1[net] 2# Testing 3#batch=1 4#subdivisions=1 5# Training 6batch=64 7subdivisions=8 8width=416 9height=416 10channels=3 11momentum=0.9 12decay=0.0005 13angle=0 14saturation = 1.5 15exposure = 1.5 16hue=.1 17 18learning_rate=0.001 19burn_in=1000 20max_batches = 500200 21policy=steps 22steps=400000,450000 23scales=.1,.1 24 25### CONV1 - 1 (1) 26# conv1 27[convolutional] 28filters=32 29size=3 30pad=1 31stride=2 32batch_normalize=1 33activation=swish 34 35 36### CONV2 - MBConv1 - 1 (1) 37# conv2_1_expand 38[convolutional] 39filters=32 40size=1 41stride=1 42pad=0 43batch_normalize=1 44activation=swish 45 46# conv2_1_dwise 47[convolutional] 48groups=32 49filters=32 50size=3 51stride=1 52pad=1 53batch_normalize=1 54activation=swish 55 56 57#squeeze-n-excitation 58[avgpool] 59 60# squeeze ratio r=4 (recommended r=16) 61[convolutional] 62filters=8 63size=1 64stride=1 65activation=swish 66 67# excitation 68[convolutional] 69filters=32 70size=1 71stride=1 72activation=logistic 73 74# multiply channels 75[scale_channels] 76from=-4 77 78 79# conv2_1_linear 80[convolutional] 81filters=16 82size=1 83stride=1 84pad=0 85batch_normalize=1 86activation=linear 87 88 89 90### CONV3 - MBConv6 - 1 (2) 91# conv2_2_expand 92[convolutional] 93filters=96 94size=1 95stride=1 96pad=0 97batch_normalize=1 98activation=swish 99 100# conv2_2_dwise 101[convolutional] 102groups=96 103filters=96 104size=3 105pad=1 106stride=2 107batch_normalize=1 108activation=swish 109 110 111#squeeze-n-excitation 112[avgpool] 113 114# squeeze ratio r=8 (recommended r=16) 115[convolutional] 116filters=16 117size=1 118stride=1 119activation=swish 120 121# excitation 122[convolutional] 123filters=96 124size=1 125stride=1 126activation=logistic 127 128# multiply channels 129[scale_channels] 130from=-4 131 132 133# conv2_2_linear 134[convolutional] 135filters=24 136size=1 137stride=1 138pad=0 139batch_normalize=1 140activation=linear 141 142 143### CONV3 - MBConv6 - 2 (2) 144# conv3_1_expand 145[convolutional] 146filters=144 147size=1 148stride=1 149pad=0 150batch_normalize=1 151activation=swish 152 153# conv3_1_dwise 154[convolutional] 155groups=144 156filters=144 157size=3 158stride=1 159pad=1 160batch_normalize=1 161activation=swish 162 163 164#squeeze-n-excitation 165[avgpool] 166 167# squeeze ratio r=16 (recommended r=16) 168[convolutional] 169filters=8 170size=1 171stride=1 172activation=swish 173 174# excitation 175[convolutional] 176filters=144 177size=1 178stride=1 179activation=logistic 180 181# multiply channels 182[scale_channels] 183from=-4 184 185 186# conv3_1_linear 187[convolutional] 188filters=24 189size=1 190stride=1 191pad=0 192batch_normalize=1 193activation=linear 194 195 196 197### CONV4 - MBConv6 - 1 (2) 198# dropout only before residual connection 199[dropout] 200probability=.0 201 202# block_3_1 203[shortcut] 204from=-9 205activation=linear 206 207# conv_3_2_expand 208[convolutional] 209filters=144 210size=1 211stride=1 212pad=0 213batch_normalize=1 214activation=swish 215 216# conv_3_2_dwise 217[convolutional] 218groups=144 219filters=144 220size=5 221pad=1 222stride=2 223batch_normalize=1 224activation=swish 225 226 227#squeeze-n-excitation 228[avgpool] 229 230# squeeze ratio r=16 (recommended r=16) 231[convolutional] 232filters=8 233size=1 234stride=1 235activation=swish 236 237# excitation 238[convolutional] 239filters=144 240size=1 241stride=1 242activation=logistic 243 244# multiply channels 245[scale_channels] 246from=-4 247 248 249# conv_3_2_linear 250[convolutional] 251filters=40 252size=1 253stride=1 254pad=0 255batch_normalize=1 256activation=linear 257 258 259### CONV4 - MBConv6 - 2 (2) 260# conv_4_1_expand 261[convolutional] 262filters=192 263size=1 264stride=1 265pad=0 266batch_normalize=1 267activation=swish 268 269# conv_4_1_dwise 270[convolutional] 271groups=192 272filters=192 273size=5 274stride=1 275pad=1 276batch_normalize=1 277activation=swish 278 279 280#squeeze-n-excitation 281[avgpool] 282 283# squeeze ratio r=16 (recommended r=16) 284[convolutional] 285filters=16 286size=1 287stride=1 288activation=swish 289 290# excitation 291[convolutional] 292filters=192 293size=1 294stride=1 295activation=logistic 296 297# multiply channels 298[scale_channels] 299from=-4 300 301 302# conv_4_1_linear 303[convolutional] 304filters=40 305size=1 306stride=1 307pad=0 308batch_normalize=1 309activation=linear 310 311 312 313 314### CONV5 - MBConv6 - 1 (3) 315# dropout only before residual connection 316[dropout] 317probability=.0 318 319# block_4_2 320[shortcut] 321from=-9 322activation=linear 323 324# conv_4_3_expand 325[convolutional] 326filters=192 327size=1 328stride=1 329pad=0 330batch_normalize=1 331activation=swish 332 333# conv_4_3_dwise 334[convolutional] 335groups=192 336filters=192 337size=3 338stride=1 339pad=1 340batch_normalize=1 341activation=swish 342 343 344#squeeze-n-excitation 345[avgpool] 346 347# squeeze ratio r=16 (recommended r=16) 348[convolutional] 349filters=16 350size=1 351stride=1 352activation=swish 353 354# excitation 355[convolutional] 356filters=192 357size=1 358stride=1 359activation=logistic 360 361# multiply channels 362[scale_channels] 363from=-4 364 365 366# conv_4_3_linear 367[convolutional] 368filters=80 369size=1 370stride=1 371pad=0 372batch_normalize=1 373activation=linear 374 375 376### CONV5 - MBConv6 - 2 (3) 377# conv_4_4_expand 378[convolutional] 379filters=384 380size=1 381stride=1 382pad=0 383batch_normalize=1 384activation=swish 385 386# conv_4_4_dwise 387[convolutional] 388groups=384 389filters=384 390size=3 391stride=1 392pad=1 393batch_normalize=1 394activation=swish 395 396 397#squeeze-n-excitation 398[avgpool] 399 400# squeeze ratio r=16 (recommended r=16) 401[convolutional] 402filters=24 403size=1 404stride=1 405activation=swish 406 407# excitation 408[convolutional] 409filters=384 410size=1 411stride=1 412activation=logistic 413 414# multiply channels 415[scale_channels] 416from=-4 417 418 419# conv_4_4_linear 420[convolutional] 421filters=80 422size=1 423stride=1 424pad=0 425batch_normalize=1 426activation=linear 427 428 429### CONV5 - MBConv6 - 3 (3) 430# dropout only before residual connection 431[dropout] 432probability=.0 433 434# block_4_4 435[shortcut] 436from=-9 437activation=linear 438 439# conv_4_5_expand 440[convolutional] 441filters=384 442size=1 443stride=1 444pad=0 445batch_normalize=1 446activation=swish 447 448# conv_4_5_dwise 449[convolutional] 450groups=384 451filters=384 452size=3 453stride=1 454pad=1 455batch_normalize=1 456activation=swish 457 458 459#squeeze-n-excitation 460[avgpool] 461 462# squeeze ratio r=16 (recommended r=16) 463[convolutional] 464filters=24 465size=1 466stride=1 467activation=swish 468 469# excitation 470[convolutional] 471filters=384 472size=1 473stride=1 474activation=logistic 475 476# multiply channels 477[scale_channels] 478from=-4 479 480 481# conv_4_5_linear 482[convolutional] 483filters=80 484size=1 485stride=1 486pad=0 487batch_normalize=1 488activation=linear 489 490 491 492### CONV6 - MBConv6 - 1 (3) 493# dropout only before residual connection 494[dropout] 495probability=.0 496 497# block_4_6 498[shortcut] 499from=-9 500activation=linear 501 502# conv_4_7_expand 503[convolutional] 504filters=384 505size=1 506stride=1 507pad=0 508batch_normalize=1 509activation=swish 510 511# conv_4_7_dwise 512[convolutional] 513groups=384 514filters=384 515size=5 516pad=1 517stride=2 518batch_normalize=1 519activation=swish 520 521 522#squeeze-n-excitation 523[avgpool] 524 525# squeeze ratio r=16 (recommended r=16) 526[convolutional] 527filters=24 528size=1 529stride=1 530activation=swish 531 532# excitation 533[convolutional] 534filters=384 535size=1 536stride=1 537activation=logistic 538 539# multiply channels 540[scale_channels] 541from=-4 542 543 544# conv_4_7_linear 545[convolutional] 546filters=112 547size=1 548stride=1 549pad=0 550batch_normalize=1 551activation=linear 552 553 554### CONV6 - MBConv6 - 2 (3) 555# conv_5_1_expand 556[convolutional] 557filters=576 558size=1 559stride=1 560pad=0 561batch_normalize=1 562activation=swish 563 564# conv_5_1_dwise 565[convolutional] 566groups=576 567filters=576 568size=5 569stride=1 570pad=1 571batch_normalize=1 572activation=swish 573 574 575#squeeze-n-excitation 576[avgpool] 577 578# squeeze ratio r=16 (recommended r=16) 579[convolutional] 580filters=32 581size=1 582stride=1 583activation=swish 584 585# excitation 586[convolutional] 587filters=576 588size=1 589stride=1 590activation=logistic 591 592# multiply channels 593[scale_channels] 594from=-4 595 596 597# conv_5_1_linear 598[convolutional] 599filters=112 600size=1 601stride=1 602pad=0 603batch_normalize=1 604activation=linear 605 606 607### CONV6 - MBConv6 - 3 (3) 608# dropout only before residual connection 609[dropout] 610probability=.0 611 612# block_5_1 613[shortcut] 614from=-9 615activation=linear 616 617# conv_5_2_expand 618[convolutional] 619filters=576 620size=1 621stride=1 622pad=0 623batch_normalize=1 624activation=swish 625 626# conv_5_2_dwise 627[convolutional] 628groups=576 629filters=576 630size=5 631stride=1 632pad=1 633batch_normalize=1 634activation=swish 635 636 637#squeeze-n-excitation 638[avgpool] 639 640# squeeze ratio r=16 (recommended r=16) 641[convolutional] 642filters=32 643size=1 644stride=1 645activation=swish 646 647# excitation 648[convolutional] 649filters=576 650size=1 651stride=1 652activation=logistic 653 654# multiply channels 655[scale_channels] 656from=-4 657 658 659# conv_5_2_linear 660[convolutional] 661filters=112 662size=1 663stride=1 664pad=0 665batch_normalize=1 666activation=linear 667 668 669### CONV7 - MBConv6 - 1 (4) 670# dropout only before residual connection 671[dropout] 672probability=.0 673 674# block_5_2 675[shortcut] 676from=-9 677activation=linear 678 679# conv_5_3_expand 680[convolutional] 681filters=576 682size=1 683stride=1 684pad=0 685batch_normalize=1 686activation=swish 687 688# conv_5_3_dwise 689[convolutional] 690groups=576 691filters=576 692size=5 693pad=1 694stride=2 695batch_normalize=1 696activation=swish 697 698 699#squeeze-n-excitation 700[avgpool] 701 702# squeeze ratio r=16 (recommended r=16) 703[convolutional] 704filters=32 705size=1 706stride=1 707activation=swish 708 709# excitation 710[convolutional] 711filters=576 712size=1 713stride=1 714activation=logistic 715 716# multiply channels 717[scale_channels] 718from=-4 719 720 721# conv_5_3_linear 722[convolutional] 723filters=192 724size=1 725stride=1 726pad=0 727batch_normalize=1 728activation=linear 729 730 731### CONV7 - MBConv6 - 2 (4) 732# conv_6_1_expand 733[convolutional] 734filters=960 735size=1 736stride=1 737pad=0 738batch_normalize=1 739activation=swish 740 741# conv_6_1_dwise 742[convolutional] 743groups=960 744filters=960 745size=5 746stride=1 747pad=1 748batch_normalize=1 749activation=swish 750 751 752#squeeze-n-excitation 753[avgpool] 754 755# squeeze ratio r=16 (recommended r=16) 756[convolutional] 757filters=64 758size=1 759stride=1 760activation=swish 761 762# excitation 763[convolutional] 764filters=960 765size=1 766stride=1 767activation=logistic 768 769# multiply channels 770[scale_channels] 771from=-4 772 773 774# conv_6_1_linear 775[convolutional] 776filters=192 777size=1 778stride=1 779pad=0 780batch_normalize=1 781activation=linear 782 783 784### CONV7 - MBConv6 - 3 (4) 785# dropout only before residual connection 786[dropout] 787probability=.0 788 789# block_6_1 790[shortcut] 791from=-9 792activation=linear 793 794# conv_6_2_expand 795[convolutional] 796filters=960 797size=1 798stride=1 799pad=0 800batch_normalize=1 801activation=swish 802 803# conv_6_2_dwise 804[convolutional] 805groups=960 806filters=960 807size=5 808stride=1 809pad=1 810batch_normalize=1 811activation=swish 812 813 814#squeeze-n-excitation 815[avgpool] 816 817# squeeze ratio r=16 (recommended r=16) 818[convolutional] 819filters=64 820size=1 821stride=1 822activation=swish 823 824# excitation 825[convolutional] 826filters=960 827size=1 828stride=1 829activation=logistic 830 831# multiply channels 832[scale_channels] 833from=-4 834 835 836# conv_6_2_linear 837[convolutional] 838filters=192 839size=1 840stride=1 841pad=0 842batch_normalize=1 843activation=linear 844 845 846### CONV7 - MBConv6 - 4 (4) 847# dropout only before residual connection 848[dropout] 849probability=.0 850 851# block_6_1 852[shortcut] 853from=-9 854activation=linear 855 856# conv_6_2_expand 857[convolutional] 858filters=960 859size=1 860stride=1 861pad=0 862batch_normalize=1 863activation=swish 864 865# conv_6_2_dwise 866[convolutional] 867groups=960 868filters=960 869size=5 870stride=1 871pad=1 872batch_normalize=1 873activation=swish 874 875 876#squeeze-n-excitation 877[avgpool] 878 879# squeeze ratio r=16 (recommended r=16) 880[convolutional] 881filters=64 882size=1 883stride=1 884activation=swish 885 886# excitation 887[convolutional] 888filters=960 889size=1 890stride=1 891activation=logistic 892 893# multiply channels 894[scale_channels] 895from=-4 896 897 898# conv_6_2_linear 899[convolutional] 900filters=192 901size=1 902stride=1 903pad=0 904batch_normalize=1 905activation=linear 906 907 908 909### CONV8 - MBConv6 - 1 (1) 910# dropout only before residual connection 911[dropout] 912probability=.0 913 914# block_6_2 915[shortcut] 916from=-9 917activation=linear 918 919# conv_6_3_expand 920[convolutional] 921filters=960 922size=1 923stride=1 924pad=0 925batch_normalize=1 926activation=swish 927 928# conv_6_3_dwise 929[convolutional] 930groups=960 931filters=960 932size=3 933stride=1 934pad=1 935batch_normalize=1 936activation=swish 937 938 939#squeeze-n-excitation 940[avgpool] 941 942# squeeze ratio r=16 (recommended r=16) 943[convolutional] 944filters=64 945size=1 946stride=1 947activation=swish 948 949# excitation 950[convolutional] 951filters=960 952size=1 953stride=1 954activation=logistic 955 956# multiply channels 957[scale_channels] 958from=-4 959 960 961# conv_6_3_linear 962[convolutional] 963filters=320 964size=1 965stride=1 966pad=0 967batch_normalize=1 968activation=linear 969 970 971### CONV9 - Conv2d 1x1 972# conv_6_4 973[convolutional] 974filters=1280 975size=1 976stride=1 977pad=0 978batch_normalize=1 979activation=swish 980 981########################## 982 983[convolutional] 984batch_normalize=1 985filters=256 986size=1 987stride=1 988pad=1 989activation=leaky 990 991[convolutional] 992batch_normalize=1 993filters=256 994size=3 995stride=1 996pad=1 997activation=leaky 998 999[shortcut] 1000activation=leaky 1001from=-2 1002 1003[convolutional] 1004size=1 1005stride=1 1006pad=1 1007filters=255 1008activation=linear 1009 1010 1011 1012[yolo] 1013mask = 3,4,5 1014anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 1015classes=80 1016num=6 1017jitter=.3 1018ignore_thresh = .7 1019truth_thresh = 1 1020random=0 1021 1022[route] 1023layers = -4 1024 1025[convolutional] 1026batch_normalize=1 1027filters=128 1028size=1 1029stride=1 1030pad=1 1031activation=leaky 1032 1033[upsample] 1034stride=2 1035 1036[shortcut] 1037activation=leaky 1038from=90 1039 1040[convolutional] 1041batch_normalize=1 1042filters=128 1043size=3 1044stride=1 1045pad=1 1046activation=leaky 1047 1048[shortcut] 1049activation=leaky 1050from=-3 1051 1052[shortcut] 1053activation=leaky 1054from=90 1055 1056[convolutional] 1057size=1 1058stride=1 1059pad=1 1060filters=255 1061activation=linear 1062 1063[yolo] 1064mask = 1,2,3 1065anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 1066classes=80 1067num=6 1068jitter=.3 1069ignore_thresh = .7 1070truth_thresh = 1 1071random=0 1072 1073