1[net] 2# Training 3batch=120 4subdivisions=4 5# Testing 6#batch=1 7#subdivisions=1 8height=224 9width=224 10channels=3 11momentum=0.9 12decay=0.0005 13max_crop=256 14#mixup=4 15blur=1 16cutmix=1 17mosaic=1 18 19burn_in=1000 20#burn_in=100 21learning_rate=0.256 22policy=poly 23power=4 24max_batches=800000 25momentum=0.9 26decay=0.00005 27 28angle=7 29hue=.1 30saturation=.75 31exposure=.75 32aspect=.75 33 34 35### CONV1 - 1 (1) 36# conv1 37[convolutional] 38filters=32 39size=3 40pad=1 41stride=2 42batch_normalize=1 43activation=swish 44 45 46### CONV2 - MBConv1 - 1 (1) 47# conv2_1_expand 48[convolutional] 49filters=32 50size=1 51stride=1 52pad=0 53batch_normalize=1 54activation=swish 55 56# conv2_1_dwise 57[convolutional] 58groups=32 59filters=32 60size=3 61stride=1 62pad=1 63batch_normalize=1 64activation=swish 65 66 67#squeeze-n-excitation 68[avgpool] 69 70# squeeze ratio r=4 (recommended r=16) 71[convolutional] 72filters=8 73size=1 74stride=1 75activation=swish 76 77# excitation 78[convolutional] 79filters=32 80size=1 81stride=1 82activation=logistic 83 84# multiply channels 85[scale_channels] 86from=-4 87 88 89# conv2_1_linear 90[convolutional] 91filters=16 92size=1 93stride=1 94pad=0 95batch_normalize=1 96activation=linear 97 98 99 100### CONV3 - MBConv6 - 1 (2) 101# conv2_2_expand 102[convolutional] 103filters=96 104size=1 105stride=1 106pad=0 107batch_normalize=1 108activation=swish 109 110# conv2_2_dwise 111[convolutional] 112groups=96 113filters=96 114size=3 115pad=1 116stride=2 117batch_normalize=1 118activation=swish 119 120 121#squeeze-n-excitation 122[avgpool] 123 124# squeeze ratio r=8 (recommended r=16) 125[convolutional] 126filters=16 127size=1 128stride=1 129activation=swish 130 131# excitation 132[convolutional] 133filters=96 134size=1 135stride=1 136activation=logistic 137 138# multiply channels 139[scale_channels] 140from=-4 141 142 143# conv2_2_linear 144[convolutional] 145filters=24 146size=1 147stride=1 148pad=0 149batch_normalize=1 150activation=linear 151 152 153### CONV3 - MBConv6 - 2 (2) 154# conv3_1_expand 155[convolutional] 156filters=144 157size=1 158stride=1 159pad=0 160batch_normalize=1 161activation=swish 162 163# conv3_1_dwise 164[convolutional] 165groups=144 166filters=144 167size=3 168stride=1 169pad=1 170batch_normalize=1 171activation=swish 172 173 174#squeeze-n-excitation 175[avgpool] 176 177# squeeze ratio r=16 (recommended r=16) 178[convolutional] 179filters=8 180size=1 181stride=1 182activation=swish 183 184# excitation 185[convolutional] 186filters=144 187size=1 188stride=1 189activation=logistic 190 191# multiply channels 192[scale_channels] 193from=-4 194 195 196# conv3_1_linear 197[convolutional] 198filters=24 199size=1 200stride=1 201pad=0 202batch_normalize=1 203activation=linear 204 205 206 207### CONV4 - MBConv6 - 1 (2) 208# dropout only before residual connection 209[dropout] 210probability=.2 211 212# block_3_1 213[shortcut] 214from=-9 215activation=linear 216 217# conv_3_2_expand 218[convolutional] 219filters=144 220size=1 221stride=1 222pad=0 223batch_normalize=1 224activation=swish 225 226# conv_3_2_dwise 227[convolutional] 228groups=144 229filters=144 230size=5 231pad=1 232stride=2 233batch_normalize=1 234activation=swish 235 236 237#squeeze-n-excitation 238[avgpool] 239 240# squeeze ratio r=16 (recommended r=16) 241[convolutional] 242filters=8 243size=1 244stride=1 245activation=swish 246 247# excitation 248[convolutional] 249filters=144 250size=1 251stride=1 252activation=logistic 253 254# multiply channels 255[scale_channels] 256from=-4 257 258 259# conv_3_2_linear 260[convolutional] 261filters=40 262size=1 263stride=1 264pad=0 265batch_normalize=1 266activation=linear 267 268 269### CONV4 - MBConv6 - 2 (2) 270# conv_4_1_expand 271[convolutional] 272filters=192 273size=1 274stride=1 275pad=0 276batch_normalize=1 277activation=swish 278 279# conv_4_1_dwise 280[convolutional] 281groups=192 282filters=192 283size=5 284stride=1 285pad=1 286batch_normalize=1 287activation=swish 288 289 290#squeeze-n-excitation 291[avgpool] 292 293# squeeze ratio r=16 (recommended r=16) 294[convolutional] 295filters=16 296size=1 297stride=1 298activation=swish 299 300# excitation 301[convolutional] 302filters=192 303size=1 304stride=1 305activation=logistic 306 307# multiply channels 308[scale_channels] 309from=-4 310 311 312# conv_4_1_linear 313[convolutional] 314filters=40 315size=1 316stride=1 317pad=0 318batch_normalize=1 319activation=linear 320 321 322 323 324### CONV5 - MBConv6 - 1 (3) 325# dropout only before residual connection 326[dropout] 327probability=.2 328 329# block_4_2 330[shortcut] 331from=-9 332activation=linear 333 334# conv_4_3_expand 335[convolutional] 336filters=192 337size=1 338stride=1 339pad=0 340batch_normalize=1 341activation=swish 342 343# conv_4_3_dwise 344[convolutional] 345groups=192 346filters=192 347size=3 348stride=1 349pad=1 350batch_normalize=1 351activation=swish 352 353 354#squeeze-n-excitation 355[avgpool] 356 357# squeeze ratio r=16 (recommended r=16) 358[convolutional] 359filters=16 360size=1 361stride=1 362activation=swish 363 364# excitation 365[convolutional] 366filters=192 367size=1 368stride=1 369activation=logistic 370 371# multiply channels 372[scale_channels] 373from=-4 374 375 376# conv_4_3_linear 377[convolutional] 378filters=80 379size=1 380stride=1 381pad=0 382batch_normalize=1 383activation=linear 384 385 386### CONV5 - MBConv6 - 2 (3) 387# conv_4_4_expand 388[convolutional] 389filters=384 390size=1 391stride=1 392pad=0 393batch_normalize=1 394activation=swish 395 396# conv_4_4_dwise 397[convolutional] 398groups=384 399filters=384 400size=3 401stride=1 402pad=1 403batch_normalize=1 404activation=swish 405 406 407#squeeze-n-excitation 408[avgpool] 409 410# squeeze ratio r=16 (recommended r=16) 411[convolutional] 412filters=24 413size=1 414stride=1 415activation=swish 416 417# excitation 418[convolutional] 419filters=384 420size=1 421stride=1 422activation=logistic 423 424# multiply channels 425[scale_channels] 426from=-4 427 428 429# conv_4_4_linear 430[convolutional] 431filters=80 432size=1 433stride=1 434pad=0 435batch_normalize=1 436activation=linear 437 438 439### CONV5 - MBConv6 - 3 (3) 440# dropout only before residual connection 441[dropout] 442probability=.2 443 444# block_4_4 445[shortcut] 446from=-9 447activation=linear 448 449# conv_4_5_expand 450[convolutional] 451filters=384 452size=1 453stride=1 454pad=0 455batch_normalize=1 456activation=swish 457 458# conv_4_5_dwise 459[convolutional] 460groups=384 461filters=384 462size=3 463stride=1 464pad=1 465batch_normalize=1 466activation=swish 467 468 469#squeeze-n-excitation 470[avgpool] 471 472# squeeze ratio r=16 (recommended r=16) 473[convolutional] 474filters=24 475size=1 476stride=1 477activation=swish 478 479# excitation 480[convolutional] 481filters=384 482size=1 483stride=1 484activation=logistic 485 486# multiply channels 487[scale_channels] 488from=-4 489 490 491# conv_4_5_linear 492[convolutional] 493filters=80 494size=1 495stride=1 496pad=0 497batch_normalize=1 498activation=linear 499 500 501 502### CONV6 - MBConv6 - 1 (3) 503# dropout only before residual connection 504[dropout] 505probability=.2 506 507# block_4_6 508[shortcut] 509from=-9 510activation=linear 511 512# conv_4_7_expand 513[convolutional] 514filters=384 515size=1 516stride=1 517pad=0 518batch_normalize=1 519activation=swish 520 521# conv_4_7_dwise 522[convolutional] 523groups=384 524filters=384 525size=5 526pad=1 527stride=2 528batch_normalize=1 529activation=swish 530 531 532#squeeze-n-excitation 533[avgpool] 534 535# squeeze ratio r=16 (recommended r=16) 536[convolutional] 537filters=24 538size=1 539stride=1 540activation=swish 541 542# excitation 543[convolutional] 544filters=384 545size=1 546stride=1 547activation=logistic 548 549# multiply channels 550[scale_channels] 551from=-4 552 553 554# conv_4_7_linear 555[convolutional] 556filters=112 557size=1 558stride=1 559pad=0 560batch_normalize=1 561activation=linear 562 563 564### CONV6 - MBConv6 - 2 (3) 565# conv_5_1_expand 566[convolutional] 567filters=576 568size=1 569stride=1 570pad=0 571batch_normalize=1 572activation=swish 573 574# conv_5_1_dwise 575[convolutional] 576groups=576 577filters=576 578size=5 579stride=1 580pad=1 581batch_normalize=1 582activation=swish 583 584 585#squeeze-n-excitation 586[avgpool] 587 588# squeeze ratio r=16 (recommended r=16) 589[convolutional] 590filters=32 591size=1 592stride=1 593activation=swish 594 595# excitation 596[convolutional] 597filters=576 598size=1 599stride=1 600activation=logistic 601 602# multiply channels 603[scale_channels] 604from=-4 605 606 607# conv_5_1_linear 608[convolutional] 609filters=112 610size=1 611stride=1 612pad=0 613batch_normalize=1 614activation=linear 615 616 617### CONV6 - MBConv6 - 3 (3) 618# dropout only before residual connection 619[dropout] 620probability=.2 621 622# block_5_1 623[shortcut] 624from=-9 625activation=linear 626 627# conv_5_2_expand 628[convolutional] 629filters=576 630size=1 631stride=1 632pad=0 633batch_normalize=1 634activation=swish 635 636# conv_5_2_dwise 637[convolutional] 638groups=576 639filters=576 640size=5 641stride=1 642pad=1 643batch_normalize=1 644activation=swish 645 646 647#squeeze-n-excitation 648[avgpool] 649 650# squeeze ratio r=16 (recommended r=16) 651[convolutional] 652filters=32 653size=1 654stride=1 655activation=swish 656 657# excitation 658[convolutional] 659filters=576 660size=1 661stride=1 662activation=logistic 663 664# multiply channels 665[scale_channels] 666from=-4 667 668 669# conv_5_2_linear 670[convolutional] 671filters=112 672size=1 673stride=1 674pad=0 675batch_normalize=1 676activation=linear 677 678 679### CONV7 - MBConv6 - 1 (4) 680# dropout only before residual connection 681[dropout] 682probability=.2 683 684# block_5_2 685[shortcut] 686from=-9 687activation=linear 688 689# conv_5_3_expand 690[convolutional] 691filters=576 692size=1 693stride=1 694pad=0 695batch_normalize=1 696activation=swish 697 698# conv_5_3_dwise 699[convolutional] 700groups=576 701filters=576 702size=5 703pad=1 704stride=2 705batch_normalize=1 706activation=swish 707 708 709#squeeze-n-excitation 710[avgpool] 711 712# squeeze ratio r=16 (recommended r=16) 713[convolutional] 714filters=32 715size=1 716stride=1 717activation=swish 718 719# excitation 720[convolutional] 721filters=576 722size=1 723stride=1 724activation=logistic 725 726# multiply channels 727[scale_channels] 728from=-4 729 730 731# conv_5_3_linear 732[convolutional] 733filters=192 734size=1 735stride=1 736pad=0 737batch_normalize=1 738activation=linear 739 740 741### CONV7 - MBConv6 - 2 (4) 742# conv_6_1_expand 743[convolutional] 744filters=960 745size=1 746stride=1 747pad=0 748batch_normalize=1 749activation=swish 750 751# conv_6_1_dwise 752[convolutional] 753groups=960 754filters=960 755size=5 756stride=1 757pad=1 758batch_normalize=1 759activation=swish 760 761 762#squeeze-n-excitation 763[avgpool] 764 765# squeeze ratio r=16 (recommended r=16) 766[convolutional] 767filters=64 768size=1 769stride=1 770activation=swish 771 772# excitation 773[convolutional] 774filters=960 775size=1 776stride=1 777activation=logistic 778 779# multiply channels 780[scale_channels] 781from=-4 782 783 784# conv_6_1_linear 785[convolutional] 786filters=192 787size=1 788stride=1 789pad=0 790batch_normalize=1 791activation=linear 792 793 794### CONV7 - MBConv6 - 3 (4) 795# dropout only before residual connection 796[dropout] 797probability=.2 798 799# block_6_1 800[shortcut] 801from=-9 802activation=linear 803 804# conv_6_2_expand 805[convolutional] 806filters=960 807size=1 808stride=1 809pad=0 810batch_normalize=1 811activation=swish 812 813# conv_6_2_dwise 814[convolutional] 815groups=960 816filters=960 817size=5 818stride=1 819pad=1 820batch_normalize=1 821activation=swish 822 823 824#squeeze-n-excitation 825[avgpool] 826 827# squeeze ratio r=16 (recommended r=16) 828[convolutional] 829filters=64 830size=1 831stride=1 832activation=swish 833 834# excitation 835[convolutional] 836filters=960 837size=1 838stride=1 839activation=logistic 840 841# multiply channels 842[scale_channels] 843from=-4 844 845 846# conv_6_2_linear 847[convolutional] 848filters=192 849size=1 850stride=1 851pad=0 852batch_normalize=1 853activation=linear 854 855 856### CONV7 - MBConv6 - 4 (4) 857# dropout only before residual connection 858[dropout] 859probability=.2 860 861# block_6_1 862[shortcut] 863from=-9 864activation=linear 865 866# conv_6_2_expand 867[convolutional] 868filters=960 869size=1 870stride=1 871pad=0 872batch_normalize=1 873activation=swish 874 875# conv_6_2_dwise 876[convolutional] 877groups=960 878filters=960 879size=5 880stride=1 881pad=1 882batch_normalize=1 883activation=swish 884 885 886#squeeze-n-excitation 887[avgpool] 888 889# squeeze ratio r=16 (recommended r=16) 890[convolutional] 891filters=64 892size=1 893stride=1 894activation=swish 895 896# excitation 897[convolutional] 898filters=960 899size=1 900stride=1 901activation=logistic 902 903# multiply channels 904[scale_channels] 905from=-4 906 907 908# conv_6_2_linear 909[convolutional] 910filters=192 911size=1 912stride=1 913pad=0 914batch_normalize=1 915activation=linear 916 917 918 919### CONV8 - MBConv6 - 1 (1) 920# dropout only before residual connection 921[dropout] 922probability=.2 923 924# block_6_2 925[shortcut] 926from=-9 927activation=linear 928 929# conv_6_3_expand 930[convolutional] 931filters=960 932size=1 933stride=1 934pad=0 935batch_normalize=1 936activation=swish 937 938# conv_6_3_dwise 939[convolutional] 940groups=960 941filters=960 942size=3 943stride=1 944pad=1 945batch_normalize=1 946activation=swish 947 948 949#squeeze-n-excitation 950[avgpool] 951 952# squeeze ratio r=16 (recommended r=16) 953[convolutional] 954filters=64 955size=1 956stride=1 957activation=swish 958 959# excitation 960[convolutional] 961filters=960 962size=1 963stride=1 964activation=logistic 965 966# multiply channels 967[scale_channels] 968from=-4 969 970 971# conv_6_3_linear 972[convolutional] 973filters=320 974size=1 975stride=1 976pad=0 977batch_normalize=1 978activation=linear 979 980 981### CONV9 - Conv2d 1x1 982# conv_6_4 983[convolutional] 984filters=1280 985size=1 986stride=1 987pad=0 988batch_normalize=1 989activation=swish 990 991 992[avgpool] 993 994[dropout] 995probability=.2 996 997[convolutional] 998filters=1000 999size=1 1000stride=1 1001pad=0 1002activation=linear 1003 1004[softmax] 1005groups=1 1006 1007#[cost] 1008#type=sse 1009 1010