }
  17 | layer {
  18 |   name: "val_data"
  19 |   type: "Data"
  20 |   top: "data"
  21 |   include: { phase: TEST stage: "val" }
  22 | }
  23 | layer {
  24 |   name: "val_label"
  25 |   type: "Data"
  26 |   top: "label"
  27 |   include: { phase: TEST stage: "val" }
  28 | }
  29 | layer {
  30 |   name: "deploy_data"
  31 |   type: "Input"
  32 |   top: "data"
  33 |   include: { phase: TEST not_stage: "val" }
  34 | }
  35 | 
  36 | # Data transformation layers
  37 | layer {
  38 |   name: "train_transform"
  39 |   type: "DetectNetTransformation"
  40 |   bottom: "data"
  41 |   bottom: "label"
  42 |   top: "transformed_data"
  43 |   top: "transformed_label"
  44 |   detectnet_groundtruth_param: {
  45 |     stride: 16
  46 |     scale_cvg: 0.4
  47 |     gridbox_type: GRIDBOX_MIN
  48 |     coverage_type: RECTANGULAR
  49 |     min_cvg_len: 20
  50 |     obj_norm: true
  51 |     image_size_x: 512
  52 |     image_size_y: 512
  53 |     crop_bboxes: false
  54 |   }
  55 |    detectnet_augmentation_param: {
  56 |     crop_prob: 1
  57 |     shift_x: 32
  58 |     shift_y: 32
  59 |     flip_prob: 0.5
  60 |     rotation_prob: 0
  61 |     max_rotate_degree: 5
  62 |     scale_prob: 0.4
  63 |     scale_min: 0.8
  64 |     scale_max: 1.2
  65 |     hue_rotation_prob: 0.8
  66 |     hue_rotation: 30
  67 |     desaturation_prob: 0.8
  68 |     desaturation_max: 0.8
  69 |   }
  70 |   transform_param: {
  71 |     mean_value: 127
  72 |   }
  73 |   include: { phase: TRAIN }
  74 | }
  75 | layer {
  76 |   name: "val_transform"
  77 |   type: "DetectNetTransformation"
  78 |   bottom: "data"
  79 |   bottom: "label"
  80 |   top: "transformed_data"
  81 |   top: "transformed_label"
  82 |   detectnet_groundtruth_param: {
  83 |     stride: 16
  84 |     scale_cvg: 0.4
  85 |     gridbox_type: GRIDBOX_MIN
  86 |     coverage_type: RECTANGULAR
  87 |     min_cvg_len: 20
  88 |     obj_norm: true
  89 |     image_size_x: 1280
  90 |     image_size_y: 720
  91 |     crop_bboxes: false
  92 |   }
  93 |   transform_param: {
  94 |     mean_value: 127
  95 |   }
  96 |   include: { phase: TEST stage: "val" }
  97 | }
  98 | layer {
  99 |   name: "deploy_transform"
 100 |   type: "Power"
 101 |   bottom: "data"
 102 |   top: "transformed_data"
 103 |   power_param {
 104 |     shift: -127
 105 |   }
 106 |   include: { phase: TEST not_stage: "val" }
 107 | }
 108 | 
 109 | # Label conversion layers
 110 | layer {
 111 |   name: "slice-label"
 112 |   type: "Slice"
 113 |   bottom: "transformed_label"
 114 |   top: "foreground-label"
 115 |   top: "bbox-label"
 116 |   top: "size-label"
 117 |   top: "obj-label"
 118 |   top: "coverage-label"
 119 |   slice_param {
 120 |     slice_dim: 1
 121 |     slice_point: 1
 122 |     slice_point: 5
 123 |     slice_point: 7
 124 |     slice_point: 8
 125 |   }
 126 |   include { phase: TRAIN }
 127 |   include { phase: TEST stage: "val" }
 128 | }
 129 | layer {
 130 |   name: "coverage-block"
 131 |   type: "Concat"
 132 |   bottom: "foreground-label"
 133 |   bottom: "foreground-label"
 134 |   bottom: "foreground-label"
 135 |   bottom: "foreground-label"
 136 |   top: "coverage-block"
 137 |   concat_param {
 138 |     concat_dim: 1
 139 |   }
 140 |   include { phase: TRAIN }
 141 |   include { phase: TEST stage: "val" }
 142 | }
 143 | layer {
 144 |   name: "size-block"
 145 |   type: "Concat"
 146 |   bottom: "size-label"
 147 |   bottom: "size-label"
 148 |   top: "size-block"
 149 |   concat_param {
 150 |     concat_dim: 1
 151 |   }
 152 |   include { phase: TRAIN }
 153 |   include { phase: TEST stage: "val" }
 154 | }
 155 | layer {
 156 |   name: "obj-block"
 157 |   type: "Concat"
 158 |   bottom: "obj-label"
 159 |   bottom: "obj-label"
 160 |   bottom: "obj-label"
 161 |   bottom: "obj-label"
 162 |   top: "obj-block"
 163 |   concat_param {
 164 |     concat_dim: 1
 165 |   }
 166 |   include { phase: TRAIN }
 167 |   include { phase: TEST stage: "val" }
 168 | }
 169 | layer {
 170 |   name: "bb-label-norm"
 171 |   type: "Eltwise"
 172 |   bottom: "bbox-label"
 173 |   bottom: "size-block"
 174 |   top: "bbox-label-norm"
 175 |   eltwise_param {
 176 |     operation: PROD
 177 |   }
 178 |   include { phase: TRAIN }
 179 |   include { phase: TEST stage: "val" }
 180 | }
 181 | layer {
 182 |   name: "bb-obj-norm"
 183 |   type: "Eltwise"
 184 |   bottom: "bbox-label-norm"
 185 |   bottom: "obj-block"
 186 |   top: "bbox-obj-label-norm"
 187 |   eltwise_param {
 188 |     operation: PROD
 189 |   }
 190 |   include { phase: TRAIN }
 191 |   include { phase: TEST stage: "val" }
 192 | }
 193 | 
 194 | ######################################################################
 195 | # Start of convolutional network
 196 | ######################################################################
 197 | 
 198 | layer {
 199 |   name: "conv1/7x7_s2"
 200 |   type: "Convolution"
 201 |   bottom: "transformed_data"
 202 |   top: "conv1/7x7_s2"
 203 |   param {
 204 |     lr_mult: 1
 205 |     decay_mult: 1
 206 |   }
 207 |   param {
 208 |     lr_mult: 2
 209 |     decay_mult: 0
 210 |   }
 211 |   convolution_param {
 212 |     num_output: 64
 213 |     pad: 3
 214 |     kernel_size: 7
 215 |     stride: 2
 216 |     weight_filler {
 217 |       type: "xavier"
 218 |       std: 0.1
 219 |     }
 220 |     bias_filler {
 221 |       type: "constant"
 222 |       value: 0.2
 223 |     }
 224 |   }
 225 | }
 226 | 
 227 | layer {
 228 |   name: "conv1/relu_7x7"
 229 |   type: "ReLU"
 230 |   bottom: "conv1/7x7_s2"
 231 |   top: "conv1/7x7_s2"
 232 | }
 233 | 
 234 | layer {
 235 |   name: "pool1/3x3_s2"
 236 |   type: "Pooling"
 237 |   bottom: "conv1/7x7_s2"
 238 |   top: "pool1/3x3_s2"
 239 |   pooling_param {
 240 |     pool: MAX
 241 |     kernel_size: 3
 242 |     stride: 2
 243 |   }
 244 | }
 245 | 
 246 | layer {
 247 |   name: "pool1/norm1"
 248 |   type: "LRN"
 249 |   bottom: "pool1/3x3_s2"
 250 |   top: "pool1/norm1"
 251 |   lrn_param {
 252 |     local_size: 5
 253 |     alpha: 0.0001
 254 |     beta: 0.75
 255 |   }
 256 | }
 257 | 
 258 | layer {
 259 |   name: "conv2/3x3_reduce"
 260 |   type: "Convolution"
 261 |   bottom: "pool1/norm1"
 262 |   top: "conv2/3x3_reduce"
 263 |   param {
 264 |     lr_mult: 1
 265 |     decay_mult: 1
 266 |   }
 267 |   param {
 268 |     lr_mult: 2
 269 |     decay_mult: 0
 270 |   }
 271 |   convolution_param {
 272 |     num_output: 64
 273 |     kernel_size: 1
 274 |     weight_filler {
 275 |       type: "xavier"
 276 |       std: 0.1
 277 |     }
 278 |     bias_filler {
 279 |       type: "constant"
 280 |       value: 0.2
 281 |     }
 282 |   }
 283 | }
 284 | 
 285 | layer {
 286 |   name: "conv2/relu_3x3_reduce"
 287 |   type: "ReLU"
 288 |   bottom: "conv2/3x3_reduce"
 289 |   top: "conv2/3x3_reduce"
 290 | }
 291 | 
 292 | layer {
 293 |   name: "conv2/3x3"
 294 |   type: "Convolution"
 295 |   bottom: "conv2/3x3_reduce"
 296 |   top: "conv2/3x3"
 297 |   param {
 298 |     lr_mult: 1
 299 |     decay_mult: 1
 300 |   }
 301 |   param {
 302 |     lr_mult: 2
 303 |     decay_mult: 0
 304 |   }
 305 |   convolution_param {
 306 |     num_output: 192
 307 |     pad: 1
 308 |     kernel_size: 3
 309 |     weight_filler {
 310 |       type: "xavier"
 311 |       std: 0.03
 312 |     }
 313 |     bias_filler {
 314 |       type: "constant"
 315 |       value: 0.2
 316 |     }
 317 |   }
 318 | }
 319 | 
 320 | layer {
 321 |   name: "conv2/relu_3x3"
 322 |   type: "ReLU"
 323 |   bottom: "conv2/3x3"
 324 |   top: "conv2/3x3"
 325 | }
 326 | 
 327 | layer {
 328 |   name: "conv2/norm2"
 329 |   type: "LRN"
 330 |   bottom: "conv2/3x3"
 331 |   top: "conv2/norm2"
 332 |   lrn_param {
 333 |     local_size: 5
 334 |     alpha: 0.0001
 335 |     beta: 0.75
 336 |   }
 337 | }
 338 | 
 339 | layer {
 340 |   name: "pool2/3x3_s2"
 341 |   type: "Pooling"
 342 |   bottom: "conv2/norm2"
 343 |   top: "pool2/3x3_s2"
 344 |   pooling_param {
 345 |     pool: MAX
 346 |     kernel_size: 3
 347 |     stride: 2
 348 |   }
 349 | }
 350 | 
 351 | layer {
 352 |   name: "inception_3a/1x1"
 353 |   type: "Convolution"
 354 |   bottom: "pool2/3x3_s2"
 355 |   top: "inception_3a/1x1"
 356 |   param {
 357 |     lr_mult: 1
 358 |     decay_mult: 1
 359 |   }
 360 |   param {
 361 |     lr_mult: 2
 362 |     decay_mult: 0
 363 |   }
 364 |   convolution_param {
 365 |     num_output: 64
 366 |     kernel_size: 1
 367 |     weight_filler {
 368 |       type: "xavier"
 369 |       std: 0.03
 370 |     }
 371 |     bias_filler {
 372 |       type: "constant"
 373 |       value: 0.2
 374 |     }
 375 |   }
 376 | }
 377 | 
 378 | layer {
 379 |   name: "inception_3a/relu_1x1"
 380 |   type: "ReLU"
 381 |   bottom: "inception_3a/1x1"
 382 |   top: "inception_3a/1x1"
 383 | }
 384 | 
 385 | layer {
 386 |   name: "inception_3a/3x3_reduce"
 387 |   type: "Convolution"
 388 |   bottom: "pool2/3x3_s2"
 389 |   top: "inception_3a/3x3_reduce"
 390 |   param {
 391 |     lr_mult: 1
 392 |     decay_mult: 1
 393 |   }
 394 |   param {
 395 |     lr_mult: 2
 396 |     decay_mult: 0
 397 |   }
 398 |   convolution_param {
 399 |     num_output: 96
 400 |     kernel_size: 1
 401 |     weight_filler {
 402 |       type: "xavier"
 403 |       std: 0.09
 404 |     }
 405 |     bias_filler {
 406 |       type: "constant"
 407 |       value: 0.2
 408 |     }
 409 |   }
 410 | }
 411 | 
 412 | layer {
 413 |   name: "inception_3a/relu_3x3_reduce"
 414 |   type: "ReLU"
 415 |   bottom: "inception_3a/3x3_reduce"
 416 |   top: "inception_3a/3x3_reduce"
 417 | }
 418 | 
 419 | layer {
 420 |   name: "inception_3a/3x3"
 421 |   type: "Convolution"
 422 |   bottom: "inception_3a/3x3_reduce"
 423 |   top: "inception_3a/3x3"
 424 |   param {
 425 |     lr_mult: 1
 426 |     decay_mult: 1
 427 |   }
 428 |   param {
 429 |     lr_mult: 2
 430 |     decay_mult: 0
 431 |   }
 432 |   convolution_param {
 433 |     num_output: 128
 434 |     pad: 1
 435 |     kernel_size: 3
 436 |     weight_filler {
 437 |       type: "xavier"
 438 |       std: 0.03
 439 |     }
 440 |     bias_filler {
 441 |       type: "constant"
 442 |       value: 0.2
 443 |     }
 444 |   }
 445 | }
 446 | 
 447 | layer {
 448 |   name: "inception_3a/relu_3x3"
 449 |   type: "ReLU"
 450 |   bottom: "inception_3a/3x3"
 451 |   top: "inception_3a/3x3"
 452 | }
 453 | 
 454 | layer {
 455 |   name: "inception_3a/5x5_reduce"
 456 |   type: "Convolution"
 457 |   bottom: "pool2/3x3_s2"
 458 |   top: "inception_3a/5x5_reduce"
 459 |   param {
 460 |     lr_mult: 1
 461 |     decay_mult: 1
 462 |   }
 463 |   param {
 464 |     lr_mult: 2
 465 |     decay_mult: 0
 466 |   }
 467 |   convolution_param {
 468 |     num_output: 16
 469 |     kernel_size: 1
 470 |     weight_filler {
 471 |       type: "xavier"
 472 |       std: 0.2
 473 |     }
 474 |     bias_filler {
 475 |       type: "constant"
 476 |       value: 0.2
 477 |     }
 478 |   }
 479 | }
 480 | layer {
 481 |   name: "inception_3a/relu_5x5_reduce"
 482 |   type: "ReLU"
 483 |   bottom: "inception_3a/5x5_reduce"
 484 |   top: "inception_3a/5x5_reduce"
 485 | }
 486 | layer {
 487 |   name: "inception_3a/5x5"
 488 |   type: "Convolution"
 489 |   bottom: "inception_3a/5x5_reduce"
 490 |   top: "inception_3a/5x5"
 491 |   param {
 492 |     lr_mult: 1
 493 |     decay_mult: 1
 494 |   }
 495 |   param {
 496 |     lr_mult: 2
 497 |     decay_mult: 0
 498 |   }
 499 |   convolution_param {
 500 |     num_output: 32
 501 |     pad: 2
 502 |     kernel_size: 5
 503 |     weight_filler {
 504 |       type: "xavier"
 505 |       std: 0.03
 506 |     }
 507 |     bias_filler {
 508 |       type: "constant"
 509 |       value: 0.2
 510 |     }
 511 |   }
 512 | }
 513 | layer {
 514 |   name: "inception_3a/relu_5x5"
 515 |   type: "ReLU"
 516 |   bottom: "inception_3a/5x5"
 517 |   top: "inception_3a/5x5"
 518 | }
 519 | 
 520 | layer {
 521 |   name: "inception_3a/pool"
 522 |   type: "Pooling"
 523 |   bottom: "pool2/3x3_s2"
 524 |   top: "inception_3a/pool"
 525 |   pooling_param {
 526 |     pool: MAX
 527 |     kernel_size: 3
 528 |     stride: 1
 529 |     pad: 1
 530 |   }
 531 | }
 532 | 
 533 | layer {
 534 |   name: "inception_3a/pool_proj"
 535 |   type: "Convolution"
 536 |   bottom: "inception_3a/pool"
 537 |   top: "inception_3a/pool_proj"
 538 |   param {
 539 |     lr_mult: 1
 540 |     decay_mult: 1
 541 |   }
 542 |   param {
 543 |     lr_mult: 2
 544 |     decay_mult: 0
 545 |   }
 546 |   convolution_param {
 547 |     num_output: 32
 548 |     kernel_size: 1
 549 |     weight_filler {
 550 |       type: "xavier"
 551 |       std: 0.1
 552 |     }
 553 |     bias_filler {
 554 |       type: "constant"
 555 |       value: 0.2
 556 |     }
 557 |   }
 558 | }
 559 | layer {
 560 |   name: "inception_3a/relu_pool_proj"
 561 |   type: "ReLU"
 562 |   bottom: "inception_3a/pool_proj"
 563 |   top: "inception_3a/pool_proj"
 564 | }
 565 | 
 566 | layer {
 567 |   name: "inception_3a/output"
 568 |   type: "Concat"
 569 |   bottom: "inception_3a/1x1"
 570 |   bottom: "inception_3a/3x3"
 571 |   bottom: "inception_3a/5x5"
 572 |   bottom: "inception_3a/pool_proj"
 573 |   top: "inception_3a/output"
 574 | }
 575 | 
 576 | layer {
 577 |   name: "inception_3b/1x1"
 578 |   type: "Convolution"
 579 |   bottom: "inception_3a/output"
 580 |   top: "inception_3b/1x1"
 581 |   param {
 582 |     lr_mult: 1
 583 |     decay_mult: 1
 584 |   }
 585 |   param {
 586 |     lr_mult: 2
 587 |     decay_mult: 0
 588 |   }
 589 |   convolution_param {
 590 |     num_output: 128
 591 |     kernel_size: 1
 592 |     weight_filler {
 593 |       type: "xavier"
 594 |       std: 0.03
 595 |     }
 596 |     bias_filler {
 597 |       type: "constant"
 598 |       value: 0.2
 599 |     }
 600 |   }
 601 | }
 602 | 
 603 | layer {
 604 |   name: "inception_3b/relu_1x1"
 605 |   type: "ReLU"
 606 |   bottom: "inception_3b/1x1"
 607 |   top: "inception_3b/1x1"
 608 | }
 609 | 
 610 | layer {
 611 |   name: "inception_3b/3x3_reduce"
 612 |   type: "Convolution"
 613 |   bottom: "inception_3a/output"
 614 |   top: "inception_3b/3x3_reduce"
 615 |   param {
 616 |     lr_mult: 1
 617 |     decay_mult: 1
 618 |   }
 619 |   param {
 620 |     lr_mult: 2
 621 |     decay_mult: 0
 622 |   }
 623 |   convolution_param {
 624 |     num_output: 128
 625 |     kernel_size: 1
 626 |     weight_filler {
 627 |       type: "xavier"
 628 |       std: 0.09
 629 |     }
 630 |     bias_filler {
 631 |       type: "constant"
 632 |       value: 0.2
 633 |     }
 634 |   }
 635 | }
 636 | layer {
 637 |   name: "inception_3b/relu_3x3_reduce"
 638 |   type: "ReLU"
 639 |   bottom: "inception_3b/3x3_reduce"
 640 |   top: "inception_3b/3x3_reduce"
 641 | }
 642 | layer {
 643 |   name: "inception_3b/3x3"
 644 |   type: "Convolution"
 645 |   bottom: "inception_3b/3x3_reduce"
 646 |   top: "inception_3b/3x3"
 647 |   param {
 648 |     lr_mult: 1
 649 |     decay_mult: 1
 650 |   }
 651 |   param {
 652 |     lr_mult: 2
 653 |     decay_mult: 0
 654 |   }
 655 |   convolution_param {
 656 |     num_output: 192
 657 |     pad: 1
 658 |     kernel_size: 3
 659 |     weight_filler {
 660 |       type: "xavier"
 661 |       std: 0.03
 662 |     }
 663 |     bias_filler {
 664 |       type: "constant"
 665 |       value: 0.2
 666 |     }
 667 |   }
 668 | }
 669 | layer {
 670 |   name: "inception_3b/relu_3x3"
 671 |   type: "ReLU"
 672 |   bottom: "inception_3b/3x3"
 673 |   top: "inception_3b/3x3"
 674 | }
 675 | 
 676 | layer {
 677 |   name: "inception_3b/5x5_reduce"
 678 |   type: "Convolution"
 679 |   bottom: "inception_3a/output"
 680 |   top: "inception_3b/5x5_reduce"
 681 |   param {
 682 |     lr_mult: 1
 683 |     decay_mult: 1
 684 |   }
 685 |   param {
 686 |     lr_mult: 2
 687 |     decay_mult: 0
 688 |   }
 689 |   convolution_param {
 690 |     num_output: 32
 691 |     kernel_size: 1
 692 |     weight_filler {
 693 |       type: "xavier"
 694 |       std: 0.2
 695 |     }
 696 |     bias_filler {
 697 |       type: "constant"
 698 |       value: 0.2
 699 |     }
 700 |   }
 701 | }
 702 | layer {
 703 |   name: "inception_3b/relu_5x5_reduce"
 704 |   type: "ReLU"
 705 |   bottom: "inception_3b/5x5_reduce"
 706 |   top: "inception_3b/5x5_reduce"
 707 | }
 708 | layer {
 709 |   name: "inception_3b/5x5"
 710 |   type: "Convolution"
 711 |   bottom: "inception_3b/5x5_reduce"
 712 |   top: "inception_3b/5x5"
 713 |   param {
 714 |     lr_mult: 1
 715 |     decay_mult: 1
 716 |   }
 717 |   param {
 718 |     lr_mult: 2
 719 |     decay_mult: 0
 720 |   }
 721 |   convolution_param {
 722 |     num_output: 96
 723 |     pad: 2
 724 |     kernel_size: 5
 725 |     weight_filler {
 726 |       type: "xavier"
 727 |       std: 0.03
 728 |     }
 729 |     bias_filler {
 730 |       type: "constant"
 731 |       value: 0.2
 732 |     }
 733 |   }
 734 | }
 735 | layer {
 736 |   name: "inception_3b/relu_5x5"
 737 |   type: "ReLU"
 738 |   bottom: "inception_3b/5x5"
 739 |   top: "inception_3b/5x5"
 740 | }
 741 | 
 742 | layer {
 743 |   name: "inception_3b/pool"
 744 |   type: "Pooling"
 745 |   bottom: "inception_3a/output"
 746 |   top: "inception_3b/pool"
 747 |   pooling_param {
 748 |     pool: MAX
 749 |     kernel_size: 3
 750 |     stride: 1
 751 |     pad: 1
 752 |   }
 753 | }
 754 | layer {
 755 |   name: "inception_3b/pool_proj"
 756 |   type: "Convolution"
 757 |   bottom: "inception_3b/pool"
 758 |   top: "inception_3b/pool_proj"
 759 |   param {
 760 |     lr_mult: 1
 761 |     decay_mult: 1
 762 |   }
 763 |   param {
 764 |     lr_mult: 2
 765 |     decay_mult: 0
 766 |   }
 767 |   convolution_param {
 768 |     num_output: 64
 769 |     kernel_size: 1
 770 |     weight_filler {
 771 |       type: "xavier"
 772 |       std: 0.1
 773 |     }
 774 |     bias_filler {
 775 |       type: "constant"
 776 |       value: 0.2
 777 |     }
 778 |   }
 779 | }
 780 | layer {
 781 |   name: "inception_3b/relu_pool_proj"
 782 |   type: "ReLU"
 783 |   bottom: "inception_3b/pool_proj"
 784 |   top: "inception_3b/pool_proj"
 785 | }
 786 | layer {
 787 |   name: "inception_3b/output"
 788 |   type: "Concat"
 789 |   bottom: "inception_3b/1x1"
 790 |   bottom: "inception_3b/3x3"
 791 |   bottom: "inception_3b/5x5"
 792 |   bottom: "inception_3b/pool_proj"
 793 |   top: "inception_3b/output"
 794 | }
 795 | 
 796 | layer {
 797 |   name: "pool3/3x3_s2"
 798 |   type: "Pooling"
 799 |   bottom: "inception_3b/output"
 800 |   top: "pool3/3x3_s2"
 801 |   pooling_param {
 802 |     pool: MAX
 803 |     kernel_size: 3
 804 |     stride: 2
 805 |   }
 806 | }
 807 | 
 808 | layer {
 809 |   name: "inception_4a/1x1"
 810 |   type: "Convolution"
 811 |   bottom: "pool3/3x3_s2"
 812 |   top: "inception_4a/1x1"
 813 |   param {
 814 |     lr_mult: 1
 815 |     decay_mult: 1
 816 |   }
 817 |   param {
 818 |     lr_mult: 2
 819 |     decay_mult: 0
 820 |   }
 821 |   convolution_param {
 822 |     num_output: 192
 823 |     kernel_size: 1
 824 |     weight_filler {
 825 |       type: "xavier"
 826 |       std: 0.03
 827 |     }
 828 |     bias_filler {
 829 |       type: "constant"
 830 |       value: 0.2
 831 |     }
 832 |   }
 833 | }
 834 | 
 835 | layer {
 836 |   name: "inception_4a/relu_1x1"
 837 |   type: "ReLU"
 838 |   bottom: "inception_4a/1x1"
 839 |   top: "inception_4a/1x1"
 840 | }
 841 | 
 842 | layer {
 843 |   name: "inception_4a/3x3_reduce"
 844 |   type: "Convolution"
 845 |   bottom: "pool3/3x3_s2"
 846 |   top: "inception_4a/3x3_reduce"
 847 |   param {
 848 |     lr_mult: 1
 849 |     decay_mult: 1
 850 |   }
 851 |   param {
 852 |     lr_mult: 2
 853 |     decay_mult: 0
 854 |   }
 855 |   convolution_param {
 856 |     num_output: 96
 857 |     kernel_size: 1
 858 |     weight_filler {
 859 |       type: "xavier"
 860 |       std: 0.09
 861 |     }
 862 |     bias_filler {
 863 |       type: "constant"
 864 |       value: 0.2
 865 |     }
 866 |   }
 867 | }
 868 | 
 869 | layer {
 870 |   name: "inception_4a/relu_3x3_reduce"
 871 |   type: "ReLU"
 872 |   bottom: "inception_4a/3x3_reduce"
 873 |   top: "inception_4a/3x3_reduce"
 874 | }
 875 | 
 876 | layer {
 877 |   name: "inception_4a/3x3"
 878 |   type: "Convolution"
 879 |   bottom: "inception_4a/3x3_reduce"
 880 |   top: "inception_4a/3x3"
 881 |   param {
 882 |     lr_mult: 1
 883 |     decay_mult: 1
 884 |   }
 885 |   param {
 886 |     lr_mult: 2
 887 |     decay_mult: 0
 888 |   }
 889 |   convolution_param {
 890 |     num_output: 208
 891 |     pad: 1
 892 |     kernel_size: 3
 893 |     weight_filler {
 894 |       type: "xavier"
 895 |       std: 0.03
 896 |     }
 897 |     bias_filler {
 898 |       type: "constant"
 899 |       value: 0.2
 900 |     }
 901 |   }
 902 | }
 903 | 
 904 | layer {
 905 |   name: "inception_4a/relu_3x3"
 906 |   type: "ReLU"
 907 |   bottom: "inception_4a/3x3"
 908 |   top: "inception_4a/3x3"
 909 | }
 910 | 
 911 | layer {
 912 |   name: "inception_4a/5x5_reduce"
 913 |   type: "Convolution"
 914 |   bottom: "pool3/3x3_s2"
 915 |   top: "inception_4a/5x5_reduce"
 916 |   param {
 917 |     lr_mult: 1
 918 |     decay_mult: 1
 919 |   }
 920 |   param {
 921 |     lr_mult: 2
 922 |     decay_mult: 0
 923 |   }
 924 |   convolution_param {
 925 |     num_output: 16
 926 |     kernel_size: 1
 927 |     weight_filler {
 928 |       type: "xavier"
 929 |       std: 0.2
 930 |     }
 931 |     bias_filler {
 932 |       type: "constant"
 933 |       value: 0.2
 934 |     }
 935 |   }
 936 | }
 937 | layer {
 938 |   name: "inception_4a/relu_5x5_reduce"
 939 |   type: "ReLU"
 940 |   bottom: "inception_4a/5x5_reduce"
 941 |   top: "inception_4a/5x5_reduce"
 942 | }
 943 | layer {
 944 |   name: "inception_4a/5x5"
 945 |   type: "Convolution"
 946 |   bottom: "inception_4a/5x5_reduce"
 947 |   top: "inception_4a/5x5"
 948 |   param {
 949 |     lr_mult: 1
 950 |     decay_mult: 1
 951 |   }
 952 |   param {
 953 |     lr_mult: 2
 954 |     decay_mult: 0
 955 |   }
 956 |   convolution_param {
 957 |     num_output: 48
 958 |     pad: 2
 959 |     kernel_size: 5
 960 |     weight_filler {
 961 |       type: "xavier"
 962 |       std: 0.03
 963 |     }
 964 |     bias_filler {
 965 |       type: "constant"
 966 |       value: 0.2
 967 |     }
 968 |   }
 969 | }
 970 | layer {
 971 |   name: "inception_4a/relu_5x5"
 972 |   type: "ReLU"
 973 |   bottom: "inception_4a/5x5"
 974 |   top: "inception_4a/5x5"
 975 | }
 976 | layer {
 977 |   name: "inception_4a/pool"
 978 |   type: "Pooling"
 979 |   bottom: "pool3/3x3_s2"
 980 |   top: "inception_4a/pool"
 981 |   pooling_param {
 982 |     pool: MAX
 983 |     kernel_size: 3
 984 |     stride: 1
 985 |     pad: 1
 986 |   }
 987 | }
 988 | layer {
 989 |   name: "inception_4a/pool_proj"
 990 |   type: "Convolution"
 991 |   bottom: "inception_4a/pool"
 992 |   top: "inception_4a/pool_proj"
 993 |   param {
 994 |     lr_mult: 1
 995 |     decay_mult: 1
 996 |   }
 997 |   param {
 998 |     lr_mult: 2
 999 |     decay_mult: 0
1000 |   }
1001 |   convolution_param {
1002 |     num_output: 64
1003 |     kernel_size: 1
1004 |     weight_filler {
1005 |       type: "xavier"
1006 |       std: 0.1
1007 |     }
1008 |     bias_filler {
1009 |       type: "constant"
1010 |       value: 0.2
1011 |     }
1012 |   }
1013 | }
1014 | layer {
1015 |   name: "inception_4a/relu_pool_proj"
1016 |   type: "ReLU"
1017 |   bottom: "inception_4a/pool_proj"
1018 |   top: "inception_4a/pool_proj"
1019 | }
1020 | layer {
1021 |   name: "inception_4a/output"
1022 |   type: "Concat"
1023 |   bottom: "inception_4a/1x1"
1024 |   bottom: "inception_4a/3x3"
1025 |   bottom: "inception_4a/5x5"
1026 |   bottom: "inception_4a/pool_proj"
1027 |   top: "inception_4a/output"
1028 | }
1029 | 
1030 | layer {
1031 |   name: "inception_4b/1x1"
1032 |   type: "Convolution"
1033 |   bottom: "inception_4a/output"
1034 |   top: "inception_4b/1x1"
1035 |   param {
1036 |     lr_mult: 1
1037 |     decay_mult: 1
1038 |   }
1039 |   param {
1040 |     lr_mult: 2
1041 |     decay_mult: 0
1042 |   }
1043 |   convolution_param {
1044 |     num_output: 160
1045 |     kernel_size: 1
1046 |     weight_filler {
1047 |       type: "xavier"
1048 |       std: 0.03
1049 |     }
1050 |     bias_filler {
1051 |       type: "constant"
1052 |       value: 0.2
1053 |     }
1054 |   }
1055 | }
1056 | 
1057 | layer {
1058 |   name: "inception_4b/relu_1x1"
1059 |   type: "ReLU"
1060 |   bottom: "inception_4b/1x1"
1061 |   top: "inception_4b/1x1"
1062 | }
1063 | layer {
1064 |   name: "inception_4b/3x3_reduce"
1065 |   type: "Convolution"
1066 |   bottom: "inception_4a/output"
1067 |   top: "inception_4b/3x3_reduce"
1068 |   param {
1069 |     lr_mult: 1
1070 |     decay_mult: 1
1071 |   }
1072 |   param {
1073 |     lr_mult: 2
1074 |     decay_mult: 0
1075 |   }
1076 |   convolution_param {
1077 |     num_output: 112
1078 |     kernel_size: 1
1079 |     weight_filler {
1080 |       type: "xavier"
1081 |       std: 0.09
1082 |     }
1083 |     bias_filler {
1084 |       type: "constant"
1085 |       value: 0.2
1086 |     }
1087 |   }
1088 | }
1089 | layer {
1090 |   name: "inception_4b/relu_3x3_reduce"
1091 |   type: "ReLU"
1092 |   bottom: "inception_4b/3x3_reduce"
1093 |   top: "inception_4b/3x3_reduce"
1094 | }
1095 | layer {
1096 |   name: "inception_4b/3x3"
1097 |   type: "Convolution"
1098 |   bottom: "inception_4b/3x3_reduce"
1099 |   top: "inception_4b/3x3"
1100 |   param {
1101 |     lr_mult: 1
1102 |     decay_mult: 1
1103 |   }
1104 |   param {
1105 |     lr_mult: 2
1106 |     decay_mult: 0
1107 |   }
1108 |   convolution_param {
1109 |     num_output: 224
1110 |     pad: 1
1111 |     kernel_size: 3
1112 |     weight_filler {
1113 |       type: "xavier"
1114 |       std: 0.03
1115 |     }
1116 |     bias_filler {
1117 |       type: "constant"
1118 |       value: 0.2
1119 |     }
1120 |   }
1121 | }
1122 | layer {
1123 |   name: "inception_4b/relu_3x3"
1124 |   type: "ReLU"
1125 |   bottom: "inception_4b/3x3"
1126 |   top: "inception_4b/3x3"
1127 | }
1128 | layer {
1129 |   name: "inception_4b/5x5_reduce"
1130 |   type: "Convolution"
1131 |   bottom: "inception_4a/output"
1132 |   top: "inception_4b/5x5_reduce"
1133 |   param {
1134 |     lr_mult: 1
1135 |     decay_mult: 1
1136 |   }
1137 |   param {
1138 |     lr_mult: 2
1139 |     decay_mult: 0
1140 |   }
1141 |   convolution_param {
1142 |     num_output: 24
1143 |     kernel_size: 1
1144 |     weight_filler {
1145 |       type: "xavier"
1146 |       std: 0.2
1147 |     }
1148 |     bias_filler {
1149 |       type: "constant"
1150 |       value: 0.2
1151 |     }
1152 |   }
1153 | }
1154 | layer {
1155 |   name: "inception_4b/relu_5x5_reduce"
1156 |   type: "ReLU"
1157 |   bottom: "inception_4b/5x5_reduce"
1158 |   top: "inception_4b/5x5_reduce"
1159 | }
1160 | layer {
1161 |   name: "inception_4b/5x5"
1162 |   type: "Convolution"
1163 |   bottom: "inception_4b/5x5_reduce"
1164 |   top: "inception_4b/5x5"
1165 |   param {
1166 |     lr_mult: 1
1167 |     decay_mult: 1
1168 |   }
1169 |   param {
1170 |     lr_mult: 2
1171 |     decay_mult: 0
1172 |   }
1173 |   convolution_param {
1174 |     num_output: 64
1175 |     pad: 2
1176 |     kernel_size: 5
1177 |     weight_filler {
1178 |       type: "xavier"
1179 |       std: 0.03
1180 |     }
1181 |     bias_filler {
1182 |       type: "constant"
1183 |       value: 0.2
1184 |     }
1185 |   }
1186 | }
1187 | layer {
1188 |   name: "inception_4b/relu_5x5"
1189 |   type: "ReLU"
1190 |   bottom: "inception_4b/5x5"
1191 |   top: "inception_4b/5x5"
1192 | }
1193 | layer {
1194 |   name: "inception_4b/pool"
1195 |   type: "Pooling"
1196 |   bottom: "inception_4a/output"
1197 |   top: "inception_4b/pool"
1198 |   pooling_param {
1199 |     pool: MAX
1200 |     kernel_size: 3
1201 |     stride: 1
1202 |     pad: 1
1203 |   }
1204 | }
1205 | layer {
1206 |   name: "inception_4b/pool_proj"
1207 |   type: "Convolution"
1208 |   bottom: "inception_4b/pool"
1209 |   top: "inception_4b/pool_proj"
1210 |   param {
1211 |     lr_mult: 1
1212 |     decay_mult: 1
1213 |   }
1214 |   param {
1215 |     lr_mult: 2
1216 |     decay_mult: 0
1217 |   }
1218 |   convolution_param {
1219 |     num_output: 64
1220 |     kernel_size: 1
1221 |     weight_filler {
1222 |       type: "xavier"
1223 |       std: 0.1
1224 |     }
1225 |     bias_filler {
1226 |       type: "constant"
1227 |       value: 0.2
1228 |     }
1229 |   }
1230 | }
1231 | layer {
1232 |   name: "inception_4b/relu_pool_proj"
1233 |   type: "ReLU"
1234 |   bottom: "inception_4b/pool_proj"
1235 |   top: "inception_4b/pool_proj"
1236 | }
1237 | layer {
1238 |   name: "inception_4b/output"
1239 |   type: "Concat"
1240 |   bottom: "inception_4b/1x1"
1241 |   bottom: "inception_4b/3x3"
1242 |   bottom: "inception_4b/5x5"
1243 |   bottom: "inception_4b/pool_proj"
1244 |   top: "inception_4b/output"
1245 | }
1246 | 
1247 | layer {
1248 |   name: "inception_4c/1x1"
1249 |   type: "Convolution"
1250 |   bottom: "inception_4b/output"
1251 |   top: "inception_4c/1x1"
1252 |   param {
1253 |     lr_mult: 1
1254 |     decay_mult: 1
1255 |   }
1256 |   param {
1257 |     lr_mult: 2
1258 |     decay_mult: 0
1259 |   }
1260 |   convolution_param {
1261 |     num_output: 128
1262 |     kernel_size: 1
1263 |     weight_filler {
1264 |       type: "xavier"
1265 |       std: 0.03
1266 |     }
1267 |     bias_filler {
1268 |       type: "constant"
1269 |       value: 0.2
1270 |     }
1271 |   }
1272 | }
1273 | 
1274 | layer {
1275 |   name: "inception_4c/relu_1x1"
1276 |   type: "ReLU"
1277 |   bottom: "inception_4c/1x1"
1278 |   top: "inception_4c/1x1"
1279 | }
1280 | 
1281 | layer {
1282 |   name: "inception_4c/3x3_reduce"
1283 |   type: "Convolution"
1284 |   bottom: "inception_4b/output"
1285 |   top: "inception_4c/3x3_reduce"
1286 |   param {
1287 |     lr_mult: 1
1288 |     decay_mult: 1
1289 |   }
1290 |   param {
1291 |     lr_mult: 2
1292 |     decay_mult: 0
1293 |   }
1294 |   convolution_param {
1295 |     num_output: 128
1296 |     kernel_size: 1
1297 |     weight_filler {
1298 |       type: "xavier"
1299 |       std: 0.09
1300 |     }
1301 |     bias_filler {
1302 |       type: "constant"
1303 |       value: 0.2
1304 |     }
1305 |   }
1306 | }
1307 | 
1308 | layer {
1309 |   name: "inception_4c/relu_3x3_reduce"
1310 |   type: "ReLU"
1311 |   bottom: "inception_4c/3x3_reduce"
1312 |   top: "inception_4c/3x3_reduce"
1313 | }
1314 | layer {
1315 |   name: "inception_4c/3x3"
1316 |   type: "Convolution"
1317 |   bottom: "inception_4c/3x3_reduce"
1318 |   top: "inception_4c/3x3"
1319 |   param {
1320 |     lr_mult: 1
1321 |     decay_mult: 1
1322 |   }
1323 |   param {
1324 |     lr_mult: 2
1325 |     decay_mult: 0
1326 |   }
1327 |   convolution_param {
1328 |     num_output: 256
1329 |     pad: 1
1330 |     kernel_size: 3
1331 |     weight_filler {
1332 |       type: "xavier"
1333 |       std: 0.03
1334 |     }
1335 |     bias_filler {
1336 |       type: "constant"
1337 |       value: 0.2
1338 |     }
1339 |   }
1340 | }
1341 | layer {
1342 |   name: "inception_4c/relu_3x3"
1343 |   type: "ReLU"
1344 |   bottom: "inception_4c/3x3"
1345 |   top: "inception_4c/3x3"
1346 | }
1347 | layer {
1348 |   name: "inception_4c/5x5_reduce"
1349 |   type: "Convolution"
1350 |   bottom: "inception_4b/output"
1351 |   top: "inception_4c/5x5_reduce"
1352 |   param {
1353 |     lr_mult: 1
1354 |     decay_mult: 1
1355 |   }
1356 |   param {
1357 |     lr_mult: 2
1358 |     decay_mult: 0
1359 |   }
1360 |   convolution_param {
1361 |     num_output: 24
1362 |     kernel_size: 1
1363 |     weight_filler {
1364 |       type: "xavier"
1365 |       std: 0.2
1366 |     }
1367 |     bias_filler {
1368 |       type: "constant"
1369 |       value: 0.2
1370 |     }
1371 |   }
1372 | }
1373 | layer {
1374 |   name: "inception_4c/relu_5x5_reduce"
1375 |   type: "ReLU"
1376 |   bottom: "inception_4c/5x5_reduce"
1377 |   top: "inception_4c/5x5_reduce"
1378 | }
1379 | layer {
1380 |   name: "inception_4c/5x5"
1381 |   type: "Convolution"
1382 |   bottom: "inception_4c/5x5_reduce"
1383 |   top: "inception_4c/5x5"
1384 |   param {
1385 |     lr_mult: 1
1386 |     decay_mult: 1
1387 |   }
1388 |   param {
1389 |     lr_mult: 2
1390 |     decay_mult: 0
1391 |   }
1392 |   convolution_param {
1393 |     num_output: 64
1394 |     pad: 2
1395 |     kernel_size: 5
1396 |     weight_filler {
1397 |       type: "xavier"
1398 |       std: 0.03
1399 |     }
1400 |     bias_filler {
1401 |       type: "constant"
1402 |       value: 0.2
1403 |     }
1404 |   }
1405 | }
1406 | layer {
1407 |   name: "inception_4c/relu_5x5"
1408 |   type: "ReLU"
1409 |   bottom: "inception_4c/5x5"
1410 |   top: "inception_4c/5x5"
1411 | }
1412 | layer {
1413 |   name: "inception_4c/pool"
1414 |   type: "Pooling"
1415 |   bottom: "inception_4b/output"
1416 |   top: "inception_4c/pool"
1417 |   pooling_param {
1418 |     pool: MAX
1419 |     kernel_size: 3
1420 |     stride: 1
1421 |     pad: 1
1422 |   }
1423 | }
1424 | layer {
1425 |   name: "inception_4c/pool_proj"
1426 |   type: "Convolution"
1427 |   bottom: "inception_4c/pool"
1428 |   top: "inception_4c/pool_proj"
1429 |   param {
1430 |     lr_mult: 1
1431 |     decay_mult: 1
1432 |   }
1433 |   param {
1434 |     lr_mult: 2
1435 |     decay_mult: 0
1436 |   }
1437 |   convolution_param {
1438 |     num_output: 64
1439 |     kernel_size: 1
1440 |     weight_filler {
1441 |       type: "xavier"
1442 |       std: 0.1
1443 |     }
1444 |     bias_filler {
1445 |       type: "constant"
1446 |       value: 0.2
1447 |     }
1448 |   }
1449 | }
1450 | layer {
1451 |   name: "inception_4c/relu_pool_proj"
1452 |   type: "ReLU"
1453 |   bottom: "inception_4c/pool_proj"
1454 |   top: "inception_4c/pool_proj"
1455 | }
1456 | layer {
1457 |   name: "inception_4c/output"
1458 |   type: "Concat"
1459 |   bottom: "inception_4c/1x1"
1460 |   bottom: "inception_4c/3x3"
1461 |   bottom: "inception_4c/5x5"
1462 |   bottom: "inception_4c/pool_proj"
1463 |   top: "inception_4c/output"
1464 | }
1465 | 
1466 | layer {
1467 |   name: "inception_4d/1x1"
1468 |   type: "Convolution"
1469 |   bottom: "inception_4c/output"
1470 |   top: "inception_4d/1x1"
1471 |   param {
1472 |     lr_mult: 1
1473 |     decay_mult: 1
1474 |   }
1475 |   param {
1476 |     lr_mult: 2
1477 |     decay_mult: 0
1478 |   }
1479 |   convolution_param {
1480 |     num_output: 112
1481 |     kernel_size: 1
1482 |     weight_filler {
1483 |       type: "xavier"
1484 |       std: 0.1
1485 |     }
1486 |     bias_filler {
1487 |       type: "constant"
1488 |       value: 0.2
1489 |     }
1490 |   }
1491 | }
1492 | layer {
1493 |   name: "inception_4d/relu_1x1"
1494 |   type: "ReLU"
1495 |   bottom: "inception_4d/1x1"
1496 |   top: "inception_4d/1x1"
1497 | }
1498 | layer {
1499 |   name: "inception_4d/3x3_reduce"
1500 |   type: "Convolution"
1501 |   bottom: "inception_4c/output"
1502 |   top: "inception_4d/3x3_reduce"
1503 |   param {
1504 |     lr_mult: 1
1505 |     decay_mult: 1
1506 |   }
1507 |   param {
1508 |     lr_mult: 2
1509 |     decay_mult: 0
1510 |   }
1511 |   convolution_param {
1512 |     num_output: 144
1513 |     kernel_size: 1
1514 |     weight_filler {
1515 |       type: "xavier"
1516 |       std: 0.1
1517 |     }
1518 |     bias_filler {
1519 |       type: "constant"
1520 |       value: 0.2
1521 |     }
1522 |   }
1523 | }
1524 | layer {
1525 |   name: "inception_4d/relu_3x3_reduce"
1526 |   type: "ReLU"
1527 |   bottom: "inception_4d/3x3_reduce"
1528 |   top: "inception_4d/3x3_reduce"
1529 | }
1530 | layer {
1531 |   name: "inception_4d/3x3"
1532 |   type: "Convolution"
1533 |   bottom: "inception_4d/3x3_reduce"
1534 |   top: "inception_4d/3x3"
1535 |   param {
1536 |     lr_mult: 1
1537 |     decay_mult: 1
1538 |   }
1539 |   param {
1540 |     lr_mult: 2
1541 |     decay_mult: 0
1542 |   }
1543 |   convolution_param {
1544 |     num_output: 288
1545 |     pad: 1
1546 |     kernel_size: 3
1547 |     weight_filler {
1548 |       type: "xavier"
1549 |       std: 0.1
1550 |     }
1551 |     bias_filler {
1552 |       type: "constant"
1553 |       value: 0.2
1554 |     }
1555 |   }
1556 | }
1557 | layer {
1558 |   name: "inception_4d/relu_3x3"
1559 |   type: "ReLU"
1560 |   bottom: "inception_4d/3x3"
1561 |   top: "inception_4d/3x3"
1562 | }
1563 | layer {
1564 |   name: "inception_4d/5x5_reduce"
1565 |   type: "Convolution"
1566 |   bottom: "inception_4c/output"
1567 |   top: "inception_4d/5x5_reduce"
1568 |   param {
1569 |     lr_mult: 1
1570 |     decay_mult: 1
1571 |   }
1572 |   param {
1573 |     lr_mult: 2
1574 |     decay_mult: 0
1575 |   }
1576 |   convolution_param {
1577 |     num_output: 32
1578 |     kernel_size: 1
1579 |     weight_filler {
1580 |       type: "xavier"
1581 |       std: 0.1
1582 |     }
1583 |     bias_filler {
1584 |       type: "constant"
1585 |       value: 0.2
1586 |     }
1587 |   }
1588 | }
1589 | layer {
1590 |   name: "inception_4d/relu_5x5_reduce"
1591 |   type: "ReLU"
1592 |   bottom: "inception_4d/5x5_reduce"
1593 |   top: "inception_4d/5x5_reduce"
1594 | }
1595 | layer {
1596 |   name: "inception_4d/5x5"
1597 |   type: "Convolution"
1598 |   bottom: "inception_4d/5x5_reduce"
1599 |   top: "inception_4d/5x5"
1600 |   param {
1601 |     lr_mult: 1
1602 |     decay_mult: 1
1603 |   }
1604 |   param {
1605 |     lr_mult: 2
1606 |     decay_mult: 0
1607 |   }
1608 |   convolution_param {
1609 |     num_output: 64
1610 |     pad: 2
1611 |     kernel_size: 5
1612 |     weight_filler {
1613 |       type: "xavier"
1614 |       std: 0.1
1615 |     }
1616 |     bias_filler {
1617 |       type: "constant"
1618 |       value: 0.2
1619 |     }
1620 |   }
1621 | }
1622 | layer {
1623 |   name: "inception_4d/relu_5x5"
1624 |   type: "ReLU"
1625 |   bottom: "inception_4d/5x5"
1626 |   top: "inception_4d/5x5"
1627 | }
1628 | layer {
1629 |   name: "inception_4d/pool"
1630 |   type: "Pooling"
1631 |   bottom: "inception_4c/output"
1632 |   top: "inception_4d/pool"
1633 |   pooling_param {
1634 |     pool: MAX
1635 |     kernel_size: 3
1636 |     stride: 1
1637 |     pad: 1
1638 |   }
1639 | }
1640 | layer {
1641 |   name: "inception_4d/pool_proj"
1642 |   type: "Convolution"
1643 |   bottom: "inception_4d/pool"
1644 |   top: "inception_4d/pool_proj"
1645 |   param {
1646 |     lr_mult: 1
1647 |     decay_mult: 1
1648 |   }
1649 |   param {
1650 |     lr_mult: 2
1651 |     decay_mult: 0
1652 |   }
1653 |   convolution_param {
1654 |     num_output: 64
1655 |     kernel_size: 1
1656 |     weight_filler {
1657 |       type: "xavier"
1658 |       std: 0.1
1659 |     }
1660 |     bias_filler {
1661 |       type: "constant"
1662 |       value: 0.2
1663 |     }
1664 |   }
1665 | }
1666 | layer {
1667 |   name: "inception_4d/relu_pool_proj"
1668 |   type: "ReLU"
1669 |   bottom: "inception_4d/pool_proj"
1670 |   top: "inception_4d/pool_proj"
1671 | }
1672 | layer {
1673 |   name: "inception_4d/output"
1674 |   type: "Concat"
1675 |   bottom: "inception_4d/1x1"
1676 |   bottom: "inception_4d/3x3"
1677 |   bottom: "inception_4d/5x5"
1678 |   bottom: "inception_4d/pool_proj"
1679 |   top: "inception_4d/output"
1680 | }
1681 | 
1682 | layer {
1683 |   name: "inception_4e/1x1"
1684 |   type: "Convolution"
1685 |   bottom: "inception_4d/output"
1686 |   top: "inception_4e/1x1"
1687 |   param {
1688 |     lr_mult: 1
1689 |     decay_mult: 1
1690 |   }
1691 |   param {
1692 |     lr_mult: 2
1693 |     decay_mult: 0
1694 |   }
1695 |   convolution_param {
1696 |     num_output: 256
1697 |     kernel_size: 1
1698 |     weight_filler {
1699 |       type: "xavier"
1700 |       std: 0.03
1701 |     }
1702 |     bias_filler {
1703 |       type: "constant"
1704 |       value: 0.2
1705 |     }
1706 |   }
1707 | }
1708 | layer {
1709 |   name: "inception_4e/relu_1x1"
1710 |   type: "ReLU"
1711 |   bottom: "inception_4e/1x1"
1712 |   top: "inception_4e/1x1"
1713 | }
1714 | layer {
1715 |   name: "inception_4e/3x3_reduce"
1716 |   type: "Convolution"
1717 |   bottom: "inception_4d/output"
1718 |   top: "inception_4e/3x3_reduce"
1719 |   param {
1720 |     lr_mult: 1
1721 |     decay_mult: 1
1722 |   }
1723 |   param {
1724 |     lr_mult: 2
1725 |     decay_mult: 0
1726 |   }
1727 |   convolution_param {
1728 |     num_output: 160
1729 |     kernel_size: 1
1730 |     weight_filler {
1731 |       type: "xavier"
1732 |       std: 0.09
1733 |     }
1734 |     bias_filler {
1735 |       type: "constant"
1736 |       value: 0.2
1737 |     }
1738 |   }
1739 | }
1740 | layer {
1741 |   name: "inception_4e/relu_3x3_reduce"
1742 |   type: "ReLU"
1743 |   bottom: "inception_4e/3x3_reduce"
1744 |   top: "inception_4e/3x3_reduce"
1745 | }
1746 | layer {
1747 |   name: "inception_4e/3x3"
1748 |   type: "Convolution"
1749 |   bottom: "inception_4e/3x3_reduce"
1750 |   top: "inception_4e/3x3"
1751 |   param {
1752 |     lr_mult: 1
1753 |     decay_mult: 1
1754 |   }
1755 |   param {
1756 |     lr_mult: 2
1757 |     decay_mult: 0
1758 |   }
1759 |   convolution_param {
1760 |     num_output: 320
1761 |     pad: 1
1762 |     kernel_size: 3
1763 |     weight_filler {
1764 |       type: "xavier"
1765 |       std: 0.03
1766 |     }
1767 |     bias_filler {
1768 |       type: "constant"
1769 |       value: 0.2
1770 |     }
1771 |   }
1772 | }
1773 | layer {
1774 |   name: "inception_4e/relu_3x3"
1775 |   type: "ReLU"
1776 |   bottom: "inception_4e/3x3"
1777 |   top: "inception_4e/3x3"
1778 | }
1779 | layer {
1780 |   name: "inception_4e/5x5_reduce"
1781 |   type: "Convolution"
1782 |   bottom: "inception_4d/output"
1783 |   top: "inception_4e/5x5_reduce"
1784 |   param {
1785 |     lr_mult: 1
1786 |     decay_mult: 1
1787 |   }
1788 |   param {
1789 |     lr_mult: 2
1790 |     decay_mult: 0
1791 |   }
1792 |   convolution_param {
1793 |     num_output: 32
1794 |     kernel_size: 1
1795 |     weight_filler {
1796 |       type: "xavier"
1797 |       std: 0.2
1798 |     }
1799 |     bias_filler {
1800 |       type: "constant"
1801 |       value: 0.2
1802 |     }
1803 |   }
1804 | }
1805 | layer {
1806 |   name: "inception_4e/relu_5x5_reduce"
1807 |   type: "ReLU"
1808 |   bottom: "inception_4e/5x5_reduce"
1809 |   top: "inception_4e/5x5_reduce"
1810 | }
1811 | layer {
1812 |   name: "inception_4e/5x5"
1813 |   type: "Convolution"
1814 |   bottom: "inception_4e/5x5_reduce"
1815 |   top: "inception_4e/5x5"
1816 |   param {
1817 |     lr_mult: 1
1818 |     decay_mult: 1
1819 |   }
1820 |   param {
1821 |     lr_mult: 2
1822 |     decay_mult: 0
1823 |   }
1824 |   convolution_param {
1825 |     num_output: 128
1826 |     pad: 2
1827 |     kernel_size: 5
1828 |     weight_filler {
1829 |       type: "xavier"
1830 |       std: 0.03
1831 |     }
1832 |     bias_filler {
1833 |       type: "constant"
1834 |       value: 0.2
1835 |     }
1836 |   }
1837 | }
1838 | layer {
1839 |   name: "inception_4e/relu_5x5"
1840 |   type: "ReLU"
1841 |   bottom: "inception_4e/5x5"
1842 |   top: "inception_4e/5x5"
1843 | }
1844 | layer {
1845 |   name: "inception_4e/pool"
1846 |   type: "Pooling"
1847 |   bottom: "inception_4d/output"
1848 |   top: "inception_4e/pool"
1849 |   pooling_param {
1850 |     pool: MAX
1851 |     kernel_size: 3
1852 |     stride: 1
1853 |     pad: 1
1854 |   }
1855 | }
1856 | layer {
1857 |   name: "inception_4e/pool_proj"
1858 |   type: "Convolution"
1859 |   bottom: "inception_4e/pool"
1860 |   top: "inception_4e/pool_proj"
1861 |   param {
1862 |     lr_mult: 1
1863 |     decay_mult: 1
1864 |   }
1865 |   param {
1866 |     lr_mult: 2
1867 |     decay_mult: 0
1868 |   }
1869 |   convolution_param {
1870 |     num_output: 128
1871 |     kernel_size: 1
1872 |     weight_filler {
1873 |       type: "xavier"
1874 |       std: 0.1
1875 |     }
1876 |     bias_filler {
1877 |       type: "constant"
1878 |       value: 0.2
1879 |     }
1880 |   }
1881 | }
1882 | layer {
1883 |   name: "inception_4e/relu_pool_proj"
1884 |   type: "ReLU"
1885 |   bottom: "inception_4e/pool_proj"
1886 |   top: "inception_4e/pool_proj"
1887 | }
1888 | layer {
1889 |   name: "inception_4e/output"
1890 |   type: "Concat"
1891 |   bottom: "inception_4e/1x1"
1892 |   bottom: "inception_4e/3x3"
1893 |   bottom: "inception_4e/5x5"
1894 |   bottom: "inception_4e/pool_proj"
1895 |   top: "inception_4e/output"
1896 | }
1897 | 
1898 | 
1899 | 
1900 | layer {
1901 |   name: "inception_5a/1x1"
1902 |   type: "Convolution"
1903 |   bottom: "inception_4e/output"
1904 |   top: "inception_5a/1x1"
1905 |   param {
1906 |     lr_mult: 1
1907 |     decay_mult: 1
1908 |   }
1909 |   param {
1910 |     lr_mult: 2
1911 |     decay_mult: 0
1912 |   }
1913 |   convolution_param {
1914 |     num_output: 256
1915 |     kernel_size: 1
1916 |     weight_filler {
1917 |       type: "xavier"
1918 |       std: 0.03
1919 |     }
1920 |     bias_filler {
1921 |       type: "constant"
1922 |       value: 0.2
1923 |     }
1924 |   }
1925 | }
1926 | layer {
1927 |   name: "inception_5a/relu_1x1"
1928 |   type: "ReLU"
1929 |   bottom: "inception_5a/1x1"
1930 |   top: "inception_5a/1x1"
1931 | }
1932 | 
1933 | layer {
1934 |   name: "inception_5a/3x3_reduce"
1935 |   type: "Convolution"
1936 |   bottom: "inception_4e/output"
1937 |   top: "inception_5a/3x3_reduce"
1938 |   param {
1939 |     lr_mult: 1
1940 |     decay_mult: 1
1941 |   }
1942 |   param {
1943 |     lr_mult: 2
1944 |     decay_mult: 0
1945 |   }
1946 |   convolution_param {
1947 |     num_output: 160
1948 |     kernel_size: 1
1949 |     weight_filler {
1950 |       type: "xavier"
1951 |       std: 0.09
1952 |     }
1953 |     bias_filler {
1954 |       type: "constant"
1955 |       value: 0.2
1956 |     }
1957 |   }
1958 | }
1959 | layer {
1960 |   name: "inception_5a/relu_3x3_reduce"
1961 |   type: "ReLU"
1962 |   bottom: "inception_5a/3x3_reduce"
1963 |   top: "inception_5a/3x3_reduce"
1964 | }
1965 | 
1966 | layer {
1967 |   name: "inception_5a/3x3"
1968 |   type: "Convolution"
1969 |   bottom: "inception_5a/3x3_reduce"
1970 |   top: "inception_5a/3x3"
1971 |   param {
1972 |     lr_mult: 1
1973 |     decay_mult: 1
1974 |   }
1975 |   param {
1976 |     lr_mult: 2
1977 |     decay_mult: 0
1978 |   }
1979 |   convolution_param {
1980 |     num_output: 320
1981 |     pad: 1
1982 |     kernel_size: 3
1983 |     weight_filler {
1984 |       type: "xavier"
1985 |       std: 0.03
1986 |     }
1987 |     bias_filler {
1988 |       type: "constant"
1989 |       value: 0.2
1990 |     }
1991 |   }
1992 | }
1993 | layer {
1994 |   name: "inception_5a/relu_3x3"
1995 |   type: "ReLU"
1996 |   bottom: "inception_5a/3x3"
1997 |   top: "inception_5a/3x3"
1998 | }
1999 | layer {
2000 |   name: "inception_5a/5x5_reduce"
2001 |   type: "Convolution"
2002 |   bottom: "inception_4e/output"
2003 |   top: "inception_5a/5x5_reduce"
2004 |   param {
2005 |     lr_mult: 1
2006 |     decay_mult: 1
2007 |   }
2008 |   param {
2009 |     lr_mult: 2
2010 |     decay_mult: 0
2011 |   }
2012 |   convolution_param {
2013 |     num_output: 32
2014 |     kernel_size: 1
2015 |     weight_filler {
2016 |       type: "xavier"
2017 |       std: 0.2
2018 |     }
2019 |     bias_filler {
2020 |       type: "constant"
2021 |       value: 0.2
2022 |     }
2023 |   }
2024 | }
2025 | layer {
2026 |   name: "inception_5a/relu_5x5_reduce"
2027 |   type: "ReLU"
2028 |   bottom: "inception_5a/5x5_reduce"
2029 |   top: "inception_5a/5x5_reduce"
2030 | }
2031 | layer {
2032 |   name: "inception_5a/5x5"
2033 |   type: "Convolution"
2034 |   bottom: "inception_5a/5x5_reduce"
2035 |   top: "inception_5a/5x5"
2036 |   param {
2037 |     lr_mult: 1
2038 |     decay_mult: 1
2039 |   }
2040 |   param {
2041 |     lr_mult: 2
2042 |     decay_mult: 0
2043 |   }
2044 |   convolution_param {
2045 |     num_output: 128
2046 |     pad: 2
2047 |     kernel_size: 5
2048 |     weight_filler {
2049 |       type: "xavier"
2050 |       std: 0.03
2051 |     }
2052 |     bias_filler {
2053 |       type: "constant"
2054 |       value: 0.2
2055 |     }
2056 |   }
2057 | }
2058 | layer {
2059 |   name: "inception_5a/relu_5x5"
2060 |   type: "ReLU"
2061 |   bottom: "inception_5a/5x5"
2062 |   top: "inception_5a/5x5"
2063 | }
2064 | layer {
2065 |   name: "inception_5a/pool"
2066 |   type: "Pooling"
2067 |   bottom: "inception_4e/output"
2068 |   top: "inception_5a/pool"
2069 |   pooling_param {
2070 |     pool: MAX
2071 |     kernel_size: 3
2072 |     stride: 1
2073 |     pad: 1
2074 |   }
2075 | }
2076 | layer {
2077 |   name: "inception_5a/pool_proj"
2078 |   type: "Convolution"
2079 |   bottom: "inception_5a/pool"
2080 |   top: "inception_5a/pool_proj"
2081 |   param {
2082 |     lr_mult: 1
2083 |     decay_mult: 1
2084 |   }
2085 |   param {
2086 |     lr_mult: 2
2087 |     decay_mult: 0
2088 |   }
2089 |   convolution_param {
2090 |     num_output: 128
2091 |     kernel_size: 1
2092 |     weight_filler {
2093 |       type: "xavier"
2094 |       std: 0.1
2095 |     }
2096 |     bias_filler {
2097 |       type: "constant"
2098 |       value: 0.2
2099 |     }
2100 |   }
2101 | }
2102 | layer {
2103 |   name: "inception_5a/relu_pool_proj"
2104 |   type: "ReLU"
2105 |   bottom: "inception_5a/pool_proj"
2106 |   top: "inception_5a/pool_proj"
2107 | }
2108 | layer {
2109 |   name: "inception_5a/output"
2110 |   type: "Concat"
2111 |   bottom: "inception_5a/1x1"
2112 |   bottom: "inception_5a/3x3"
2113 |   bottom: "inception_5a/5x5"
2114 |   bottom: "inception_5a/pool_proj"
2115 |   top: "inception_5a/output"
2116 | }
2117 | 
2118 | layer {
2119 |   name: "inception_5b/1x1"
2120 |   type: "Convolution"
2121 |   bottom: "inception_5a/output"
2122 |   top: "inception_5b/1x1"
2123 |   param {
2124 |     lr_mult: 1
2125 |     decay_mult: 1
2126 |   }
2127 |   param {
2128 |     lr_mult: 2
2129 |     decay_mult: 0
2130 |   }
2131 |   convolution_param {
2132 |     num_output: 720
2133 |     kernel_size: 1
2134 |     weight_filler {
2135 |       type: "xavier"
2136 |       std: 0.1
2137 |     }
2138 |     bias_filler {
2139 |       type: "constant"
2140 |       value: 0.2
2141 |     }
2142 |   }
2143 | }
2144 | layer {
2145 |   name: "inception_5b/relu_1x1"
2146 |   type: "ReLU"
2147 |   bottom: "inception_5b/1x1"
2148 |   top: "inception_5b/1x1"
2149 | }
2150 | layer {
2151 |   name: "inception_5b/3x3_reduce"
2152 |   type: "Convolution"
2153 |   bottom: "inception_5a/output"
2154 |   top: "inception_5b/3x3_reduce"
2155 |   param {
2156 |     lr_mult: 1
2157 |     decay_mult: 1
2158 |   }
2159 |   param {
2160 |     lr_mult: 1
2161 |     decay_mult: 0
2162 |   }
2163 |   convolution_param {
2164 |     num_output: 192
2165 |     kernel_size: 1
2166 |     weight_filler {
2167 |       type: "xavier"
2168 |       std: 0.1
2169 |     }
2170 |     bias_filler {
2171 |       type: "constant"
2172 |       value: 0.2
2173 |     }
2174 |   }
2175 | }
2176 | layer {
2177 |   name: "inception_5b/relu_3x3_reduce"
2178 |   type: "ReLU"
2179 |   bottom: "inception_5b/3x3_reduce"
2180 |   top: "inception_5b/3x3_reduce"
2181 | }
2182 | layer {
2183 |   name: "inception_5b/3x3"
2184 |   type: "Convolution"
2185 |   bottom: "inception_5b/3x3_reduce"
2186 |   top: "inception_5b/3x3"
2187 |   param {
2188 |     lr_mult: 1
2189 |     decay_mult: 1
2190 |   }
2191 |   param {
2192 |     lr_mult: 2
2193 |     decay_mult: 0
2194 |   }
2195 |   convolution_param {
2196 |     num_output: 720
2197 |     pad: 1
2198 |     kernel_size: 3
2199 |     weight_filler {
2200 |       type: "xavier"
2201 |       std: 0.1
2202 |     }
2203 |     bias_filler {
2204 |       type: "constant"
2205 |       value: 0.2
2206 |     }
2207 |   }
2208 | }
2209 | layer {
2210 |   name: "inception_5b/relu_3x3"
2211 |   type: "ReLU"
2212 |   bottom: "inception_5b/3x3"
2213 |   top: "inception_5b/3x3"
2214 | }
2215 | layer {
2216 |   name: "inception_5b/5x5_reduce"
2217 |   type: "Convolution"
2218 |   bottom: "inception_5a/output"
2219 |   top: "inception_5b/5x5_reduce"
2220 |   param {
2221 |     lr_mult: 1
2222 |     decay_mult: 1
2223 |   }
2224 |   param {
2225 |     lr_mult: 2
2226 |     decay_mult: 0
2227 |   }
2228 |   convolution_param {
2229 |     num_output: 48
2230 |     kernel_size: 1
2231 |     weight_filler {
2232 |       type: "xavier"
2233 |       std: 0.1
2234 |     }
2235 |     bias_filler {
2236 |       type: "constant"
2237 |       value: 0.2
2238 |     }
2239 |   }
2240 | }
2241 | layer {
2242 |   name: "inception_5b/relu_5x5_reduce"
2243 |   type: "ReLU"
2244 |   bottom: "inception_5b/5x5_reduce"
2245 |   top: "inception_5b/5x5_reduce"
2246 | }
2247 | layer {
2248 |   name: "inception_5b/5x5"
2249 |   type: "Convolution"
2250 |   bottom: "inception_5b/5x5_reduce"
2251 |   top: "inception_5b/5x5"
2252 |   param {
2253 |     lr_mult: 1
2254 |     decay_mult: 1
2255 |   }
2256 |   param {
2257 |     lr_mult: 2
2258 |     decay_mult: 0
2259 |   }
2260 |   convolution_param {
2261 |     num_output: 128
2262 |     pad: 2
2263 |     kernel_size: 5
2264 |     weight_filler {
2265 |       type: "xavier"
2266 |       std: 0.1
2267 |     }
2268 |     bias_filler {
2269 |       type: "constant"
2270 |       value: 0.2
2271 |     }
2272 |   }
2273 | }
2274 | layer {
2275 |   name: "inception_5b/relu_5x5"
2276 |   type: "ReLU"
2277 |   bottom: "inception_5b/5x5"
2278 |   top: "inception_5b/5x5"
2279 | }
2280 | layer {
2281 |   name: "inception_5b/pool"
2282 |   type: "Pooling"
2283 |   bottom: "inception_5a/output"
2284 |   top: "inception_5b/pool"
2285 |   pooling_param {
2286 |     pool: MAX
2287 |     kernel_size: 3
2288 |     stride: 1
2289 |     pad: 1
2290 |   }
2291 | }
2292 | layer {
2293 |   name: "inception_5b/pool_proj"
2294 |   type: "Convolution"
2295 |   bottom: "inception_5b/pool"
2296 |   top: "inception_5b/pool_proj"
2297 |   param {
2298 |     lr_mult: 1
2299 |     decay_mult: 1
2300 |   }
2301 |   param {
2302 |     lr_mult: 2
2303 |     decay_mult: 0
2304 |   }
2305 |   convolution_param {
2306 |     num_output: 128
2307 |     kernel_size: 1
2308 |     weight_filler {
2309 |       type: "xavier"
2310 |       std: 0.1
2311 |     }
2312 |     bias_filler {
2313 |       type: "constant"
2314 |       value: 0.2
2315 |     }
2316 |   }
2317 | }
2318 | layer {
2319 |   name: "inception_5b/relu_pool_proj"
2320 |   type: "ReLU"
2321 |   bottom: "inception_5b/pool_proj"
2322 |   top: "inception_5b/pool_proj"
2323 | }
2324 | layer {
2325 |   name: "inception_5b/output"
2326 |   type: "Concat"
2327 |   bottom: "inception_5b/1x1"
2328 |   bottom: "inception_5b/3x3"
2329 |   bottom: "inception_5b/5x5"
2330 |   bottom: "inception_5b/pool_proj"
2331 |   top: "inception_5b/output"
2332 | }
2333 | layer {
2334 |   name: "pool5/drop_s1"
2335 |   type: "Dropout"
2336 |   bottom: "inception_5b/output"
2337 |   top: "pool5/drop_s1"
2338 |   dropout_param {
2339 |     dropout_ratio: 0.4
2340 |   }
2341 | }
2342 | layer {
2343 |   name: "cvg/classifier"
2344 |   type: "Convolution"
2345 |   bottom: "pool5/drop_s1"
2346 |   top: "cvg/classifier"
2347 |   param {
2348 |     lr_mult: 1
2349 |     decay_mult: 1
2350 |   }
2351 |   param {
2352 |     lr_mult: 2
2353 |     decay_mult: 0
2354 |   }
2355 |   convolution_param {
2356 |     num_output: 1
2357 |     kernel_size: 1
2358 |     weight_filler {
2359 |       type: "xavier"
2360 |       std: 0.03
2361 |     }
2362 |     bias_filler {
2363 |       type: "constant"
2364 |       value: 0.
2365 |     }
2366 |   }
2367 | }
2368 | layer {
2369 |   name: "coverage/sig"
2370 |   type: "Sigmoid"
2371 |   bottom: "cvg/classifier"
2372 |   top: "coverage"
2373 | }
2374 | layer {
2375 |   name: "bbox/regressor"
2376 |   type: "Convolution"
2377 |   bottom: "pool5/drop_s1"
2378 |   top: "bboxes"
2379 |   param {
2380 |     lr_mult: 1
2381 |     decay_mult: 1
2382 |   }
2383 |   param {
2384 |     lr_mult: 2
2385 |     decay_mult: 0
2386 |   }
2387 |   convolution_param {
2388 |     num_output: 4
2389 |     kernel_size: 1
2390 |     weight_filler {
2391 |       type: "xavier"
2392 |       std: 0.03
2393 |     }
2394 |     bias_filler {
2395 |       type: "constant"
2396 |       value: 0.
2397 |     }
2398 |   }
2399 | }
2400 | 
2401 | ######################################################################
2402 | # End of convolutional network
2403 | ######################################################################
2404 | 
2405 | # Convert bboxes
2406 | layer {
2407 |   name: "bbox_mask"
2408 |   type: "Eltwise"
2409 |   bottom: "bboxes"
2410 |   bottom: "coverage-block"
2411 |   top: "bboxes-masked"
2412 |   eltwise_param {
2413 |     operation: PROD
2414 |   }
2415 |   include { phase: TRAIN }
2416 |   include { phase: TEST stage: "val" }
2417 | }
2418 | layer {
2419 |   name: "bbox-norm"
2420 |   type: "Eltwise"
2421 |   bottom: "bboxes-masked"
2422 |   bottom: "size-block"
2423 |   top: "bboxes-masked-norm"
2424 |   eltwise_param {
2425 |     operation: PROD
2426 |   }
2427 |   include { phase: TRAIN }
2428 |   include { phase: TEST stage: "val" }
2429 | }
2430 | layer {
2431 |   name: "bbox-obj-norm"
2432 |   type: "Eltwise"
2433 |   bottom: "bboxes-masked-norm"
2434 |   bottom: "obj-block"
2435 |   top: "bboxes-obj-masked-norm"
2436 |   eltwise_param {
2437 |     operation: PROD
2438 |   }
2439 |   include { phase: TRAIN }
2440 |   include { phase: TEST stage: "val" }
2441 | }
2442 | 
2443 | # Loss layers
2444 | layer {
2445 |   name: "bbox_loss"
2446 |   type: "L1Loss"
2447 |   bottom: "bboxes-obj-masked-norm"
2448 |   bottom: "bbox-obj-label-norm"
2449 |   top: "loss_bbox"
2450 |   loss_weight: 2
2451 |   include { phase: TRAIN }
2452 |   include { phase: TEST stage: "val" }
2453 | }
2454 | layer {
2455 |   name: "coverage_loss"
2456 |   type: "EuclideanLoss"
2457 |   bottom: "coverage"
2458 |   bottom: "coverage-label"
2459 |   top: "loss_coverage"
2460 |   include { phase: TRAIN }
2461 |   include { phase: TEST stage: "val" }
2462 | }
2463 | 
2464 | # Cluster bboxes
2465 | layer {
2466 |     type: 'Python'
2467 |     name: 'cluster'
2468 |     bottom: 'coverage'
2469 |     bottom: 'bboxes'
2470 |     top: 'bbox-list'
2471 |     python_param {
2472 |         module: 'caffe.layers.detectnet.clustering'
2473 |         layer: 'ClusterDetections'
2474 |         param_str : '1280, 720, 16, 0.6, 3, 0.02, 22'
2475 |     }
2476 |     include: { phase: TEST }
2477 | }
2478 | 
2479 | # Calculate mean average precision
2480 | layer {
2481 |   type: 'Python'
2482 |   name: 'cluster_gt'
2483 |   bottom: 'coverage-label'
2484 |   bottom: 'bbox-label'
2485 |   top: 'bbox-list-label'
2486 |   python_param {
2487 |       module: 'caffe.layers.detectnet.clustering'
2488 |       layer: 'ClusterGroundtruth'
2489 |       param_str : '1280, 720, 16'
2490 |   }
2491 |   include: { phase: TEST stage: "val" }
2492 | }
2493 | layer {
2494 |     type: 'Python'
2495 |     name: 'score'
2496 |     bottom: 'bbox-list-label'
2497 |     bottom: 'bbox-list'
2498 |     top: 'bbox-list-scored'
2499 |     python_param {
2500 |         module: 'caffe.layers.detectnet.mean_ap'
2501 |         layer: 'ScoreDetections'
2502 |     }
2503 |     include: { phase: TEST stage: "val" }
2504 | }
2505 | layer {
2506 |     type: 'Python'
2507 |     name: 'mAP'
2508 |     bottom: 'bbox-list-scored'
2509 |     top: 'mAP'
2510 |     top: 'precision'
2511 |     top: 'recall'
2512 |     python_param {
2513 |         module: 'caffe.layers.detectnet.mean_ap'
2514 |         layer: 'mAP'
2515 |         param_str : '1280, 720, 16'
2516 |     }
2517 |     include: { phase: TEST stage: "val" }
2518 | }
2519 | 


--------------------------------------------------------------------------------
/LogoDetection.py:
--------------------------------------------------------------------------------
  1 | # This might be run only once if no ffmpeg is installed
  2 | #import imageio
  3 | #imageio.plugins.ffmpeg.download()
  4 | 
  5 | import cv2
  6 | import numpy as np
  7 | import os
  8 | import time
  9 | from PIL import Image
 10 | from math import sqrt, ceil, floor
 11 | import numpy
 12 | 
 13 | from google.protobuf import text_format
 14 | from moviepy.editor import VideoFileClip
 15 | import image_slicer
 16 | from collections import Counter
 17 | 
 18 | import scipy.misc
 19 | os.environ['GLOG_minloglevel'] = '2' # Suppress most caffe output
 20 | 
 21 | import caffe
 22 | from caffe.proto import caffe_pb2
 23 | import random
 24 | 
 25 | countedLogos = []
 26 | frameNumber = 0
 27 | 
 28 | def incrementFrameNumber():
 29 |     global frameNumber
 30 |     frameNumber += 1
 31 | 
 32 | def addLogoEntry(logo):
 33 |     global countedLogos
 34 |     countedLogos.append(logo)
 35 | 
 36 | DEPLOY_FILE = 'deploy.prototxt'
 37 | MEAN_FILE = None #'/Users/kris/Downloads/fedex_mil_model_epoch_70.0/mean.binaryproto'
 38 | #'/Users/kris/Downloads/fedex_mil_model_epoch_70.0/snapshot_iter_28000.caffemodel'
 39 | MODELS = ['fedex', 'enterprise', 'adidas', 'hankook', 'unicredit']
 40 | BATCH_SIZE = 1
 41 | OUTPUT_FILE = 'output.mp4'
 42 | INPUT_FILE = '/Users/kris/Downloads/footbal-split-movie-57s.mp4'
 43 | USE_GPU = False
 44 | FPS = 60.0
 45 | 
 46 | # IMAGE_FILE = '/Users/kris/Downloads/football2-resized2/resized/195583_01_01.png'
 47 | 
 48 | class Logo:
 49 |     """Represents a single logo set."""
 50 |     frameNumber = 0
 51 |     foundBoxes = 0
 52 | 
 53 |     def __init__(self, frameNumber, foundBoxes):
 54 |         self.frameNumber = frameNumber
 55 |         self.foundBoxes = foundBoxes
 56 | 
 57 |     def __repr__(self):
 58 |      return "Logo(" + str(self.frameNumber) + ": " + str(self.foundBoxes) + " )"
 59 | 
 60 | 
 61 | def get_net(caffemodel, deploy_file, use_gpu=False):
 62 |     """
 63 |     Returns an instance of caffe.Net
 64 | 
 65 |     Arguments:
 66 |     caffemodel -- path to a .caffemodel file
 67 |     deploy_file -- path to a .prototxt file
 68 | 
 69 |     Keyword arguments:
 70 |     use_gpu -- if True, use the GPU for inference
 71 |     """
 72 |     if use_gpu:
 73 |         caffe.set_mode_gpu()
 74 |     else:
 75 |         caffe.set_mode_cpu()
 76 | 
 77 |     # load a new model
 78 |     return caffe.Net(deploy_file, caffemodel, caffe.TEST)
 79 | 
 80 | def get_transformer(deploy_file, mean_file=None):
 81 |     """
 82 |     Returns an instance of caffe.io.Transformer
 83 | 
 84 |     Arguments:
 85 |     deploy_file -- path to a .prototxt file
 86 | 
 87 |     Keyword arguments:
 88 |     mean_file -- path to a .binaryproto file (optional)
 89 |     """
 90 |     network = caffe_pb2.NetParameter()
 91 |     with open(deploy_file) as infile:
 92 |         text_format.Merge(infile.read(), network)
 93 | 
 94 |     if network.input_shape:
 95 |         dims = network.input_shape[0].dim
 96 |     else:
 97 |         dims = network.input_dim[:4]
 98 | 
 99 |     t = caffe.io.Transformer(
100 |             inputs = {'data': dims}
101 |             )
102 |     t.set_transpose('data', (2,0,1)) # transpose to (channels, height, width)
103 | 
104 |     # color images
105 |     if dims[1] == 3:
106 |         # channel swap
107 |         t.set_channel_swap('data', (2,1,0))
108 | 
109 |     if mean_file:
110 |         # set mean pixel
111 |         println("mean file exists")
112 |         with open(mean_file,'rb') as infile:
113 |             blob = caffe_pb2.BlobProto()
114 |             blob.MergeFromString(infile.read())
115 |             if blob.HasField('shape'):
116 |                 blob_dims = blob.shape
117 |                 assert len(blob_dims) == 4, 'Shape should have 4 dimensions - shape is "%s"' % blob.shape
118 |             elif blob.HasField('num') and blob.HasField('channels') and \
119 |                     blob.HasField('height') and blob.HasField('width'):
120 |                 blob_dims = (blob.num, blob.channels, blob.height, blob.width)
121 |             else:
122 |                 raise ValueError('blob does not provide shape or 4d dimensions')
123 |             pixel = np.reshape(blob.data, blob_dims[1:]).mean(1).mean(1)
124 |             t.set_mean('data', pixel)
125 | 
126 |     return t
127 | 
128 | def resize_img(image, height, width):
129 |     """
130 |     Resizes the image to detectnet inputs
131 | 
132 |     Arguments:
133 |     image -- a single image
134 |     height -- height of the network input
135 |     width -- width of the network input
136 |     """
137 |     image = np.array(image)
138 |     image = scipy.misc.imresize(image, (height, width), 'bilinear')
139 |     return image
140 | 
141 | def draw_bboxes(image, locations, clr):
142 |     """
143 |     Draws the bounding boxes into an image
144 | 
145 |     Arguments:
146 |     image -- a single image already resized
147 |     locations -- the location of the bounding boxes
148 |     """
149 |     boxesFound = 0
150 |     for left,top,right,bottom,confidence in locations:
151 |         if confidence==0:
152 |             continue
153 |         boxesFound += 1
154 |         cv2.rectangle(image,(left,top),(right,bottom),clr,3)
155 |     return (boxesFound, image)
156 | 
157 | def forward_pass(image, net, transformer, batch_size=None):
158 |     """
159 |     Returns scores for each image as an np.ndarray (nImages x nClasses)
160 | 
161 |     Arguments:
162 |     image -- a list of np.ndarrays
163 |     net -- a caffe.Net
164 |     transformer -- a caffe.io.Transformer
165 | 
166 |     Keyword arguments:
167 |     batch_size -- how many images can be processed at once
168 |         (a high value may result in out-of-memory errors)
169 |     """
170 |     if batch_size is None:
171 |         batch_size = 1
172 | 
173 |     caffe_images = []
174 | 
175 |     if image.ndim == 2:
176 |         caffe_images.append(image[:,:,np.newaxis])
177 |     else:
178 |         caffe_images.append(image)
179 | 
180 |     dims = transformer.inputs['data'][1:]
181 | 
182 |     scores = None
183 |     for chunk in [caffe_images[x:x+batch_size] for x in xrange(0, len(caffe_images), batch_size)]:
184 |         new_shape = (len(chunk),) + tuple(dims)
185 |         if net.blobs['data'].data.shape != new_shape:
186 |             net.blobs['data'].reshape(*new_shape)
187 |         for index, image in enumerate(chunk):
188 |             image_data = transformer.preprocess('data', image)
189 |             net.blobs['data'].data[index] = image_data
190 |         start = time.time()
191 |         output = net.forward()[net.outputs[-1]]
192 |         end = time.time()
193 |         if scores is None:
194 |             scores = np.copy(output)
195 |         else:
196 |             scores = np.vstack((scores, output))
197 |         print 'Processed %s/%s images in %f seconds ...' % (len(scores), len(caffe_images), (end - start))
198 | 
199 |     return scores
200 | 
201 | def classify(caffemodel, deploy_file, image, clr,
202 |         mean_file=None, batch_size=None, use_gpu=False):
203 |     """
204 |     Classify some images against a Caffe model and print the results
205 | 
206 |     Arguments:
207 |     caffemodel -- path to a .caffemodel
208 |     deploy_file -- path to a .prototxt
209 |     image_files -- list of paths to images
210 | 
211 |     Keyword arguments:
212 |     mean_file -- path to a .binaryproto
213 |     use_gpu -- if True, run inference on the GPU
214 |     """
215 |     # Load the model
216 |     net = get_net(caffemodel, deploy_file, use_gpu)
217 |     transformer = get_transformer(deploy_file, mean_file)
218 |     _, channels, height, width = transformer.inputs['data']
219 |     if channels == 3:
220 |         mode = 'RGB'
221 |     elif channels == 1:
222 |         mode = 'L'
223 |     else:
224 |         raise ValueError('Invalid number for channels: %s' % channels)
225 | 
226 |     image = resize_img(image, height, width)
227 | 
228 |     # Classify the image
229 |     scores = forward_pass(image, net, transformer, batch_size=batch_size)
230 |     # print("Scores: ")
231 |     # print(scores)
232 |     ### Process the results
233 | 
234 |     # Format of scores is [ batch_size x max_bbox_per_image x 5 (xl, yt, xr, yb, confidence) ]
235 |     # https://github.com/NVIDIA/caffe/blob/v0.15.13/python/caffe/layers/detectnet/clustering.py#L81
236 |     for i, image_results in enumerate(scores):
237 |         boxesFound, img_result = draw_bboxes(image,image_results, clr)
238 |     # This line is optinal, in this case we resize to the size of the original input video, can be removed
239 |     #img_result = resize_img(img_result,720,1280)
240 |     return (boxesFound, img_result)
241 | 
242 | def getColorForClass(modelName):
243 |     return {
244 |         'fedex': (255,0,0),
245 |         'enterprise': (0,0,255),
246 |         'unicredit': (0,255,0),
247 |         'amstel': (150,150,0),
248 |         'adidas': (0,150,150),
249 |         'hankook': (0,255,255)
250 |     }[modelName]
251 | 
252 | def detect_logos(image):
253 |     """
254 |     Runs our pipeline given a single image and returns another one with the bounding boxes drawn
255 | 
256 |     Arguments:
257 |     image -- cv2 image file being 1/4 of the original
258 |     """
259 |     result = image
260 |     boxes = {}
261 |     for model in MODELS:
262 |         print("Detecting bboxes for: " + model)
263 |         clr = getColorForClass(model)
264 |         boxesFound, result = classify('./models/'+model+'.caffemodel', DEPLOY_FILE, result, clr, MEAN_FILE, BATCH_SIZE, USE_GPU)
265 |         boxes[model] = boxesFound
266 |     return (boxes, result)
267 | 
268 | def slice_image(image, number_tiles):
269 |     im = toPILImage(image)
270 |     im_w, im_h = im.size
271 |     columns, rows = image_slicer.calc_columns_rows(number_tiles)
272 |     extras = (columns * rows) - number_tiles
273 |     tile_w, tile_h = int(floor(im_w / columns)), int(floor(im_h / rows))
274 | 
275 |     tiles = []
276 |     number = 1
277 |     for pos_y in range(0, im_h - rows, tile_h): # -rows for rounding error.
278 |         for pos_x in range(0, im_w - columns, tile_w): # as above.
279 |             area = (pos_x, pos_y, pos_x + tile_w, pos_y + tile_h)
280 |             image = im.crop(area)
281 |             position = (int(floor(pos_x / tile_w)) + 1,
282 |                         int(floor(pos_y / tile_h)) + 1)
283 |             coords = (pos_x, pos_y)
284 |             tile = image_slicer.Tile(image, number, position, coords)
285 |             tiles.append(tile)
286 |             number += 1
287 |     return tuple(tiles)
288 | 
289 | def toPILImage(opencvImage):
290 |     img = cv2.cvtColor(opencvImage, cv2.COLOR_BGR2RGB)
291 |     im = Image.fromarray(img)
292 |     return im
293 | 
294 | def toOpenCVFormat(pilImage):
295 |     open_cv_image = numpy.array(pilImage)
296 |     image = open_cv_image[:, :, ::-1].copy()
297 |     return image
298 | 
299 | def joinImages(modified_images):
300 |     result = Image.new("RGB", (1280, 720))
301 | 
302 |     for index, img in enumerate(modified_images):
303 |         img.thumbnail((640, 360), Image.ANTIALIAS)
304 |     result.paste(modified_images[0], (0, 0))
305 |     result.paste(modified_images[2], (0, 360))
306 |     result.paste(modified_images[1], (640, 0))
307 |     result.paste(modified_images[3], (640, 360))
308 |     return result
309 | 
310 | def merge_dicts(allBoxes):
311 |     inp = [dict(x) for x in allBoxes]
312 |     count = Counter()
313 |     for y in inp:
314 |         count += Counter(y)
315 |     return dict(count)
316 | 
317 | def detect_logos_full_img(image):
318 |     """
319 |     Splits a single frame into 4 images
320 | 
321 |     Arguments:
322 |     image -- cv2 image file for single video frame
323 |     """
324 |     tiles = slice_image(image, 4)
325 |     modified_images = []
326 |     print("tiles length: " + str(len(tiles)))
327 |     fullImageBoxes = []
328 |     for i, tile in enumerate(tiles):
329 |         open_cv_image = numpy.array(tile.image)
330 |         image = open_cv_image[:, :, ::-1].copy()
331 |         boxes, img_detected = detect_logos(image)
332 |         modified_images.append(toPILImage(img_detected))
333 |         fullImageBoxes.append(boxes)
334 |     img = joinImages(modified_images)
335 |     allBoxes = merge_dicts(fullImageBoxes)
336 |     print("All boxes: " + str(allBoxes))
337 |     addLogoEntry(Logo(frameNumber, allBoxes))
338 |     incrementFrameNumber()
339 |     return toOpenCVFormat(img)
340 | 
341 | def produceReport(logos, movieTime):
342 |     fedexFrameCount = 0
343 |     amstelFrameCount = 0
344 |     unicreditFrameCount = 0
345 |     adidasFrameCount = 0
346 |     hankookFrameCount = 0
347 |     enterpriseFrameCount = 0
348 |     allBoxes = []
349 | 
350 |     for logo in logos:
351 |         if 'fedex' in logo.foundBoxes and logo.foundBoxes['fedex'] > 0:
352 |             fedexFrameCount += 1
353 |         if 'amstel' in logo.foundBoxes and logo.foundBoxes['amstel'] > 0:
354 |             amstelFrameCount += 1
355 |         if 'unicredit' in logo.foundBoxes and logo.foundBoxes['unicredit'] > 0:
356 |             unicreditFrameCount += 1
357 |         if 'adidas' in logo.foundBoxes and logo.foundBoxes['adidas'] > 0:
358 |             adidasFrameCount += 1
359 |         if 'hankook' in logo.foundBoxes and logo.foundBoxes['hankook'] > 0:
360 |             hankookFrameCount += 1
361 |         if 'enterprise' in logo.foundBoxes and logo.foundBoxes['enterprise'] > 0:
362 |             enterpriseFrameCount += 1
363 |         allBoxes.append(logo.foundBoxes)
364 | 
365 |     allObjectsDetected = merge_dicts(allBoxes)
366 |     report = """
367 |     *****************************************************
368 | 
369 |             TOTAL TIME: %s s
370 |             LOGO DETECTOR REPORT:
371 | 
372 |             FEDEX STATS:
373 |                 frame count: %s
374 |                 total time: %s
375 |                 objects detected: %s
376 | 
377 |             AMSTEL STATS:
378 |                 frame count: %s
379 |                 total time: %s
380 |                 objects detected: %s
381 | 
382 |             UNICREDIT STATS:
383 |                 frame count: %s
384 |                 total time: %s
385 |                 objects detected: %s
386 | 
387 |             ADIDAS STATS:
388 |                 frame count: %s
389 |                 total time: %s
390 |                 objects detected: %s
391 | 
392 |             HANKOOK STATS:
393 |                 frame count: %s
394 |                 total time: %s
395 |                 objects detected: %s
396 | 
397 |             ENTERPRISE STATS:
398 |                 frame count: %s
399 |                 total time: %s
400 |                 objects detected: %s
401 | 
402 |             more information at: www.softwaremill.com
403 | 
404 |     *****************************************************
405 | 
406 |             """ % (str(movieTime),
407 |                   str(fedexFrameCount),
408 |                   "{:.4f}".format(fedexFrameCount / FPS) + " s",
409 |                   str(allObjectsDetected.get('fedex', 0)),
410 |                   str(amstelFrameCount),
411 |                   "{:.4f}".format(amstelFrameCount / FPS) + " s",
412 |                   str(allObjectsDetected.get('amstel', 0)),
413 |                   str(unicreditFrameCount),
414 |                   "{:.4f}".format(unicreditFrameCount / FPS) + " s",
415 |                   str(allObjectsDetected.get('unicredit', 0)),
416 |                   str(adidasFrameCount),
417 |                   "{:.4f}".format(adidasFrameCount / FPS) + " s",
418 |                   str(allObjectsDetected.get('adidas', 0)),
419 |                   str(hankookFrameCount),
420 |                   "{:.4f}".format(hankookFrameCount / FPS) + " s",
421 |                   str(allObjectsDetected.get('hankook', 0)),
422 |                   str(enterpriseFrameCount),
423 |                   "{:.4f}".format(enterpriseFrameCount / FPS) + " s",
424 |                   str(allObjectsDetected.get('enterprise', 0)))
425 |     print report
426 | 
427 | 
428 | if __name__ == '__main__':
429 |     frameNumber = 0
430 |     script_start_time = time.time()
431 | 
432 |     project_output = OUTPUT_FILE
433 | 
434 |     clip1 = VideoFileClip(INPUT_FILE)
435 |     white_clip = clip1.fl_image(detect_logos_full_img)
436 |     white_clip.write_videofile(project_output, audio=False)
437 | 
438 |     #IMAGE_FILE = '/Users/kris/Downloads/football2-resized2/resized/184196_01_01.png'
439 |     #IMAGE_FILE = '/Users/kris/Downloads/football/frame21238.jpg'
440 |     #image = cv2.imread(IMAGE_FILE);
441 |     #img = detect_logos_full_img(image)
442 |     #cv2.imwrite('frame2.jpg',img)
443 |     print 'Video took %f seconds.' % (time.time() - script_start_time)
444 |     print 'Counted logos: ' + str(countedLogos)
445 |     print 'Processed frames: ' + str(frameNumber)
446 |     produceReport(countedLogos, clip1.duration)
447 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # detectnet-tests
2 | Python scripts and other resources for tesing DetectNet on Nvidia DIGITS
3 | 


--------------------------------------------------------------------------------
/convert-to-kitti.py:
--------------------------------------------------------------------------------
 1 | import os, errno
 2 | import xml.etree.ElementTree
 3 | import random
 4 | from sklearn.cross_validation import train_test_split
 5 | import numpy
 6 | from shutil import copyfile
 7 | import shutil
 8 | 
 9 | ## Remove underscores: find football2-resized3 -type f -name "*_*" -exec sh -c 'd=$(dirname "$1"); mv "$1" "$d/$(basename "$1" | tr -d _)"' sh {} \;
10 | 
11 | sourceDirectory='/Users/kris/Downloads/football2-resized2/resized'
12 | destinationDirectory='/Users/kris/Downloads/football2-resized3'
13 | 
14 | # remove the dir First
15 | if os.path.exists(destinationDirectory):
16 |     shutil.rmtree(destinationDirectory)
17 | 
18 | if not os.path.exists(destinationDirectory):
19 |     os.makedirs(destinationDirectory)
20 |     os.makedirs(destinationDirectory + '/train')
21 |     os.makedirs(destinationDirectory + '/train/images')
22 |     os.makedirs(destinationDirectory + '/train/labels')
23 |     os.makedirs(destinationDirectory + '/val')
24 |     os.makedirs(destinationDirectory + '/val/images')
25 |     os.makedirs(destinationDirectory + '/val/labels')
26 | 
27 | def split():
28 |     data = numpy.array(os.listdir(sourceDirectory + '/annotations'))
29 |     train ,test = train_test_split(data,test_size=0.2)
30 |     return train, test
31 | 
32 | def processSingleFile(filename):
33 |     lines = []
34 |     if filename.endswith(".xml"):
35 |         print("Processing: {0}".format(os.path.join(sourceDirectory + '/annotations', filename)))
36 |         e = xml.etree.ElementTree.parse(os.path.join(sourceDirectory + '/annotations', filename)).getroot()
37 |         name, xmin, ymin, xmax, ymax = '','','','',''
38 |         for elem in e.iterfind('object'):
39 |             for oel in elem.iter():
40 |                 if(oel.tag == 'name'):
41 |                     name = oel.text.strip()
42 |                 elif(oel.tag == 'xmin'):
43 |                     xmin = oel.text.strip()
44 |                 elif(oel.tag == 'ymin'):
45 |                     ymin = oel.text.strip()
46 |                 elif(oel.tag == 'xmax'):
47 |                     xmax = oel.text.strip()
48 |                 elif(oel.tag == 'ymax'):
49 |                     ymax = oel.text.strip()
50 |                 else:
51 |                     continue
52 |             # Example: Car   0.00 0 3.11 801.18 169.78 848.06 186.03 1.41 1.59 4.01 19.44 1.17 65.54 -2.89
53 |             # name 0.00 0 0.00 xmin ymax xmax ymin 0.00 0.00 0.00 0.00 0.00 0.00 0.00
54 |             lines.append(name + ' ' + '0.00 0 0.00 ' + xmin + ' ' + ymin + ' ' + xmax + ' ' + ymax + ' 0.00 0.00 0.00 0.00 0.00 0.00 0.00')
55 |     return lines
56 | 
57 | 
58 | def convertToKitti(labelsDir, imagesDir, files):
59 |     for f in files:
60 |         pre, ext = os.path.splitext(f)
61 |         # create label file
62 |         lines = processSingleFile(f)
63 |         kitti_labels = pre + '.txt'
64 |         with open(os.path.join(destinationDirectory + labelsDir, kitti_labels), "w") as text_file:
65 |             for item in lines:
66 |                 text_file.write("%s\n" % item)
67 |         # copy the image file
68 |         imgFile = pre + '.png'
69 |         srcFile = os.path.join(sourceDirectory, imgFile)
70 |         dstFile = os.path.join(destinationDirectory + imagesDir, imgFile)
71 |         copyfile(srcFile, dstFile)
72 | 
73 | print("splitting into traint and test sets: ")
74 | train, test = split()
75 | 
76 | # Convert test subset
77 | convertToKitti('/val/labels', '/val/images', test)
78 | 
79 | # Convert train subset
80 | convertToKitti('/train/labels', '/train/images', train)
81 | 
82 | print('Done!')
83 | 


--------------------------------------------------------------------------------
/deploy.prototxt:
--------------------------------------------------------------------------------
   1 | input: "data"
   2 | input_shape {
   3 |   dim: 1
   4 |   dim: 3
   5 |   dim: 720
   6 |   dim: 1280
   7 | }
   8 | layer {
   9 |   name: "deploy_transform"
  10 |   type: "Power"
  11 |   bottom: "data"
  12 |   top: "transformed_data"
  13 |   power_param {
  14 |     shift: -127.0
  15 |   }
  16 | }
  17 | layer {
  18 |   name: "conv1/7x7_s2"
  19 |   type: "Convolution"
  20 |   bottom: "transformed_data"
  21 |   top: "conv1/7x7_s2"
  22 |   param {
  23 |     lr_mult: 1.0
  24 |     decay_mult: 1.0
  25 |   }
  26 |   param {
  27 |     lr_mult: 2.0
  28 |     decay_mult: 0.0
  29 |   }
  30 |   convolution_param {
  31 |     num_output: 64
  32 |     pad: 3
  33 |     kernel_size: 7
  34 |     stride: 2
  35 |     weight_filler {
  36 |       type: "xavier"
  37 |       std: 0.1
  38 |     }
  39 |     bias_filler {
  40 |       type: "constant"
  41 |       value: 0.2
  42 |     }
  43 |   }
  44 | }
  45 | layer {
  46 |   name: "conv1/relu_7x7"
  47 |   type: "ReLU"
  48 |   bottom: "conv1/7x7_s2"
  49 |   top: "conv1/7x7_s2"
  50 | }
  51 | layer {
  52 |   name: "pool1/3x3_s2"
  53 |   type: "Pooling"
  54 |   bottom: "conv1/7x7_s2"
  55 |   top: "pool1/3x3_s2"
  56 |   pooling_param {
  57 |     pool: MAX
  58 |     kernel_size: 3
  59 |     stride: 2
  60 |   }
  61 | }
  62 | layer {
  63 |   name: "pool1/norm1"
  64 |   type: "LRN"
  65 |   bottom: "pool1/3x3_s2"
  66 |   top: "pool1/norm1"
  67 |   lrn_param {
  68 |     local_size: 5
  69 |     alpha: 0.0001
  70 |     beta: 0.75
  71 |   }
  72 | }
  73 | layer {
  74 |   name: "conv2/3x3_reduce"
  75 |   type: "Convolution"
  76 |   bottom: "pool1/norm1"
  77 |   top: "conv2/3x3_reduce"
  78 |   param {
  79 |     lr_mult: 1.0
  80 |     decay_mult: 1.0
  81 |   }
  82 |   param {
  83 |     lr_mult: 2.0
  84 |     decay_mult: 0.0
  85 |   }
  86 |   convolution_param {
  87 |     num_output: 64
  88 |     kernel_size: 1
  89 |     weight_filler {
  90 |       type: "xavier"
  91 |       std: 0.1
  92 |     }
  93 |     bias_filler {
  94 |       type: "constant"
  95 |       value: 0.2
  96 |     }
  97 |   }
  98 | }
  99 | layer {
 100 |   name: "conv2/relu_3x3_reduce"
 101 |   type: "ReLU"
 102 |   bottom: "conv2/3x3_reduce"
 103 |   top: "conv2/3x3_reduce"
 104 | }
 105 | layer {
 106 |   name: "conv2/3x3"
 107 |   type: "Convolution"
 108 |   bottom: "conv2/3x3_reduce"
 109 |   top: "conv2/3x3"
 110 |   param {
 111 |     lr_mult: 1.0
 112 |     decay_mult: 1.0
 113 |   }
 114 |   param {
 115 |     lr_mult: 2.0
 116 |     decay_mult: 0.0
 117 |   }
 118 |   convolution_param {
 119 |     num_output: 192
 120 |     pad: 1
 121 |     kernel_size: 3
 122 |     weight_filler {
 123 |       type: "xavier"
 124 |       std: 0.03
 125 |     }
 126 |     bias_filler {
 127 |       type: "constant"
 128 |       value: 0.2
 129 |     }
 130 |   }
 131 | }
 132 | layer {
 133 |   name: "conv2/relu_3x3"
 134 |   type: "ReLU"
 135 |   bottom: "conv2/3x3"
 136 |   top: "conv2/3x3"
 137 | }
 138 | layer {
 139 |   name: "conv2/norm2"
 140 |   type: "LRN"
 141 |   bottom: "conv2/3x3"
 142 |   top: "conv2/norm2"
 143 |   lrn_param {
 144 |     local_size: 5
 145 |     alpha: 0.0001
 146 |     beta: 0.75
 147 |   }
 148 | }
 149 | layer {
 150 |   name: "pool2/3x3_s2"
 151 |   type: "Pooling"
 152 |   bottom: "conv2/norm2"
 153 |   top: "pool2/3x3_s2"
 154 |   pooling_param {
 155 |     pool: MAX
 156 |     kernel_size: 3
 157 |     stride: 2
 158 |   }
 159 | }
 160 | layer {
 161 |   name: "inception_3a/1x1"
 162 |   type: "Convolution"
 163 |   bottom: "pool2/3x3_s2"
 164 |   top: "inception_3a/1x1"
 165 |   param {
 166 |     lr_mult: 1.0
 167 |     decay_mult: 1.0
 168 |   }
 169 |   param {
 170 |     lr_mult: 2.0
 171 |     decay_mult: 0.0
 172 |   }
 173 |   convolution_param {
 174 |     num_output: 64
 175 |     kernel_size: 1
 176 |     weight_filler {
 177 |       type: "xavier"
 178 |       std: 0.03
 179 |     }
 180 |     bias_filler {
 181 |       type: "constant"
 182 |       value: 0.2
 183 |     }
 184 |   }
 185 | }
 186 | layer {
 187 |   name: "inception_3a/relu_1x1"
 188 |   type: "ReLU"
 189 |   bottom: "inception_3a/1x1"
 190 |   top: "inception_3a/1x1"
 191 | }
 192 | layer {
 193 |   name: "inception_3a/3x3_reduce"
 194 |   type: "Convolution"
 195 |   bottom: "pool2/3x3_s2"
 196 |   top: "inception_3a/3x3_reduce"
 197 |   param {
 198 |     lr_mult: 1.0
 199 |     decay_mult: 1.0
 200 |   }
 201 |   param {
 202 |     lr_mult: 2.0
 203 |     decay_mult: 0.0
 204 |   }
 205 |   convolution_param {
 206 |     num_output: 96
 207 |     kernel_size: 1
 208 |     weight_filler {
 209 |       type: "xavier"
 210 |       std: 0.09
 211 |     }
 212 |     bias_filler {
 213 |       type: "constant"
 214 |       value: 0.2
 215 |     }
 216 |   }
 217 | }
 218 | layer {
 219 |   name: "inception_3a/relu_3x3_reduce"
 220 |   type: "ReLU"
 221 |   bottom: "inception_3a/3x3_reduce"
 222 |   top: "inception_3a/3x3_reduce"
 223 | }
 224 | layer {
 225 |   name: "inception_3a/3x3"
 226 |   type: "Convolution"
 227 |   bottom: "inception_3a/3x3_reduce"
 228 |   top: "inception_3a/3x3"
 229 |   param {
 230 |     lr_mult: 1.0
 231 |     decay_mult: 1.0
 232 |   }
 233 |   param {
 234 |     lr_mult: 2.0
 235 |     decay_mult: 0.0
 236 |   }
 237 |   convolution_param {
 238 |     num_output: 128
 239 |     pad: 1
 240 |     kernel_size: 3
 241 |     weight_filler {
 242 |       type: "xavier"
 243 |       std: 0.03
 244 |     }
 245 |     bias_filler {
 246 |       type: "constant"
 247 |       value: 0.2
 248 |     }
 249 |   }
 250 | }
 251 | layer {
 252 |   name: "inception_3a/relu_3x3"
 253 |   type: "ReLU"
 254 |   bottom: "inception_3a/3x3"
 255 |   top: "inception_3a/3x3"
 256 | }
 257 | layer {
 258 |   name: "inception_3a/5x5_reduce"
 259 |   type: "Convolution"
 260 |   bottom: "pool2/3x3_s2"
 261 |   top: "inception_3a/5x5_reduce"
 262 |   param {
 263 |     lr_mult: 1.0
 264 |     decay_mult: 1.0
 265 |   }
 266 |   param {
 267 |     lr_mult: 2.0
 268 |     decay_mult: 0.0
 269 |   }
 270 |   convolution_param {
 271 |     num_output: 16
 272 |     kernel_size: 1
 273 |     weight_filler {
 274 |       type: "xavier"
 275 |       std: 0.2
 276 |     }
 277 |     bias_filler {
 278 |       type: "constant"
 279 |       value: 0.2
 280 |     }
 281 |   }
 282 | }
 283 | layer {
 284 |   name: "inception_3a/relu_5x5_reduce"
 285 |   type: "ReLU"
 286 |   bottom: "inception_3a/5x5_reduce"
 287 |   top: "inception_3a/5x5_reduce"
 288 | }
 289 | layer {
 290 |   name: "inception_3a/5x5"
 291 |   type: "Convolution"
 292 |   bottom: "inception_3a/5x5_reduce"
 293 |   top: "inception_3a/5x5"
 294 |   param {
 295 |     lr_mult: 1.0
 296 |     decay_mult: 1.0
 297 |   }
 298 |   param {
 299 |     lr_mult: 2.0
 300 |     decay_mult: 0.0
 301 |   }
 302 |   convolution_param {
 303 |     num_output: 32
 304 |     pad: 2
 305 |     kernel_size: 5
 306 |     weight_filler {
 307 |       type: "xavier"
 308 |       std: 0.03
 309 |     }
 310 |     bias_filler {
 311 |       type: "constant"
 312 |       value: 0.2
 313 |     }
 314 |   }
 315 | }
 316 | layer {
 317 |   name: "inception_3a/relu_5x5"
 318 |   type: "ReLU"
 319 |   bottom: "inception_3a/5x5"
 320 |   top: "inception_3a/5x5"
 321 | }
 322 | layer {
 323 |   name: "inception_3a/pool"
 324 |   type: "Pooling"
 325 |   bottom: "pool2/3x3_s2"
 326 |   top: "inception_3a/pool"
 327 |   pooling_param {
 328 |     pool: MAX
 329 |     kernel_size: 3
 330 |     stride: 1
 331 |     pad: 1
 332 |   }
 333 | }
 334 | layer {
 335 |   name: "inception_3a/pool_proj"
 336 |   type: "Convolution"
 337 |   bottom: "inception_3a/pool"
 338 |   top: "inception_3a/pool_proj"
 339 |   param {
 340 |     lr_mult: 1.0
 341 |     decay_mult: 1.0
 342 |   }
 343 |   param {
 344 |     lr_mult: 2.0
 345 |     decay_mult: 0.0
 346 |   }
 347 |   convolution_param {
 348 |     num_output: 32
 349 |     kernel_size: 1
 350 |     weight_filler {
 351 |       type: "xavier"
 352 |       std: 0.1
 353 |     }
 354 |     bias_filler {
 355 |       type: "constant"
 356 |       value: 0.2
 357 |     }
 358 |   }
 359 | }
 360 | layer {
 361 |   name: "inception_3a/relu_pool_proj"
 362 |   type: "ReLU"
 363 |   bottom: "inception_3a/pool_proj"
 364 |   top: "inception_3a/pool_proj"
 365 | }
 366 | layer {
 367 |   name: "inception_3a/output"
 368 |   type: "Concat"
 369 |   bottom: "inception_3a/1x1"
 370 |   bottom: "inception_3a/3x3"
 371 |   bottom: "inception_3a/5x5"
 372 |   bottom: "inception_3a/pool_proj"
 373 |   top: "inception_3a/output"
 374 | }
 375 | layer {
 376 |   name: "inception_3b/1x1"
 377 |   type: "Convolution"
 378 |   bottom: "inception_3a/output"
 379 |   top: "inception_3b/1x1"
 380 |   param {
 381 |     lr_mult: 1.0
 382 |     decay_mult: 1.0
 383 |   }
 384 |   param {
 385 |     lr_mult: 2.0
 386 |     decay_mult: 0.0
 387 |   }
 388 |   convolution_param {
 389 |     num_output: 128
 390 |     kernel_size: 1
 391 |     weight_filler {
 392 |       type: "xavier"
 393 |       std: 0.03
 394 |     }
 395 |     bias_filler {
 396 |       type: "constant"
 397 |       value: 0.2
 398 |     }
 399 |   }
 400 | }
 401 | layer {
 402 |   name: "inception_3b/relu_1x1"
 403 |   type: "ReLU"
 404 |   bottom: "inception_3b/1x1"
 405 |   top: "inception_3b/1x1"
 406 | }
 407 | layer {
 408 |   name: "inception_3b/3x3_reduce"
 409 |   type: "Convolution"
 410 |   bottom: "inception_3a/output"
 411 |   top: "inception_3b/3x3_reduce"
 412 |   param {
 413 |     lr_mult: 1.0
 414 |     decay_mult: 1.0
 415 |   }
 416 |   param {
 417 |     lr_mult: 2.0
 418 |     decay_mult: 0.0
 419 |   }
 420 |   convolution_param {
 421 |     num_output: 128
 422 |     kernel_size: 1
 423 |     weight_filler {
 424 |       type: "xavier"
 425 |       std: 0.09
 426 |     }
 427 |     bias_filler {
 428 |       type: "constant"
 429 |       value: 0.2
 430 |     }
 431 |   }
 432 | }
 433 | layer {
 434 |   name: "inception_3b/relu_3x3_reduce"
 435 |   type: "ReLU"
 436 |   bottom: "inception_3b/3x3_reduce"
 437 |   top: "inception_3b/3x3_reduce"
 438 | }
 439 | layer {
 440 |   name: "inception_3b/3x3"
 441 |   type: "Convolution"
 442 |   bottom: "inception_3b/3x3_reduce"
 443 |   top: "inception_3b/3x3"
 444 |   param {
 445 |     lr_mult: 1.0
 446 |     decay_mult: 1.0
 447 |   }
 448 |   param {
 449 |     lr_mult: 2.0
 450 |     decay_mult: 0.0
 451 |   }
 452 |   convolution_param {
 453 |     num_output: 192
 454 |     pad: 1
 455 |     kernel_size: 3
 456 |     weight_filler {
 457 |       type: "xavier"
 458 |       std: 0.03
 459 |     }
 460 |     bias_filler {
 461 |       type: "constant"
 462 |       value: 0.2
 463 |     }
 464 |   }
 465 | }
 466 | layer {
 467 |   name: "inception_3b/relu_3x3"
 468 |   type: "ReLU"
 469 |   bottom: "inception_3b/3x3"
 470 |   top: "inception_3b/3x3"
 471 | }
 472 | layer {
 473 |   name: "inception_3b/5x5_reduce"
 474 |   type: "Convolution"
 475 |   bottom: "inception_3a/output"
 476 |   top: "inception_3b/5x5_reduce"
 477 |   param {
 478 |     lr_mult: 1.0
 479 |     decay_mult: 1.0
 480 |   }
 481 |   param {
 482 |     lr_mult: 2.0
 483 |     decay_mult: 0.0
 484 |   }
 485 |   convolution_param {
 486 |     num_output: 32
 487 |     kernel_size: 1
 488 |     weight_filler {
 489 |       type: "xavier"
 490 |       std: 0.2
 491 |     }
 492 |     bias_filler {
 493 |       type: "constant"
 494 |       value: 0.2
 495 |     }
 496 |   }
 497 | }
 498 | layer {
 499 |   name: "inception_3b/relu_5x5_reduce"
 500 |   type: "ReLU"
 501 |   bottom: "inception_3b/5x5_reduce"
 502 |   top: "inception_3b/5x5_reduce"
 503 | }
 504 | layer {
 505 |   name: "inception_3b/5x5"
 506 |   type: "Convolution"
 507 |   bottom: "inception_3b/5x5_reduce"
 508 |   top: "inception_3b/5x5"
 509 |   param {
 510 |     lr_mult: 1.0
 511 |     decay_mult: 1.0
 512 |   }
 513 |   param {
 514 |     lr_mult: 2.0
 515 |     decay_mult: 0.0
 516 |   }
 517 |   convolution_param {
 518 |     num_output: 96
 519 |     pad: 2
 520 |     kernel_size: 5
 521 |     weight_filler {
 522 |       type: "xavier"
 523 |       std: 0.03
 524 |     }
 525 |     bias_filler {
 526 |       type: "constant"
 527 |       value: 0.2
 528 |     }
 529 |   }
 530 | }
 531 | layer {
 532 |   name: "inception_3b/relu_5x5"
 533 |   type: "ReLU"
 534 |   bottom: "inception_3b/5x5"
 535 |   top: "inception_3b/5x5"
 536 | }
 537 | layer {
 538 |   name: "inception_3b/pool"
 539 |   type: "Pooling"
 540 |   bottom: "inception_3a/output"
 541 |   top: "inception_3b/pool"
 542 |   pooling_param {
 543 |     pool: MAX
 544 |     kernel_size: 3
 545 |     stride: 1
 546 |     pad: 1
 547 |   }
 548 | }
 549 | layer {
 550 |   name: "inception_3b/pool_proj"
 551 |   type: "Convolution"
 552 |   bottom: "inception_3b/pool"
 553 |   top: "inception_3b/pool_proj"
 554 |   param {
 555 |     lr_mult: 1.0
 556 |     decay_mult: 1.0
 557 |   }
 558 |   param {
 559 |     lr_mult: 2.0
 560 |     decay_mult: 0.0
 561 |   }
 562 |   convolution_param {
 563 |     num_output: 64
 564 |     kernel_size: 1
 565 |     weight_filler {
 566 |       type: "xavier"
 567 |       std: 0.1
 568 |     }
 569 |     bias_filler {
 570 |       type: "constant"
 571 |       value: 0.2
 572 |     }
 573 |   }
 574 | }
 575 | layer {
 576 |   name: "inception_3b/relu_pool_proj"
 577 |   type: "ReLU"
 578 |   bottom: "inception_3b/pool_proj"
 579 |   top: "inception_3b/pool_proj"
 580 | }
 581 | layer {
 582 |   name: "inception_3b/output"
 583 |   type: "Concat"
 584 |   bottom: "inception_3b/1x1"
 585 |   bottom: "inception_3b/3x3"
 586 |   bottom: "inception_3b/5x5"
 587 |   bottom: "inception_3b/pool_proj"
 588 |   top: "inception_3b/output"
 589 | }
 590 | layer {
 591 |   name: "pool3/3x3_s2"
 592 |   type: "Pooling"
 593 |   bottom: "inception_3b/output"
 594 |   top: "pool3/3x3_s2"
 595 |   pooling_param {
 596 |     pool: MAX
 597 |     kernel_size: 3
 598 |     stride: 2
 599 |   }
 600 | }
 601 | layer {
 602 |   name: "inception_4a/1x1"
 603 |   type: "Convolution"
 604 |   bottom: "pool3/3x3_s2"
 605 |   top: "inception_4a/1x1"
 606 |   param {
 607 |     lr_mult: 1.0
 608 |     decay_mult: 1.0
 609 |   }
 610 |   param {
 611 |     lr_mult: 2.0
 612 |     decay_mult: 0.0
 613 |   }
 614 |   convolution_param {
 615 |     num_output: 192
 616 |     kernel_size: 1
 617 |     weight_filler {
 618 |       type: "xavier"
 619 |       std: 0.03
 620 |     }
 621 |     bias_filler {
 622 |       type: "constant"
 623 |       value: 0.2
 624 |     }
 625 |   }
 626 | }
 627 | layer {
 628 |   name: "inception_4a/relu_1x1"
 629 |   type: "ReLU"
 630 |   bottom: "inception_4a/1x1"
 631 |   top: "inception_4a/1x1"
 632 | }
 633 | layer {
 634 |   name: "inception_4a/3x3_reduce"
 635 |   type: "Convolution"
 636 |   bottom: "pool3/3x3_s2"
 637 |   top: "inception_4a/3x3_reduce"
 638 |   param {
 639 |     lr_mult: 1.0
 640 |     decay_mult: 1.0
 641 |   }
 642 |   param {
 643 |     lr_mult: 2.0
 644 |     decay_mult: 0.0
 645 |   }
 646 |   convolution_param {
 647 |     num_output: 96
 648 |     kernel_size: 1
 649 |     weight_filler {
 650 |       type: "xavier"
 651 |       std: 0.09
 652 |     }
 653 |     bias_filler {
 654 |       type: "constant"
 655 |       value: 0.2
 656 |     }
 657 |   }
 658 | }
 659 | layer {
 660 |   name: "inception_4a/relu_3x3_reduce"
 661 |   type: "ReLU"
 662 |   bottom: "inception_4a/3x3_reduce"
 663 |   top: "inception_4a/3x3_reduce"
 664 | }
 665 | layer {
 666 |   name: "inception_4a/3x3"
 667 |   type: "Convolution"
 668 |   bottom: "inception_4a/3x3_reduce"
 669 |   top: "inception_4a/3x3"
 670 |   param {
 671 |     lr_mult: 1.0
 672 |     decay_mult: 1.0
 673 |   }
 674 |   param {
 675 |     lr_mult: 2.0
 676 |     decay_mult: 0.0
 677 |   }
 678 |   convolution_param {
 679 |     num_output: 208
 680 |     pad: 1
 681 |     kernel_size: 3
 682 |     weight_filler {
 683 |       type: "xavier"
 684 |       std: 0.03
 685 |     }
 686 |     bias_filler {
 687 |       type: "constant"
 688 |       value: 0.2
 689 |     }
 690 |   }
 691 | }
 692 | layer {
 693 |   name: "inception_4a/relu_3x3"
 694 |   type: "ReLU"
 695 |   bottom: "inception_4a/3x3"
 696 |   top: "inception_4a/3x3"
 697 | }
 698 | layer {
 699 |   name: "inception_4a/5x5_reduce"
 700 |   type: "Convolution"
 701 |   bottom: "pool3/3x3_s2"
 702 |   top: "inception_4a/5x5_reduce"
 703 |   param {
 704 |     lr_mult: 1.0
 705 |     decay_mult: 1.0
 706 |   }
 707 |   param {
 708 |     lr_mult: 2.0
 709 |     decay_mult: 0.0
 710 |   }
 711 |   convolution_param {
 712 |     num_output: 16
 713 |     kernel_size: 1
 714 |     weight_filler {
 715 |       type: "xavier"
 716 |       std: 0.2
 717 |     }
 718 |     bias_filler {
 719 |       type: "constant"
 720 |       value: 0.2
 721 |     }
 722 |   }
 723 | }
 724 | layer {
 725 |   name: "inception_4a/relu_5x5_reduce"
 726 |   type: "ReLU"
 727 |   bottom: "inception_4a/5x5_reduce"
 728 |   top: "inception_4a/5x5_reduce"
 729 | }
 730 | layer {
 731 |   name: "inception_4a/5x5"
 732 |   type: "Convolution"
 733 |   bottom: "inception_4a/5x5_reduce"
 734 |   top: "inception_4a/5x5"
 735 |   param {
 736 |     lr_mult: 1.0
 737 |     decay_mult: 1.0
 738 |   }
 739 |   param {
 740 |     lr_mult: 2.0
 741 |     decay_mult: 0.0
 742 |   }
 743 |   convolution_param {
 744 |     num_output: 48
 745 |     pad: 2
 746 |     kernel_size: 5
 747 |     weight_filler {
 748 |       type: "xavier"
 749 |       std: 0.03
 750 |     }
 751 |     bias_filler {
 752 |       type: "constant"
 753 |       value: 0.2
 754 |     }
 755 |   }
 756 | }
 757 | layer {
 758 |   name: "inception_4a/relu_5x5"
 759 |   type: "ReLU"
 760 |   bottom: "inception_4a/5x5"
 761 |   top: "inception_4a/5x5"
 762 | }
 763 | layer {
 764 |   name: "inception_4a/pool"
 765 |   type: "Pooling"
 766 |   bottom: "pool3/3x3_s2"
 767 |   top: "inception_4a/pool"
 768 |   pooling_param {
 769 |     pool: MAX
 770 |     kernel_size: 3
 771 |     stride: 1
 772 |     pad: 1
 773 |   }
 774 | }
 775 | layer {
 776 |   name: "inception_4a/pool_proj"
 777 |   type: "Convolution"
 778 |   bottom: "inception_4a/pool"
 779 |   top: "inception_4a/pool_proj"
 780 |   param {
 781 |     lr_mult: 1.0
 782 |     decay_mult: 1.0
 783 |   }
 784 |   param {
 785 |     lr_mult: 2.0
 786 |     decay_mult: 0.0
 787 |   }
 788 |   convolution_param {
 789 |     num_output: 64
 790 |     kernel_size: 1
 791 |     weight_filler {
 792 |       type: "xavier"
 793 |       std: 0.1
 794 |     }
 795 |     bias_filler {
 796 |       type: "constant"
 797 |       value: 0.2
 798 |     }
 799 |   }
 800 | }
 801 | layer {
 802 |   name: "inception_4a/relu_pool_proj"
 803 |   type: "ReLU"
 804 |   bottom: "inception_4a/pool_proj"
 805 |   top: "inception_4a/pool_proj"
 806 | }
 807 | layer {
 808 |   name: "inception_4a/output"
 809 |   type: "Concat"
 810 |   bottom: "inception_4a/1x1"
 811 |   bottom: "inception_4a/3x3"
 812 |   bottom: "inception_4a/5x5"
 813 |   bottom: "inception_4a/pool_proj"
 814 |   top: "inception_4a/output"
 815 | }
 816 | layer {
 817 |   name: "inception_4b/1x1"
 818 |   type: "Convolution"
 819 |   bottom: "inception_4a/output"
 820 |   top: "inception_4b/1x1"
 821 |   param {
 822 |     lr_mult: 1.0
 823 |     decay_mult: 1.0
 824 |   }
 825 |   param {
 826 |     lr_mult: 2.0
 827 |     decay_mult: 0.0
 828 |   }
 829 |   convolution_param {
 830 |     num_output: 160
 831 |     kernel_size: 1
 832 |     weight_filler {
 833 |       type: "xavier"
 834 |       std: 0.03
 835 |     }
 836 |     bias_filler {
 837 |       type: "constant"
 838 |       value: 0.2
 839 |     }
 840 |   }
 841 | }
 842 | layer {
 843 |   name: "inception_4b/relu_1x1"
 844 |   type: "ReLU"
 845 |   bottom: "inception_4b/1x1"
 846 |   top: "inception_4b/1x1"
 847 | }
 848 | layer {
 849 |   name: "inception_4b/3x3_reduce"
 850 |   type: "Convolution"
 851 |   bottom: "inception_4a/output"
 852 |   top: "inception_4b/3x3_reduce"
 853 |   param {
 854 |     lr_mult: 1.0
 855 |     decay_mult: 1.0
 856 |   }
 857 |   param {
 858 |     lr_mult: 2.0
 859 |     decay_mult: 0.0
 860 |   }
 861 |   convolution_param {
 862 |     num_output: 112
 863 |     kernel_size: 1
 864 |     weight_filler {
 865 |       type: "xavier"
 866 |       std: 0.09
 867 |     }
 868 |     bias_filler {
 869 |       type: "constant"
 870 |       value: 0.2
 871 |     }
 872 |   }
 873 | }
 874 | layer {
 875 |   name: "inception_4b/relu_3x3_reduce"
 876 |   type: "ReLU"
 877 |   bottom: "inception_4b/3x3_reduce"
 878 |   top: "inception_4b/3x3_reduce"
 879 | }
 880 | layer {
 881 |   name: "inception_4b/3x3"
 882 |   type: "Convolution"
 883 |   bottom: "inception_4b/3x3_reduce"
 884 |   top: "inception_4b/3x3"
 885 |   param {
 886 |     lr_mult: 1.0
 887 |     decay_mult: 1.0
 888 |   }
 889 |   param {
 890 |     lr_mult: 2.0
 891 |     decay_mult: 0.0
 892 |   }
 893 |   convolution_param {
 894 |     num_output: 224
 895 |     pad: 1
 896 |     kernel_size: 3
 897 |     weight_filler {
 898 |       type: "xavier"
 899 |       std: 0.03
 900 |     }
 901 |     bias_filler {
 902 |       type: "constant"
 903 |       value: 0.2
 904 |     }
 905 |   }
 906 | }
 907 | layer {
 908 |   name: "inception_4b/relu_3x3"
 909 |   type: "ReLU"
 910 |   bottom: "inception_4b/3x3"
 911 |   top: "inception_4b/3x3"
 912 | }
 913 | layer {
 914 |   name: "inception_4b/5x5_reduce"
 915 |   type: "Convolution"
 916 |   bottom: "inception_4a/output"
 917 |   top: "inception_4b/5x5_reduce"
 918 |   param {
 919 |     lr_mult: 1.0
 920 |     decay_mult: 1.0
 921 |   }
 922 |   param {
 923 |     lr_mult: 2.0
 924 |     decay_mult: 0.0
 925 |   }
 926 |   convolution_param {
 927 |     num_output: 24
 928 |     kernel_size: 1
 929 |     weight_filler {
 930 |       type: "xavier"
 931 |       std: 0.2
 932 |     }
 933 |     bias_filler {
 934 |       type: "constant"
 935 |       value: 0.2
 936 |     }
 937 |   }
 938 | }
 939 | layer {
 940 |   name: "inception_4b/relu_5x5_reduce"
 941 |   type: "ReLU"
 942 |   bottom: "inception_4b/5x5_reduce"
 943 |   top: "inception_4b/5x5_reduce"
 944 | }
 945 | layer {
 946 |   name: "inception_4b/5x5"
 947 |   type: "Convolution"
 948 |   bottom: "inception_4b/5x5_reduce"
 949 |   top: "inception_4b/5x5"
 950 |   param {
 951 |     lr_mult: 1.0
 952 |     decay_mult: 1.0
 953 |   }
 954 |   param {
 955 |     lr_mult: 2.0
 956 |     decay_mult: 0.0
 957 |   }
 958 |   convolution_param {
 959 |     num_output: 64
 960 |     pad: 2
 961 |     kernel_size: 5
 962 |     weight_filler {
 963 |       type: "xavier"
 964 |       std: 0.03
 965 |     }
 966 |     bias_filler {
 967 |       type: "constant"
 968 |       value: 0.2
 969 |     }
 970 |   }
 971 | }
 972 | layer {
 973 |   name: "inception_4b/relu_5x5"
 974 |   type: "ReLU"
 975 |   bottom: "inception_4b/5x5"
 976 |   top: "inception_4b/5x5"
 977 | }
 978 | layer {
 979 |   name: "inception_4b/pool"
 980 |   type: "Pooling"
 981 |   bottom: "inception_4a/output"
 982 |   top: "inception_4b/pool"
 983 |   pooling_param {
 984 |     pool: MAX
 985 |     kernel_size: 3
 986 |     stride: 1
 987 |     pad: 1
 988 |   }
 989 | }
 990 | layer {
 991 |   name: "inception_4b/pool_proj"
 992 |   type: "Convolution"
 993 |   bottom: "inception_4b/pool"
 994 |   top: "inception_4b/pool_proj"
 995 |   param {
 996 |     lr_mult: 1.0
 997 |     decay_mult: 1.0
 998 |   }
 999 |   param {
1000 |     lr_mult: 2.0
1001 |     decay_mult: 0.0
1002 |   }
1003 |   convolution_param {
1004 |     num_output: 64
1005 |     kernel_size: 1
1006 |     weight_filler {
1007 |       type: "xavier"
1008 |       std: 0.1
1009 |     }
1010 |     bias_filler {
1011 |       type: "constant"
1012 |       value: 0.2
1013 |     }
1014 |   }
1015 | }
1016 | layer {
1017 |   name: "inception_4b/relu_pool_proj"
1018 |   type: "ReLU"
1019 |   bottom: "inception_4b/pool_proj"
1020 |   top: "inception_4b/pool_proj"
1021 | }
1022 | layer {
1023 |   name: "inception_4b/output"
1024 |   type: "Concat"
1025 |   bottom: "inception_4b/1x1"
1026 |   bottom: "inception_4b/3x3"
1027 |   bottom: "inception_4b/5x5"
1028 |   bottom: "inception_4b/pool_proj"
1029 |   top: "inception_4b/output"
1030 | }
1031 | layer {
1032 |   name: "inception_4c/1x1"
1033 |   type: "Convolution"
1034 |   bottom: "inception_4b/output"
1035 |   top: "inception_4c/1x1"
1036 |   param {
1037 |     lr_mult: 1.0
1038 |     decay_mult: 1.0
1039 |   }
1040 |   param {
1041 |     lr_mult: 2.0
1042 |     decay_mult: 0.0
1043 |   }
1044 |   convolution_param {
1045 |     num_output: 128
1046 |     kernel_size: 1
1047 |     weight_filler {
1048 |       type: "xavier"
1049 |       std: 0.03
1050 |     }
1051 |     bias_filler {
1052 |       type: "constant"
1053 |       value: 0.2
1054 |     }
1055 |   }
1056 | }
1057 | layer {
1058 |   name: "inception_4c/relu_1x1"
1059 |   type: "ReLU"
1060 |   bottom: "inception_4c/1x1"
1061 |   top: "inception_4c/1x1"
1062 | }
1063 | layer {
1064 |   name: "inception_4c/3x3_reduce"
1065 |   type: "Convolution"
1066 |   bottom: "inception_4b/output"
1067 |   top: "inception_4c/3x3_reduce"
1068 |   param {
1069 |     lr_mult: 1.0
1070 |     decay_mult: 1.0
1071 |   }
1072 |   param {
1073 |     lr_mult: 2.0
1074 |     decay_mult: 0.0
1075 |   }
1076 |   convolution_param {
1077 |     num_output: 128
1078 |     kernel_size: 1
1079 |     weight_filler {
1080 |       type: "xavier"
1081 |       std: 0.09
1082 |     }
1083 |     bias_filler {
1084 |       type: "constant"
1085 |       value: 0.2
1086 |     }
1087 |   }
1088 | }
1089 | layer {
1090 |   name: "inception_4c/relu_3x3_reduce"
1091 |   type: "ReLU"
1092 |   bottom: "inception_4c/3x3_reduce"
1093 |   top: "inception_4c/3x3_reduce"
1094 | }
1095 | layer {
1096 |   name: "inception_4c/3x3"
1097 |   type: "Convolution"
1098 |   bottom: "inception_4c/3x3_reduce"
1099 |   top: "inception_4c/3x3"
1100 |   param {
1101 |     lr_mult: 1.0
1102 |     decay_mult: 1.0
1103 |   }
1104 |   param {
1105 |     lr_mult: 2.0
1106 |     decay_mult: 0.0
1107 |   }
1108 |   convolution_param {
1109 |     num_output: 256
1110 |     pad: 1
1111 |     kernel_size: 3
1112 |     weight_filler {
1113 |       type: "xavier"
1114 |       std: 0.03
1115 |     }
1116 |     bias_filler {
1117 |       type: "constant"
1118 |       value: 0.2
1119 |     }
1120 |   }
1121 | }
1122 | layer {
1123 |   name: "inception_4c/relu_3x3"
1124 |   type: "ReLU"
1125 |   bottom: "inception_4c/3x3"
1126 |   top: "inception_4c/3x3"
1127 | }
1128 | layer {
1129 |   name: "inception_4c/5x5_reduce"
1130 |   type: "Convolution"
1131 |   bottom: "inception_4b/output"
1132 |   top: "inception_4c/5x5_reduce"
1133 |   param {
1134 |     lr_mult: 1.0
1135 |     decay_mult: 1.0
1136 |   }
1137 |   param {
1138 |     lr_mult: 2.0
1139 |     decay_mult: 0.0
1140 |   }
1141 |   convolution_param {
1142 |     num_output: 24
1143 |     kernel_size: 1
1144 |     weight_filler {
1145 |       type: "xavier"
1146 |       std: 0.2
1147 |     }
1148 |     bias_filler {
1149 |       type: "constant"
1150 |       value: 0.2
1151 |     }
1152 |   }
1153 | }
1154 | layer {
1155 |   name: "inception_4c/relu_5x5_reduce"
1156 |   type: "ReLU"
1157 |   bottom: "inception_4c/5x5_reduce"
1158 |   top: "inception_4c/5x5_reduce"
1159 | }
1160 | layer {
1161 |   name: "inception_4c/5x5"
1162 |   type: "Convolution"
1163 |   bottom: "inception_4c/5x5_reduce"
1164 |   top: "inception_4c/5x5"
1165 |   param {
1166 |     lr_mult: 1.0
1167 |     decay_mult: 1.0
1168 |   }
1169 |   param {
1170 |     lr_mult: 2.0
1171 |     decay_mult: 0.0
1172 |   }
1173 |   convolution_param {
1174 |     num_output: 64
1175 |     pad: 2
1176 |     kernel_size: 5
1177 |     weight_filler {
1178 |       type: "xavier"
1179 |       std: 0.03
1180 |     }
1181 |     bias_filler {
1182 |       type: "constant"
1183 |       value: 0.2
1184 |     }
1185 |   }
1186 | }
1187 | layer {
1188 |   name: "inception_4c/relu_5x5"
1189 |   type: "ReLU"
1190 |   bottom: "inception_4c/5x5"
1191 |   top: "inception_4c/5x5"
1192 | }
1193 | layer {
1194 |   name: "inception_4c/pool"
1195 |   type: "Pooling"
1196 |   bottom: "inception_4b/output"
1197 |   top: "inception_4c/pool"
1198 |   pooling_param {
1199 |     pool: MAX
1200 |     kernel_size: 3
1201 |     stride: 1
1202 |     pad: 1
1203 |   }
1204 | }
1205 | layer {
1206 |   name: "inception_4c/pool_proj"
1207 |   type: "Convolution"
1208 |   bottom: "inception_4c/pool"
1209 |   top: "inception_4c/pool_proj"
1210 |   param {
1211 |     lr_mult: 1.0
1212 |     decay_mult: 1.0
1213 |   }
1214 |   param {
1215 |     lr_mult: 2.0
1216 |     decay_mult: 0.0
1217 |   }
1218 |   convolution_param {
1219 |     num_output: 64
1220 |     kernel_size: 1
1221 |     weight_filler {
1222 |       type: "xavier"
1223 |       std: 0.1
1224 |     }
1225 |     bias_filler {
1226 |       type: "constant"
1227 |       value: 0.2
1228 |     }
1229 |   }
1230 | }
1231 | layer {
1232 |   name: "inception_4c/relu_pool_proj"
1233 |   type: "ReLU"
1234 |   bottom: "inception_4c/pool_proj"
1235 |   top: "inception_4c/pool_proj"
1236 | }
1237 | layer {
1238 |   name: "inception_4c/output"
1239 |   type: "Concat"
1240 |   bottom: "inception_4c/1x1"
1241 |   bottom: "inception_4c/3x3"
1242 |   bottom: "inception_4c/5x5"
1243 |   bottom: "inception_4c/pool_proj"
1244 |   top: "inception_4c/output"
1245 | }
1246 | layer {
1247 |   name: "inception_4d/1x1"
1248 |   type: "Convolution"
1249 |   bottom: "inception_4c/output"
1250 |   top: "inception_4d/1x1"
1251 |   param {
1252 |     lr_mult: 1.0
1253 |     decay_mult: 1.0
1254 |   }
1255 |   param {
1256 |     lr_mult: 2.0
1257 |     decay_mult: 0.0
1258 |   }
1259 |   convolution_param {
1260 |     num_output: 112
1261 |     kernel_size: 1
1262 |     weight_filler {
1263 |       type: "xavier"
1264 |       std: 0.1
1265 |     }
1266 |     bias_filler {
1267 |       type: "constant"
1268 |       value: 0.2
1269 |     }
1270 |   }
1271 | }
1272 | layer {
1273 |   name: "inception_4d/relu_1x1"
1274 |   type: "ReLU"
1275 |   bottom: "inception_4d/1x1"
1276 |   top: "inception_4d/1x1"
1277 | }
1278 | layer {
1279 |   name: "inception_4d/3x3_reduce"
1280 |   type: "Convolution"
1281 |   bottom: "inception_4c/output"
1282 |   top: "inception_4d/3x3_reduce"
1283 |   param {
1284 |     lr_mult: 1.0
1285 |     decay_mult: 1.0
1286 |   }
1287 |   param {
1288 |     lr_mult: 2.0
1289 |     decay_mult: 0.0
1290 |   }
1291 |   convolution_param {
1292 |     num_output: 144
1293 |     kernel_size: 1
1294 |     weight_filler {
1295 |       type: "xavier"
1296 |       std: 0.1
1297 |     }
1298 |     bias_filler {
1299 |       type: "constant"
1300 |       value: 0.2
1301 |     }
1302 |   }
1303 | }
1304 | layer {
1305 |   name: "inception_4d/relu_3x3_reduce"
1306 |   type: "ReLU"
1307 |   bottom: "inception_4d/3x3_reduce"
1308 |   top: "inception_4d/3x3_reduce"
1309 | }
1310 | layer {
1311 |   name: "inception_4d/3x3"
1312 |   type: "Convolution"
1313 |   bottom: "inception_4d/3x3_reduce"
1314 |   top: "inception_4d/3x3"
1315 |   param {
1316 |     lr_mult: 1.0
1317 |     decay_mult: 1.0
1318 |   }
1319 |   param {
1320 |     lr_mult: 2.0
1321 |     decay_mult: 0.0
1322 |   }
1323 |   convolution_param {
1324 |     num_output: 288
1325 |     pad: 1
1326 |     kernel_size: 3
1327 |     weight_filler {
1328 |       type: "xavier"
1329 |       std: 0.1
1330 |     }
1331 |     bias_filler {
1332 |       type: "constant"
1333 |       value: 0.2
1334 |     }
1335 |   }
1336 | }
1337 | layer {
1338 |   name: "inception_4d/relu_3x3"
1339 |   type: "ReLU"
1340 |   bottom: "inception_4d/3x3"
1341 |   top: "inception_4d/3x3"
1342 | }
1343 | layer {
1344 |   name: "inception_4d/5x5_reduce"
1345 |   type: "Convolution"
1346 |   bottom: "inception_4c/output"
1347 |   top: "inception_4d/5x5_reduce"
1348 |   param {
1349 |     lr_mult: 1.0
1350 |     decay_mult: 1.0
1351 |   }
1352 |   param {
1353 |     lr_mult: 2.0
1354 |     decay_mult: 0.0
1355 |   }
1356 |   convolution_param {
1357 |     num_output: 32
1358 |     kernel_size: 1
1359 |     weight_filler {
1360 |       type: "xavier"
1361 |       std: 0.1
1362 |     }
1363 |     bias_filler {
1364 |       type: "constant"
1365 |       value: 0.2
1366 |     }
1367 |   }
1368 | }
1369 | layer {
1370 |   name: "inception_4d/relu_5x5_reduce"
1371 |   type: "ReLU"
1372 |   bottom: "inception_4d/5x5_reduce"
1373 |   top: "inception_4d/5x5_reduce"
1374 | }
1375 | layer {
1376 |   name: "inception_4d/5x5"
1377 |   type: "Convolution"
1378 |   bottom: "inception_4d/5x5_reduce"
1379 |   top: "inception_4d/5x5"
1380 |   param {
1381 |     lr_mult: 1.0
1382 |     decay_mult: 1.0
1383 |   }
1384 |   param {
1385 |     lr_mult: 2.0
1386 |     decay_mult: 0.0
1387 |   }
1388 |   convolution_param {
1389 |     num_output: 64
1390 |     pad: 2
1391 |     kernel_size: 5
1392 |     weight_filler {
1393 |       type: "xavier"
1394 |       std: 0.1
1395 |     }
1396 |     bias_filler {
1397 |       type: "constant"
1398 |       value: 0.2
1399 |     }
1400 |   }
1401 | }
1402 | layer {
1403 |   name: "inception_4d/relu_5x5"
1404 |   type: "ReLU"
1405 |   bottom: "inception_4d/5x5"
1406 |   top: "inception_4d/5x5"
1407 | }
1408 | layer {
1409 |   name: "inception_4d/pool"
1410 |   type: "Pooling"
1411 |   bottom: "inception_4c/output"
1412 |   top: "inception_4d/pool"
1413 |   pooling_param {
1414 |     pool: MAX
1415 |     kernel_size: 3
1416 |     stride: 1
1417 |     pad: 1
1418 |   }
1419 | }
1420 | layer {
1421 |   name: "inception_4d/pool_proj"
1422 |   type: "Convolution"
1423 |   bottom: "inception_4d/pool"
1424 |   top: "inception_4d/pool_proj"
1425 |   param {
1426 |     lr_mult: 1.0
1427 |     decay_mult: 1.0
1428 |   }
1429 |   param {
1430 |     lr_mult: 2.0
1431 |     decay_mult: 0.0
1432 |   }
1433 |   convolution_param {
1434 |     num_output: 64
1435 |     kernel_size: 1
1436 |     weight_filler {
1437 |       type: "xavier"
1438 |       std: 0.1
1439 |     }
1440 |     bias_filler {
1441 |       type: "constant"
1442 |       value: 0.2
1443 |     }
1444 |   }
1445 | }
1446 | layer {
1447 |   name: "inception_4d/relu_pool_proj"
1448 |   type: "ReLU"
1449 |   bottom: "inception_4d/pool_proj"
1450 |   top: "inception_4d/pool_proj"
1451 | }
1452 | layer {
1453 |   name: "inception_4d/output"
1454 |   type: "Concat"
1455 |   bottom: "inception_4d/1x1"
1456 |   bottom: "inception_4d/3x3"
1457 |   bottom: "inception_4d/5x5"
1458 |   bottom: "inception_4d/pool_proj"
1459 |   top: "inception_4d/output"
1460 | }
1461 | layer {
1462 |   name: "inception_4e/1x1"
1463 |   type: "Convolution"
1464 |   bottom: "inception_4d/output"
1465 |   top: "inception_4e/1x1"
1466 |   param {
1467 |     lr_mult: 1.0
1468 |     decay_mult: 1.0
1469 |   }
1470 |   param {
1471 |     lr_mult: 2.0
1472 |     decay_mult: 0.0
1473 |   }
1474 |   convolution_param {
1475 |     num_output: 256
1476 |     kernel_size: 1
1477 |     weight_filler {
1478 |       type: "xavier"
1479 |       std: 0.03
1480 |     }
1481 |     bias_filler {
1482 |       type: "constant"
1483 |       value: 0.2
1484 |     }
1485 |   }
1486 | }
1487 | layer {
1488 |   name: "inception_4e/relu_1x1"
1489 |   type: "ReLU"
1490 |   bottom: "inception_4e/1x1"
1491 |   top: "inception_4e/1x1"
1492 | }
1493 | layer {
1494 |   name: "inception_4e/3x3_reduce"
1495 |   type: "Convolution"
1496 |   bottom: "inception_4d/output"
1497 |   top: "inception_4e/3x3_reduce"
1498 |   param {
1499 |     lr_mult: 1.0
1500 |     decay_mult: 1.0
1501 |   }
1502 |   param {
1503 |     lr_mult: 2.0
1504 |     decay_mult: 0.0
1505 |   }
1506 |   convolution_param {
1507 |     num_output: 160
1508 |     kernel_size: 1
1509 |     weight_filler {
1510 |       type: "xavier"
1511 |       std: 0.09
1512 |     }
1513 |     bias_filler {
1514 |       type: "constant"
1515 |       value: 0.2
1516 |     }
1517 |   }
1518 | }
1519 | layer {
1520 |   name: "inception_4e/relu_3x3_reduce"
1521 |   type: "ReLU"
1522 |   bottom: "inception_4e/3x3_reduce"
1523 |   top: "inception_4e/3x3_reduce"
1524 | }
1525 | layer {
1526 |   name: "inception_4e/3x3"
1527 |   type: "Convolution"
1528 |   bottom: "inception_4e/3x3_reduce"
1529 |   top: "inception_4e/3x3"
1530 |   param {
1531 |     lr_mult: 1.0
1532 |     decay_mult: 1.0
1533 |   }
1534 |   param {
1535 |     lr_mult: 2.0
1536 |     decay_mult: 0.0
1537 |   }
1538 |   convolution_param {
1539 |     num_output: 320
1540 |     pad: 1
1541 |     kernel_size: 3
1542 |     weight_filler {
1543 |       type: "xavier"
1544 |       std: 0.03
1545 |     }
1546 |     bias_filler {
1547 |       type: "constant"
1548 |       value: 0.2
1549 |     }
1550 |   }
1551 | }
1552 | layer {
1553 |   name: "inception_4e/relu_3x3"
1554 |   type: "ReLU"
1555 |   bottom: "inception_4e/3x3"
1556 |   top: "inception_4e/3x3"
1557 | }
1558 | layer {
1559 |   name: "inception_4e/5x5_reduce"
1560 |   type: "Convolution"
1561 |   bottom: "inception_4d/output"
1562 |   top: "inception_4e/5x5_reduce"
1563 |   param {
1564 |     lr_mult: 1.0
1565 |     decay_mult: 1.0
1566 |   }
1567 |   param {
1568 |     lr_mult: 2.0
1569 |     decay_mult: 0.0
1570 |   }
1571 |   convolution_param {
1572 |     num_output: 32
1573 |     kernel_size: 1
1574 |     weight_filler {
1575 |       type: "xavier"
1576 |       std: 0.2
1577 |     }
1578 |     bias_filler {
1579 |       type: "constant"
1580 |       value: 0.2
1581 |     }
1582 |   }
1583 | }
1584 | layer {
1585 |   name: "inception_4e/relu_5x5_reduce"
1586 |   type: "ReLU"
1587 |   bottom: "inception_4e/5x5_reduce"
1588 |   top: "inception_4e/5x5_reduce"
1589 | }
1590 | layer {
1591 |   name: "inception_4e/5x5"
1592 |   type: "Convolution"
1593 |   bottom: "inception_4e/5x5_reduce"
1594 |   top: "inception_4e/5x5"
1595 |   param {
1596 |     lr_mult: 1.0
1597 |     decay_mult: 1.0
1598 |   }
1599 |   param {
1600 |     lr_mult: 2.0
1601 |     decay_mult: 0.0
1602 |   }
1603 |   convolution_param {
1604 |     num_output: 128
1605 |     pad: 2
1606 |     kernel_size: 5
1607 |     weight_filler {
1608 |       type: "xavier"
1609 |       std: 0.03
1610 |     }
1611 |     bias_filler {
1612 |       type: "constant"
1613 |       value: 0.2
1614 |     }
1615 |   }
1616 | }
1617 | layer {
1618 |   name: "inception_4e/relu_5x5"
1619 |   type: "ReLU"
1620 |   bottom: "inception_4e/5x5"
1621 |   top: "inception_4e/5x5"
1622 | }
1623 | layer {
1624 |   name: "inception_4e/pool"
1625 |   type: "Pooling"
1626 |   bottom: "inception_4d/output"
1627 |   top: "inception_4e/pool"
1628 |   pooling_param {
1629 |     pool: MAX
1630 |     kernel_size: 3
1631 |     stride: 1
1632 |     pad: 1
1633 |   }
1634 | }
1635 | layer {
1636 |   name: "inception_4e/pool_proj"
1637 |   type: "Convolution"
1638 |   bottom: "inception_4e/pool"
1639 |   top: "inception_4e/pool_proj"
1640 |   param {
1641 |     lr_mult: 1.0
1642 |     decay_mult: 1.0
1643 |   }
1644 |   param {
1645 |     lr_mult: 2.0
1646 |     decay_mult: 0.0
1647 |   }
1648 |   convolution_param {
1649 |     num_output: 128
1650 |     kernel_size: 1
1651 |     weight_filler {
1652 |       type: "xavier"
1653 |       std: 0.1
1654 |     }
1655 |     bias_filler {
1656 |       type: "constant"
1657 |       value: 0.2
1658 |     }
1659 |   }
1660 | }
1661 | layer {
1662 |   name: "inception_4e/relu_pool_proj"
1663 |   type: "ReLU"
1664 |   bottom: "inception_4e/pool_proj"
1665 |   top: "inception_4e/pool_proj"
1666 | }
1667 | layer {
1668 |   name: "inception_4e/output"
1669 |   type: "Concat"
1670 |   bottom: "inception_4e/1x1"
1671 |   bottom: "inception_4e/3x3"
1672 |   bottom: "inception_4e/5x5"
1673 |   bottom: "inception_4e/pool_proj"
1674 |   top: "inception_4e/output"
1675 | }
1676 | layer {
1677 |   name: "inception_5a/1x1"
1678 |   type: "Convolution"
1679 |   bottom: "inception_4e/output"
1680 |   top: "inception_5a/1x1"
1681 |   param {
1682 |     lr_mult: 1.0
1683 |     decay_mult: 1.0
1684 |   }
1685 |   param {
1686 |     lr_mult: 2.0
1687 |     decay_mult: 0.0
1688 |   }
1689 |   convolution_param {
1690 |     num_output: 256
1691 |     kernel_size: 1
1692 |     weight_filler {
1693 |       type: "xavier"
1694 |       std: 0.03
1695 |     }
1696 |     bias_filler {
1697 |       type: "constant"
1698 |       value: 0.2
1699 |     }
1700 |   }
1701 | }
1702 | layer {
1703 |   name: "inception_5a/relu_1x1"
1704 |   type: "ReLU"
1705 |   bottom: "inception_5a/1x1"
1706 |   top: "inception_5a/1x1"
1707 | }
1708 | layer {
1709 |   name: "inception_5a/3x3_reduce"
1710 |   type: "Convolution"
1711 |   bottom: "inception_4e/output"
1712 |   top: "inception_5a/3x3_reduce"
1713 |   param {
1714 |     lr_mult: 1.0
1715 |     decay_mult: 1.0
1716 |   }
1717 |   param {
1718 |     lr_mult: 2.0
1719 |     decay_mult: 0.0
1720 |   }
1721 |   convolution_param {
1722 |     num_output: 160
1723 |     kernel_size: 1
1724 |     weight_filler {
1725 |       type: "xavier"
1726 |       std: 0.09
1727 |     }
1728 |     bias_filler {
1729 |       type: "constant"
1730 |       value: 0.2
1731 |     }
1732 |   }
1733 | }
1734 | layer {
1735 |   name: "inception_5a/relu_3x3_reduce"
1736 |   type: "ReLU"
1737 |   bottom: "inception_5a/3x3_reduce"
1738 |   top: "inception_5a/3x3_reduce"
1739 | }
1740 | layer {
1741 |   name: "inception_5a/3x3"
1742 |   type: "Convolution"
1743 |   bottom: "inception_5a/3x3_reduce"
1744 |   top: "inception_5a/3x3"
1745 |   param {
1746 |     lr_mult: 1.0
1747 |     decay_mult: 1.0
1748 |   }
1749 |   param {
1750 |     lr_mult: 2.0
1751 |     decay_mult: 0.0
1752 |   }
1753 |   convolution_param {
1754 |     num_output: 320
1755 |     pad: 1
1756 |     kernel_size: 3
1757 |     weight_filler {
1758 |       type: "xavier"
1759 |       std: 0.03
1760 |     }
1761 |     bias_filler {
1762 |       type: "constant"
1763 |       value: 0.2
1764 |     }
1765 |   }
1766 | }
1767 | layer {
1768 |   name: "inception_5a/relu_3x3"
1769 |   type: "ReLU"
1770 |   bottom: "inception_5a/3x3"
1771 |   top: "inception_5a/3x3"
1772 | }
1773 | layer {
1774 |   name: "inception_5a/5x5_reduce"
1775 |   type: "Convolution"
1776 |   bottom: "inception_4e/output"
1777 |   top: "inception_5a/5x5_reduce"
1778 |   param {
1779 |     lr_mult: 1.0
1780 |     decay_mult: 1.0
1781 |   }
1782 |   param {
1783 |     lr_mult: 2.0
1784 |     decay_mult: 0.0
1785 |   }
1786 |   convolution_param {
1787 |     num_output: 32
1788 |     kernel_size: 1
1789 |     weight_filler {
1790 |       type: "xavier"
1791 |       std: 0.2
1792 |     }
1793 |     bias_filler {
1794 |       type: "constant"
1795 |       value: 0.2
1796 |     }
1797 |   }
1798 | }
1799 | layer {
1800 |   name: "inception_5a/relu_5x5_reduce"
1801 |   type: "ReLU"
1802 |   bottom: "inception_5a/5x5_reduce"
1803 |   top: "inception_5a/5x5_reduce"
1804 | }
1805 | layer {
1806 |   name: "inception_5a/5x5"
1807 |   type: "Convolution"
1808 |   bottom: "inception_5a/5x5_reduce"
1809 |   top: "inception_5a/5x5"
1810 |   param {
1811 |     lr_mult: 1.0
1812 |     decay_mult: 1.0
1813 |   }
1814 |   param {
1815 |     lr_mult: 2.0
1816 |     decay_mult: 0.0
1817 |   }
1818 |   convolution_param {
1819 |     num_output: 128
1820 |     pad: 2
1821 |     kernel_size: 5
1822 |     weight_filler {
1823 |       type: "xavier"
1824 |       std: 0.03
1825 |     }
1826 |     bias_filler {
1827 |       type: "constant"
1828 |       value: 0.2
1829 |     }
1830 |   }
1831 | }
1832 | layer {
1833 |   name: "inception_5a/relu_5x5"
1834 |   type: "ReLU"
1835 |   bottom: "inception_5a/5x5"
1836 |   top: "inception_5a/5x5"
1837 | }
1838 | layer {
1839 |   name: "inception_5a/pool"
1840 |   type: "Pooling"
1841 |   bottom: "inception_4e/output"
1842 |   top: "inception_5a/pool"
1843 |   pooling_param {
1844 |     pool: MAX
1845 |     kernel_size: 3
1846 |     stride: 1
1847 |     pad: 1
1848 |   }
1849 | }
1850 | layer {
1851 |   name: "inception_5a/pool_proj"
1852 |   type: "Convolution"
1853 |   bottom: "inception_5a/pool"
1854 |   top: "inception_5a/pool_proj"
1855 |   param {
1856 |     lr_mult: 1.0
1857 |     decay_mult: 1.0
1858 |   }
1859 |   param {
1860 |     lr_mult: 2.0
1861 |     decay_mult: 0.0
1862 |   }
1863 |   convolution_param {
1864 |     num_output: 128
1865 |     kernel_size: 1
1866 |     weight_filler {
1867 |       type: "xavier"
1868 |       std: 0.1
1869 |     }
1870 |     bias_filler {
1871 |       type: "constant"
1872 |       value: 0.2
1873 |     }
1874 |   }
1875 | }
1876 | layer {
1877 |   name: "inception_5a/relu_pool_proj"
1878 |   type: "ReLU"
1879 |   bottom: "inception_5a/pool_proj"
1880 |   top: "inception_5a/pool_proj"
1881 | }
1882 | layer {
1883 |   name: "inception_5a/output"
1884 |   type: "Concat"
1885 |   bottom: "inception_5a/1x1"
1886 |   bottom: "inception_5a/3x3"
1887 |   bottom: "inception_5a/5x5"
1888 |   bottom: "inception_5a/pool_proj"
1889 |   top: "inception_5a/output"
1890 | }
1891 | layer {
1892 |   name: "inception_5b/1x1"
1893 |   type: "Convolution"
1894 |   bottom: "inception_5a/output"
1895 |   top: "inception_5b/1x1"
1896 |   param {
1897 |     lr_mult: 1.0
1898 |     decay_mult: 1.0
1899 |   }
1900 |   param {
1901 |     lr_mult: 2.0
1902 |     decay_mult: 0.0
1903 |   }
1904 |   convolution_param {
1905 |     num_output: 720
1906 |     kernel_size: 1
1907 |     weight_filler {
1908 |       type: "xavier"
1909 |       std: 0.1
1910 |     }
1911 |     bias_filler {
1912 |       type: "constant"
1913 |       value: 0.2
1914 |     }
1915 |   }
1916 | }
1917 | layer {
1918 |   name: "inception_5b/relu_1x1"
1919 |   type: "ReLU"
1920 |   bottom: "inception_5b/1x1"
1921 |   top: "inception_5b/1x1"
1922 | }
1923 | layer {
1924 |   name: "inception_5b/3x3_reduce"
1925 |   type: "Convolution"
1926 |   bottom: "inception_5a/output"
1927 |   top: "inception_5b/3x3_reduce"
1928 |   param {
1929 |     lr_mult: 1.0
1930 |     decay_mult: 1.0
1931 |   }
1932 |   param {
1933 |     lr_mult: 1.0
1934 |     decay_mult: 0.0
1935 |   }
1936 |   convolution_param {
1937 |     num_output: 192
1938 |     kernel_size: 1
1939 |     weight_filler {
1940 |       type: "xavier"
1941 |       std: 0.1
1942 |     }
1943 |     bias_filler {
1944 |       type: "constant"
1945 |       value: 0.2
1946 |     }
1947 |   }
1948 | }
1949 | layer {
1950 |   name: "inception_5b/relu_3x3_reduce"
1951 |   type: "ReLU"
1952 |   bottom: "inception_5b/3x3_reduce"
1953 |   top: "inception_5b/3x3_reduce"
1954 | }
1955 | layer {
1956 |   name: "inception_5b/3x3"
1957 |   type: "Convolution"
1958 |   bottom: "inception_5b/3x3_reduce"
1959 |   top: "inception_5b/3x3"
1960 |   param {
1961 |     lr_mult: 1.0
1962 |     decay_mult: 1.0
1963 |   }
1964 |   param {
1965 |     lr_mult: 2.0
1966 |     decay_mult: 0.0
1967 |   }
1968 |   convolution_param {
1969 |     num_output: 720
1970 |     pad: 1
1971 |     kernel_size: 3
1972 |     weight_filler {
1973 |       type: "xavier"
1974 |       std: 0.1
1975 |     }
1976 |     bias_filler {
1977 |       type: "constant"
1978 |       value: 0.2
1979 |     }
1980 |   }
1981 | }
1982 | layer {
1983 |   name: "inception_5b/relu_3x3"
1984 |   type: "ReLU"
1985 |   bottom: "inception_5b/3x3"
1986 |   top: "inception_5b/3x3"
1987 | }
1988 | layer {
1989 |   name: "inception_5b/5x5_reduce"
1990 |   type: "Convolution"
1991 |   bottom: "inception_5a/output"
1992 |   top: "inception_5b/5x5_reduce"
1993 |   param {
1994 |     lr_mult: 1.0
1995 |     decay_mult: 1.0
1996 |   }
1997 |   param {
1998 |     lr_mult: 2.0
1999 |     decay_mult: 0.0
2000 |   }
2001 |   convolution_param {
2002 |     num_output: 48
2003 |     kernel_size: 1
2004 |     weight_filler {
2005 |       type: "xavier"
2006 |       std: 0.1
2007 |     }
2008 |     bias_filler {
2009 |       type: "constant"
2010 |       value: 0.2
2011 |     }
2012 |   }
2013 | }
2014 | layer {
2015 |   name: "inception_5b/relu_5x5_reduce"
2016 |   type: "ReLU"
2017 |   bottom: "inception_5b/5x5_reduce"
2018 |   top: "inception_5b/5x5_reduce"
2019 | }
2020 | layer {
2021 |   name: "inception_5b/5x5"
2022 |   type: "Convolution"
2023 |   bottom: "inception_5b/5x5_reduce"
2024 |   top: "inception_5b/5x5"
2025 |   param {
2026 |     lr_mult: 1.0
2027 |     decay_mult: 1.0
2028 |   }
2029 |   param {
2030 |     lr_mult: 2.0
2031 |     decay_mult: 0.0
2032 |   }
2033 |   convolution_param {
2034 |     num_output: 128
2035 |     pad: 2
2036 |     kernel_size: 5
2037 |     weight_filler {
2038 |       type: "xavier"
2039 |       std: 0.1
2040 |     }
2041 |     bias_filler {
2042 |       type: "constant"
2043 |       value: 0.2
2044 |     }
2045 |   }
2046 | }
2047 | layer {
2048 |   name: "inception_5b/relu_5x5"
2049 |   type: "ReLU"
2050 |   bottom: "inception_5b/5x5"
2051 |   top: "inception_5b/5x5"
2052 | }
2053 | layer {
2054 |   name: "inception_5b/pool"
2055 |   type: "Pooling"
2056 |   bottom: "inception_5a/output"
2057 |   top: "inception_5b/pool"
2058 |   pooling_param {
2059 |     pool: MAX
2060 |     kernel_size: 3
2061 |     stride: 1
2062 |     pad: 1
2063 |   }
2064 | }
2065 | layer {
2066 |   name: "inception_5b/pool_proj"
2067 |   type: "Convolution"
2068 |   bottom: "inception_5b/pool"
2069 |   top: "inception_5b/pool_proj"
2070 |   param {
2071 |     lr_mult: 1.0
2072 |     decay_mult: 1.0
2073 |   }
2074 |   param {
2075 |     lr_mult: 2.0
2076 |     decay_mult: 0.0
2077 |   }
2078 |   convolution_param {
2079 |     num_output: 128
2080 |     kernel_size: 1
2081 |     weight_filler {
2082 |       type: "xavier"
2083 |       std: 0.1
2084 |     }
2085 |     bias_filler {
2086 |       type: "constant"
2087 |       value: 0.2
2088 |     }
2089 |   }
2090 | }
2091 | layer {
2092 |   name: "inception_5b/relu_pool_proj"
2093 |   type: "ReLU"
2094 |   bottom: "inception_5b/pool_proj"
2095 |   top: "inception_5b/pool_proj"
2096 | }
2097 | layer {
2098 |   name: "inception_5b/output"
2099 |   type: "Concat"
2100 |   bottom: "inception_5b/1x1"
2101 |   bottom: "inception_5b/3x3"
2102 |   bottom: "inception_5b/5x5"
2103 |   bottom: "inception_5b/pool_proj"
2104 |   top: "inception_5b/output"
2105 | }
2106 | layer {
2107 |   name: "pool5/drop_s1"
2108 |   type: "Dropout"
2109 |   bottom: "inception_5b/output"
2110 |   top: "pool5/drop_s1"
2111 |   dropout_param {
2112 |     dropout_ratio: 0.4
2113 |   }
2114 | }
2115 | layer {
2116 |   name: "cvg/classifier"
2117 |   type: "Convolution"
2118 |   bottom: "pool5/drop_s1"
2119 |   top: "cvg/classifier"
2120 |   param {
2121 |     lr_mult: 1.0
2122 |     decay_mult: 1.0
2123 |   }
2124 |   param {
2125 |     lr_mult: 2.0
2126 |     decay_mult: 0.0
2127 |   }
2128 |   convolution_param {
2129 |     num_output: 1
2130 |     kernel_size: 1
2131 |     weight_filler {
2132 |       type: "xavier"
2133 |       std: 0.03
2134 |     }
2135 |     bias_filler {
2136 |       type: "constant"
2137 |       value: 0.0
2138 |     }
2139 |   }
2140 | }
2141 | layer {
2142 |   name: "coverage/sig"
2143 |   type: "Sigmoid"
2144 |   bottom: "cvg/classifier"
2145 |   top: "coverage"
2146 | }
2147 | layer {
2148 |   name: "bbox/regressor"
2149 |   type: "Convolution"
2150 |   bottom: "pool5/drop_s1"
2151 |   top: "bboxes"
2152 |   param {
2153 |     lr_mult: 1.0
2154 |     decay_mult: 1.0
2155 |   }
2156 |   param {
2157 |     lr_mult: 2.0
2158 |     decay_mult: 0.0
2159 |   }
2160 |   convolution_param {
2161 |     num_output: 4
2162 |     kernel_size: 1
2163 |     weight_filler {
2164 |       type: "xavier"
2165 |       std: 0.03
2166 |     }
2167 |     bias_filler {
2168 |       type: "constant"
2169 |       value: 0.0
2170 |     }
2171 |   }
2172 | }
2173 | layer {
2174 |   name: "cluster"
2175 |   type: "Python"
2176 |   bottom: "coverage"
2177 |   bottom: "bboxes"
2178 |   top: "bbox-list"
2179 |   python_param {
2180 |     module: "caffe.layers.detectnet.clustering"
2181 |     layer: "ClusterDetections"
2182 |     param_str: "1280, 720, 16, 0.6, 3, 0.02, 22"
2183 |   }
2184 | }
2185 | 


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/process-video-rt.py:
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 1 | import numpy as np
 2 | import cv2
 3 | import caffe
 4 | 
 5 | 
 6 | MODEL_FILE = '/Users/kris/Downloads/fedex_mil_model_epoch_70.0/deploy.prototxt'
 7 | PRETRAINED = '/Users/kris/Downloads/fedex_mil_model_epoch_70.0/snapshot_iter_28000.caffemodel'
 8 | IMAGE_FILE = '/Users/kris/Downloads/football2-resized2/resized/195583_01_01.png'
 9 | 
10 | cap = cv2.VideoCapture(0)
11 | cap = cv2.VideoCapture('/Users/kris/Downloads/output.mp4')
12 | 
13 | caffe.set_mode_cpu()
14 | 
15 | net = caffe.Net(MODEL_FILE, PRETRAINED, caffe.TEST);
16 | 
17 | img = cv2.imread(IMAGE_FILE);
18 | 
19 | # print(res)
20 | 
21 | while(True):
22 |     # Capture frame-by-frame
23 |     ret, frame = cap.read()
24 | 
25 |     # Our operations on the frame come here
26 |     gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
27 | 
28 |     # Display the resulting frame
29 |     cv2.imshow('frame',gray)
30 |     if cv2.waitKey(1) & 0xFF == ord('q'):
31 |         break
32 | 
33 | # When everything done, release the capture
34 | cap.release()
35 | cv2.destroyAllWindows()
36 | 


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/resize-img.py:
--------------------------------------------------------------------------------
 1 | from PIL import Image
 2 | import os, errno
 3 | import image_slicer
 4 | from shutil import move
 5 | 
 6 | new_width, new_height = 1280, 720
 7 | number_of_slices = 4
 8 | sourceDirectory='/Users/kris/Downloads/football1'
 9 | destinationDirectory='/Users/kris/Downloads/football2-resized2'
10 | 
11 | if not os.path.exists(destinationDirectory):
12 |     os.makedirs(destinationDirectory + '/slices')
13 |     os.makedirs(destinationDirectory + '/resized')
14 | 
15 | # Slice images one by one and copy them to the new directory
16 | for filename in os.listdir(sourceDirectory):
17 |     if filename.endswith(".jpg"):
18 |         print("Slicing: {0}".format(os.path.join(sourceDirectory, filename)))
19 |         pre, ext = os.path.splitext(filename)
20 |         tiles = image_slicer.slice(os.path.join(sourceDirectory, filename), number_of_slices, save=False)
21 |         image_slicer.save_tiles(tiles, directory=destinationDirectory + '/slices', prefix=pre)
22 | 
23 | 
24 | for filename in os.listdir(destinationDirectory + '/slices'):
25 |     if filename.endswith(".png"):
26 |         print("Resizing: {0}".format(os.path.join(destinationDirectory + '/slices', filename)))
27 |         img = Image.open(os.path.join(destinationDirectory + '/slices', filename))
28 |         img = img.resize((new_width, new_height), Image.ANTIALIAS)
29 |         img.save(os.path.join(destinationDirectory + '/resized', filename))
30 |         continue
31 |     else:
32 |         continue
33 | 
34 | print('Done!')
35 | 


--------------------------------------------------------------------------------
/video-to-frames.py:
--------------------------------------------------------------------------------
 1 | import cv2
 2 | import math
 3 | 
 4 | 
 5 | print(cv2.__version__)
 6 | vidcap = cv2.VideoCapture('UEL.2017.09.14.Hoffenheim.vs.Braga.EN.720p-FS.mp4')
 7 | frameRate = vidcap.get(5)
 8 | print 'Frame rate: ', frameRate
 9 | success,image = vidcap.read()
10 | count = 0
11 | success = True
12 | while success:
13 |   success,image = vidcap.read()
14 |   frameId = vidcap.get(1) #current frame number
15 |   if (frameId % math.floor(frameRate) == 0):
16 |       print 'Read a new frame: ', success
17 |       cv2.imwrite("/Users/kris/Downloads/football1/%d.jpg" % count, image)     # save frame as JPEG file
18 |   count += 1
19 | 
20 | vidcap.release()
21 | print "Done!"
22 | 


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