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mailsys_server
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Repository for M.A.I.L system's analysis server.
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mailsys_server/yolov4_7gpu.cfg

yolov4_7gpu.cfg 12KB
履歴 Raw

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  1. [net]

  2. # Testing

  3. #batch=1

  4. #subdivisions=1

  5. # Training

  6. batch=64

  7. subdivisions=64

  8. width=1920

  9. height=1056

  10. channels=3

  11. momentum=0.949

  12. decay=0.0005

  13. angle=0

  14. saturation = 1.5

  15. exposure = 1.5

  16. hue=.1

  17. 

  18. learning_rate=0.000372857

  19. burn_in=7000

  20. max_batches = 100000

  21. policy=steps

  22. steps=80000, 90000

  23. scales=.1,.1

  24. 

  25. mosaic=1

  26. 

  27. [convolutional]

  28. batch_normalize=1

  29. filters=32

  30. size=3

  31. stride=1

  32. pad=1

  33. activation=mish

  34. 

  35. # Downsample

  36. 

  37. [convolutional]

  38. batch_normalize=1

  39. filters=64

  40. size=3

  41. stride=2

  42. pad=1

  43. activation=mish

  44. 

  45. [convolutional]

  46. batch_normalize=1

  47. filters=64

  48. size=1

  49. stride=1

  50. pad=1

  51. activation=mish

  52. 

  53. [route]

  54. layers = -2

  55. 

  56. [convolutional]

  57. batch_normalize=1

  58. filters=64

  59. size=1

  60. stride=1

  61. pad=1

  62. activation=mish

  63. 

  64. [convolutional]

  65. batch_normalize=1

  66. filters=32

  67. size=1

  68. stride=1

  69. pad=1

  70. activation=mish

  71. 

  72. [convolutional]

  73. batch_normalize=1

  74. filters=64

  75. size=3

  76. stride=1

  77. pad=1

  78. activation=mish

  79. 

  80. [shortcut]

  81. from=-3

  82. activation=linear

  83. 

  84. [convolutional]

  85. batch_normalize=1

  86. filters=64

  87. size=1

  88. stride=1

  89. pad=1

  90. activation=mish

  91. 

  92. [route]

  93. layers = -1,-7

  94. 

  95. [convolutional]

  96. batch_normalize=1

  97. filters=64

  98. size=1

  99. stride=1

  100. pad=1

  101. activation=mish

  102. 

  103. # Downsample

  104. 

  105. [convolutional]

  106. batch_normalize=1

  107. filters=128

  108. size=3

  109. stride=2

  110. pad=1

  111. activation=mish

  112. 

  113. [convolutional]

  114. batch_normalize=1

  115. filters=64

  116. size=1

  117. stride=1

  118. pad=1

  119. activation=mish

  120. 

  121. [route]

  122. layers = -2

  123. 

  124. [convolutional]

  125. batch_normalize=1

  126. filters=64

  127. size=1

  128. stride=1

  129. pad=1

  130. activation=mish

  131. 

  132. [convolutional]

  133. batch_normalize=1

  134. filters=64

  135. size=1

  136. stride=1

  137. pad=1

  138. activation=mish

  139. 

  140. [convolutional]

  141. batch_normalize=1

  142. filters=64

  143. size=3

  144. stride=1

  145. pad=1

  146. activation=mish

  147. 

  148. [shortcut]

  149. from=-3

  150. activation=linear

  151. 

  152. [convolutional]

  153. batch_normalize=1

  154. filters=64

  155. size=1

  156. stride=1

  157. pad=1

  158. activation=mish

  159. 

  160. [convolutional]

  161. batch_normalize=1

  162. filters=64

  163. size=3

  164. stride=1

  165. pad=1

  166. activation=mish

  167. 

  168. [shortcut]

  169. from=-3

  170. activation=linear

  171. 

  172. [convolutional]

  173. batch_normalize=1

  174. filters=64

  175. size=1

  176. stride=1

  177. pad=1

  178. activation=mish

  179. 

  180. [route]

  181. layers = -1,-10

  182. 

  183. [convolutional]

  184. batch_normalize=1

  185. filters=128

  186. size=1

  187. stride=1

  188. pad=1

  189. activation=mish

  190. 

  191. # Downsample

  192. 

  193. [convolutional]

  194. batch_normalize=1

  195. filters=256

  196. size=3

  197. stride=2

  198. pad=1

  199. activation=mish

  200. 

  201. [convolutional]

  202. batch_normalize=1

  203. filters=128

  204. size=1

  205. stride=1

  206. pad=1

  207. activation=mish

  208. 

  209. [route]

  210. layers = -2

  211. 

  212. [convolutional]

  213. batch_normalize=1

  214. filters=128

  215. size=1

  216. stride=1

  217. pad=1

  218. activation=mish

  219. 

  220. [convolutional]

  221. batch_normalize=1

  222. filters=128

  223. size=1

  224. stride=1

  225. pad=1

  226. activation=mish

  227. 

  228. [convolutional]

  229. batch_normalize=1

  230. filters=128

  231. size=3

  232. stride=1

  233. pad=1

  234. activation=mish

  235. 

  236. [shortcut]

  237. from=-3

  238. activation=linear

  239. 

  240. [convolutional]

  241. batch_normalize=1

  242. filters=128

  243. size=1

  244. stride=1

  245. pad=1

  246. activation=mish

  247. 

  248. [convolutional]

  249. batch_normalize=1

  250. filters=128

  251. size=3

  252. stride=1

  253. pad=1

  254. activation=mish

  255. 

  256. [shortcut]

  257. from=-3

  258. activation=linear

  259. 

  260. [convolutional]

  261. batch_normalize=1

  262. filters=128

  263. size=1

  264. stride=1

  265. pad=1

  266. activation=mish

  267. 

  268. [convolutional]

  269. batch_normalize=1

  270. filters=128

  271. size=3

  272. stride=1

  273. pad=1

  274. activation=mish

  275. 

  276. [shortcut]

  277. from=-3

  278. activation=linear

  279. 

  280. [convolutional]

  281. batch_normalize=1

  282. filters=128

  283. size=1

  284. stride=1

  285. pad=1

  286. activation=mish

  287. 

  288. [convolutional]

  289. batch_normalize=1

  290. filters=128

  291. size=3

  292. stride=1

  293. pad=1

  294. activation=mish

  295. 

  296. [shortcut]

  297. from=-3

  298. activation=linear

  299. 

  300. 

  301. [convolutional]

  302. batch_normalize=1

  303. filters=128

  304. size=1

  305. stride=1

  306. pad=1

  307. activation=mish

  308. 

  309. [convolutional]

  310. batch_normalize=1

  311. filters=128

  312. size=3

  313. stride=1

  314. pad=1

  315. activation=mish

  316. 

  317. [shortcut]

  318. from=-3

  319. activation=linear

  320. 

  321. [convolutional]

  322. batch_normalize=1

  323. filters=128

  324. size=1

  325. stride=1

  326. pad=1

  327. activation=mish

  328. 

  329. [convolutional]

  330. batch_normalize=1

  331. filters=128

  332. size=3

  333. stride=1

  334. pad=1

  335. activation=mish

  336. 

  337. [shortcut]

  338. from=-3

  339. activation=linear

  340. 

  341. [convolutional]

  342. batch_normalize=1

  343. filters=128

  344. size=1

  345. stride=1

  346. pad=1

  347. activation=mish

  348. 

  349. [convolutional]

  350. batch_normalize=1

  351. filters=128

  352. size=3

  353. stride=1

  354. pad=1

  355. activation=mish

  356. 

  357. [shortcut]

  358. from=-3

  359. activation=linear

  360. 

  361. [convolutional]

  362. batch_normalize=1

  363. filters=128

  364. size=1

  365. stride=1

  366. pad=1

  367. activation=mish

  368. 

  369. [convolutional]

  370. batch_normalize=1

  371. filters=128

  372. size=3

  373. stride=1

  374. pad=1

  375. activation=mish

  376. 

  377. [shortcut]

  378. from=-3

  379. activation=linear

  380. 

  381. [convolutional]

  382. batch_normalize=1

  383. filters=128

  384. size=1

  385. stride=1

  386. pad=1

  387. activation=mish

  388. 

  389. [route]

  390. layers = -1,-28

  391. 

  392. [convolutional]

  393. batch_normalize=1

  394. filters=256

  395. size=1

  396. stride=1

  397. pad=1

  398. activation=mish

  399. 

  400. # Downsample

  401. 

  402. [convolutional]

  403. batch_normalize=1

  404. filters=512

  405. size=3

  406. stride=2

  407. pad=1

  408. activation=mish

  409. 

  410. [convolutional]

  411. batch_normalize=1

  412. filters=256

  413. size=1

  414. stride=1

  415. pad=1

  416. activation=mish

  417. 

  418. [route]

  419. layers = -2

  420. 

  421. [convolutional]

  422. batch_normalize=1

  423. filters=256

  424. size=1

  425. stride=1

  426. pad=1

  427. activation=mish

  428. 

  429. [convolutional]

  430. batch_normalize=1

  431. filters=256

  432. size=1

  433. stride=1

  434. pad=1

  435. activation=mish

  436. 

  437. [convolutional]

  438. batch_normalize=1

  439. filters=256

  440. size=3

  441. stride=1

  442. pad=1

  443. activation=mish

  444. 

  445. [shortcut]

  446. from=-3

  447. activation=linear

  448. 

  449. 

  450. [convolutional]

  451. batch_normalize=1

  452. filters=256

  453. size=1

  454. stride=1

  455. pad=1

  456. activation=mish

  457. 

  458. [convolutional]

  459. batch_normalize=1

  460. filters=256

  461. size=3

  462. stride=1

  463. pad=1

  464. activation=mish

  465. 

  466. [shortcut]

  467. from=-3

  468. activation=linear

  469. 

  470. 

  471. [convolutional]

  472. batch_normalize=1

  473. filters=256

  474. size=1

  475. stride=1

  476. pad=1

  477. activation=mish

  478. 

  479. [convolutional]

  480. batch_normalize=1

  481. filters=256

  482. size=3

  483. stride=1

  484. pad=1

  485. activation=mish

  486. 

  487. [shortcut]

  488. from=-3

  489. activation=linear

  490. 

  491. 

  492. [convolutional]

  493. batch_normalize=1

  494. filters=256

  495. size=1

  496. stride=1

  497. pad=1

  498. activation=mish

  499. 

  500. [convolutional]

  501. batch_normalize=1

  502. filters=256

  503. size=3

  504. stride=1

  505. pad=1

  506. activation=mish

  507. 

  508. [shortcut]

  509. from=-3

  510. activation=linear

  511. 

  512. 

  513. [convolutional]

  514. batch_normalize=1

  515. filters=256

  516. size=1

  517. stride=1

  518. pad=1

  519. activation=mish

  520. 

  521. [convolutional]

  522. batch_normalize=1

  523. filters=256

  524. size=3

  525. stride=1

  526. pad=1

  527. activation=mish

  528. 

  529. [shortcut]

  530. from=-3

  531. activation=linear

  532. 

  533. 

  534. [convolutional]

  535. batch_normalize=1

  536. filters=256

  537. size=1

  538. stride=1

  539. pad=1

  540. activation=mish

  541. 

  542. [convolutional]

  543. batch_normalize=1

  544. filters=256

  545. size=3

  546. stride=1

  547. pad=1

  548. activation=mish

  549. 

  550. [shortcut]

  551. from=-3

  552. activation=linear

  553. 

  554. 

  555. [convolutional]

  556. batch_normalize=1

  557. filters=256

  558. size=1

  559. stride=1

  560. pad=1

  561. activation=mish

  562. 

  563. [convolutional]

  564. batch_normalize=1

  565. filters=256

  566. size=3

  567. stride=1

  568. pad=1

  569. activation=mish

  570. 

  571. [shortcut]

  572. from=-3

  573. activation=linear

  574. 

  575. [convolutional]

  576. batch_normalize=1

  577. filters=256

  578. size=1

  579. stride=1

  580. pad=1

  581. activation=mish

  582. 

  583. [convolutional]

  584. batch_normalize=1

  585. filters=256

  586. size=3

  587. stride=1

  588. pad=1

  589. activation=mish

  590. 

  591. [shortcut]

  592. from=-3

  593. activation=linear

  594. 

  595. [convolutional]

  596. batch_normalize=1

  597. filters=256

  598. size=1

  599. stride=1

  600. pad=1

  601. activation=mish

  602. 

  603. [route]

  604. layers = -1,-28

  605. 

  606. [convolutional]

  607. batch_normalize=1

  608. filters=512

  609. size=1

  610. stride=1

  611. pad=1

  612. activation=mish

  613. 

  614. # Downsample

  615. 

  616. [convolutional]

  617. batch_normalize=1

  618. filters=1024

  619. size=3

  620. stride=2

  621. pad=1

  622. activation=mish

  623. 

  624. [convolutional]

  625. batch_normalize=1

  626. filters=512

  627. size=1

  628. stride=1

  629. pad=1

  630. activation=mish

  631. 

  632. [route]

  633. layers = -2

  634. 

  635. [convolutional]

  636. batch_normalize=1

  637. filters=512

  638. size=1

  639. stride=1

  640. pad=1

  641. activation=mish

  642. 

  643. [convolutional]

  644. batch_normalize=1

  645. filters=512

  646. size=1

  647. stride=1

  648. pad=1

  649. activation=mish

  650. 

  651. [convolutional]

  652. batch_normalize=1

  653. filters=512

  654. size=3

  655. stride=1

  656. pad=1

  657. activation=mish

  658. 

  659. [shortcut]

  660. from=-3

  661. activation=linear

  662. 

  663. [convolutional]

  664. batch_normalize=1

  665. filters=512

  666. size=1

  667. stride=1

  668. pad=1

  669. activation=mish

  670. 

  671. [convolutional]

  672. batch_normalize=1

  673. filters=512

  674. size=3

  675. stride=1

  676. pad=1

  677. activation=mish

  678. 

  679. [shortcut]

  680. from=-3

  681. activation=linear

  682. 

  683. [convolutional]

  684. batch_normalize=1

  685. filters=512

  686. size=1

  687. stride=1

  688. pad=1

  689. activation=mish

  690. 

  691. [convolutional]

  692. batch_normalize=1

  693. filters=512

  694. size=3

  695. stride=1

  696. pad=1

  697. activation=mish

  698. 

  699. [shortcut]

  700. from=-3

  701. activation=linear

  702. 

  703. [convolutional]

  704. batch_normalize=1

  705. filters=512

  706. size=1

  707. stride=1

  708. pad=1

  709. activation=mish

  710. 

  711. [convolutional]

  712. batch_normalize=1

  713. filters=512

  714. size=3

  715. stride=1

  716. pad=1

  717. activation=mish

  718. 

  719. [shortcut]

  720. from=-3

  721. activation=linear

  722. 

  723. [convolutional]

  724. batch_normalize=1

  725. filters=512

  726. size=1

  727. stride=1

  728. pad=1

  729. activation=mish

  730. 

  731. [route]

  732. layers = -1,-16

  733. 

  734. [convolutional]

  735. batch_normalize=1

  736. filters=1024

  737. size=1

  738. stride=1

  739. pad=1

  740. activation=mish

  741. 

  742. ##########################

  743. 

  744. [convolutional]

  745. batch_normalize=1

  746. filters=512

  747. size=1

  748. stride=1

  749. pad=1

  750. activation=leaky

  751. 

  752. [convolutional]

  753. batch_normalize=1

  754. size=3

  755. stride=1

  756. pad=1

  757. filters=1024

  758. activation=leaky

  759. 

  760. [convolutional]

  761. batch_normalize=1

  762. filters=512

  763. size=1

  764. stride=1

  765. pad=1

  766. activation=leaky

  767. 

  768. ### SPP ###

  769. [maxpool]

  770. stride=1

  771. size=5

  772. 

  773. [route]

  774. layers=-2

  775. 

  776. [maxpool]

  777. stride=1

  778. size=9

  779. 

  780. [route]

  781. layers=-4

  782. 

  783. [maxpool]

  784. stride=1

  785. size=13

  786. 

  787. [route]

  788. layers=-1,-3,-5,-6

  789. ### End SPP ###

  790. 

  791. [convolutional]

  792. batch_normalize=1

  793. filters=512

  794. size=1

  795. stride=1

  796. pad=1

  797. activation=leaky

  798. 

  799. [convolutional]

  800. batch_normalize=1

  801. size=3

  802. stride=1

  803. pad=1

  804. filters=1024

  805. activation=leaky

  806. 

  807. [convolutional]

  808. batch_normalize=1

  809. filters=512

  810. size=1

  811. stride=1

  812. pad=1

  813. activation=leaky

  814. 

  815. [convolutional]

  816. batch_normalize=1

  817. filters=256

  818. size=1

  819. stride=1

  820. pad=1

  821. activation=leaky

  822. 

  823. [upsample]

  824. stride=2

  825. 

  826. [route]

  827. layers = 85

  828. 

  829. [convolutional]

  830. batch_normalize=1

  831. filters=256

  832. size=1

  833. stride=1

  834. pad=1

  835. activation=leaky

  836. 

  837. [route]

  838. layers = -1, -3

  839. 

  840. [convolutional]

  841. batch_normalize=1

  842. filters=256

  843. size=1

  844. stride=1

  845. pad=1

  846. activation=leaky

  847. 

  848. [convolutional]

  849. batch_normalize=1

  850. size=3

  851. stride=1

  852. pad=1

  853. filters=512

  854. activation=leaky

  855. 

  856. [convolutional]

  857. batch_normalize=1

  858. filters=256

  859. size=1

  860. stride=1

  861. pad=1

  862. activation=leaky

  863. 

  864. [convolutional]

  865. batch_normalize=1

  866. size=3

  867. stride=1

  868. pad=1

  869. filters=512

  870. activation=leaky

  871. 

  872. [convolutional]

  873. batch_normalize=1

  874. filters=256

  875. size=1

  876. stride=1

  877. pad=1

  878. activation=leaky

  879. 

  880. [convolutional]

  881. batch_normalize=1

  882. filters=128

  883. size=1

  884. stride=1

  885. pad=1

  886. activation=leaky

  887. 

  888. [upsample]

  889. stride=2

  890. 

  891. [route]

  892. layers = 54

  893. 

  894. [convolutional]

  895. batch_normalize=1

  896. filters=128

  897. size=1

  898. stride=1

  899. pad=1

  900. activation=leaky

  901. 

  902. [route]

  903. layers = -1, -3

  904. 

  905. [convolutional]

  906. batch_normalize=1

  907. filters=128

  908. size=1

  909. stride=1

  910. pad=1

  911. activation=leaky

  912. 

  913. [convolutional]

  914. batch_normalize=1

  915. size=3

  916. stride=1

  917. pad=1

  918. filters=256

  919. activation=leaky

  920. 

  921. [convolutional]

  922. batch_normalize=1

  923. filters=128

  924. size=1

  925. stride=1

  926. pad=1

  927. activation=leaky

  928. 

  929. [convolutional]

  930. batch_normalize=1

  931. size=3

  932. stride=1

  933. pad=1

  934. filters=256

  935. activation=leaky

  936. 

  937. [convolutional]

  938. batch_normalize=1

  939. filters=128

  940. size=1

  941. stride=1

  942. pad=1

  943. activation=leaky

  944. 

  945. ##########################

  946. 

  947. [convolutional]

  948. batch_normalize=1

  949. size=3

  950. stride=1

  951. pad=1

  952. filters=256

  953. activation=leaky

  954. 

  955. [convolutional]

  956. size=1

  957. stride=1

  958. pad=1

  959. filters=18

  960. activation=linear

  961. 

  962. 

  963. [yolo]

  964. mask = 0,1,2

  965. anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401

  966. classes=1

  967. num=9

  968. jitter=.3

  969. ignore_thresh = .7

  970. truth_thresh = 1

  971. random=1

  972. scale_x_y = 1.2

  973. iou_thresh=0.213

  974. cls_normalizer=1.0

  975. iou_normalizer=0.07

  976. iou_loss=ciou

  977. nms_kind=greedynms

  978. beta_nms=0.6

  979. 

  980. [route]

  981. layers = -4

  982. 

  983. [convolutional]

  984. batch_normalize=1

  985. size=3

  986. stride=2

  987. pad=1

  988. filters=256

  989. activation=leaky

  990. 

  991. [route]

  992. layers = -1, -16

  993. 

  994. [convolutional]

  995. batch_normalize=1

  996. filters=256

  997. size=1

  998. stride=1

  999. pad=1

  1000. activation=leaky

  1001. 

  1002. [convolutional]

  1003. batch_normalize=1

  1004. size=3

  1005. stride=1

  1006. pad=1

  1007. filters=512

  1008. activation=leaky

  1009. 

  1010. [convolutional]

  1011. batch_normalize=1

  1012. filters=256

  1013. size=1

  1014. stride=1

  1015. pad=1

  1016. activation=leaky

  1017. 

  1018. [convolutional]

  1019. batch_normalize=1

  1020. size=3

  1021. stride=1

  1022. pad=1

  1023. filters=512

  1024. activation=leaky

  1025. 

  1026. [convolutional]

  1027. batch_normalize=1

  1028. filters=256

  1029. size=1

  1030. stride=1

  1031. pad=1

  1032. activation=leaky

  1033. 

  1034. [convolutional]

  1035. batch_normalize=1

  1036. size=3

  1037. stride=1

  1038. pad=1

  1039. filters=512

  1040. activation=leaky

  1041. 

  1042. [convolutional]

  1043. size=1

  1044. stride=1

  1045. pad=1

  1046. filters=18

  1047. activation=linear

  1048. 

  1049. 

  1050. [yolo]

  1051. mask = 3,4,5

  1052. anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401

  1053. classes=1

  1054. num=9

  1055. jitter=.3

  1056. ignore_thresh = .7

  1057. truth_thresh = 1

  1058. random=1

  1059. scale_x_y = 1.1

  1060. iou_thresh=0.213

  1061. cls_normalizer=1.0

  1062. iou_normalizer=0.07

  1063. iou_loss=ciou

  1064. nms_kind=greedynms

  1065. beta_nms=0.6

  1066. 

  1067. [route]

  1068. layers = -4

  1069. 

  1070. [convolutional]

  1071. batch_normalize=1

  1072. size=3

  1073. stride=2

  1074. pad=1

  1075. filters=512

  1076. activation=leaky

  1077. 

  1078. [route]

  1079. layers = -1, -37

  1080. 

  1081. [convolutional]

  1082. batch_normalize=1

  1083. filters=512

  1084. size=1

  1085. stride=1

  1086. pad=1

  1087. activation=leaky

  1088. 

  1089. [convolutional]

  1090. batch_normalize=1

  1091. size=3

  1092. stride=1

  1093. pad=1

  1094. filters=1024

  1095. activation=leaky

  1096. 

  1097. [convolutional]

  1098. batch_normalize=1

  1099. filters=512

  1100. size=1

  1101. stride=1

  1102. pad=1

  1103. activation=leaky

  1104. 

  1105. [convolutional]

  1106. batch_normalize=1

  1107. size=3

  1108. stride=1

  1109. pad=1

  1110. filters=1024

  1111. activation=leaky

  1112. 

  1113. [convolutional]

  1114. batch_normalize=1

  1115. filters=512

  1116. size=1

  1117. stride=1

  1118. pad=1

  1119. activation=leaky

  1120. 

  1121. [convolutional]

  1122. batch_normalize=1

  1123. size=3

  1124. stride=1

  1125. pad=1

  1126. filters=1024

  1127. activation=leaky

  1128. 

  1129. [convolutional]

  1130. size=1

  1131. stride=1

  1132. pad=1

  1133. filters=18

  1134. activation=linear

  1135. 

  1136. 

  1137. [yolo]

  1138. mask = 6,7,8

  1139. anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401

  1140. classes=1

  1141. num=9

  1142. jitter=.3

  1143. ignore_thresh = .7

  1144. truth_thresh = 1

  1145. random=1

  1146. scale_x_y = 1.05

  1147. iou_thresh=0.213

  1148. cls_normalizer=1.0

  1149. iou_normalizer=0.07

  1150. iou_loss=ciou

  1151. nms_kind=greedynms

  1152. beta_nms=0.6