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基于官方YOLOv4开发构建目标检测模型超详细实战教程【以自建缺陷检测数据集为例】_yolov4 模型文件

作者：盐析白兔 | 2024-05-15 13:20:01

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yolov4 模型文件

本文是关于基于YOLOv4开发构建目标检测模型的超详细实战教程，超详细实战教程相关的博文在前文有相应的系列，感兴趣的话可以自行移步阅读即可：
《基于yolov7开发实践实例分割模型超详细教程》

《YOLOv7基于自己的数据集从零构建模型完整训练、推理计算超详细教程》

《DETR (DEtection TRansformer)基于自建数据集开发构建目标检测模型超详细教程》

《基于yolov5-v7.0开发实践实例分割模型超详细教程》

《轻量级模型YOLOv5-Lite基于自己的数据集【焊接质量检测】从零构建模型超详细教程》

《轻量级模型NanoDet基于自己的数据集【接打电话检测】从零构建模型超详细教程》

《基于YOLOv5-v6.2全新版本模型构建自己的图像识别模型超详细教程》

《基于自建数据集【海底生物检测】使用YOLOv5-v6.1/2版本构建目标检测模型超详细教程》

 《超轻量级目标检测模型Yolo-FastestV2基于自建数据集【手写汉字检测】构建模型训练、推理完整流程超详细教程》

《基于YOLOv8开发构建目标检测模型超详细教程【以焊缝质量检测数据场景为例】》

最早期接触v3和v4的时候印象中模型的训练方式都是基于Darknet框架开发构建的，模型都是通过cfg文件进行配置的，从v5开始才全面转向了PyTorch形式的项目，延续到了现在。

yolov4.cfg如下：

  1. [net]
  2. batch=64
  3. subdivisions=8
  4. # Training
  5. #width=512
  6. #height=512
  7. width=608
  8. height=608
  9. channels=3
  10. momentum=0.949
  11. decay=0.0005
  12. angle=0
  13. saturation = 1.5
  14. exposure = 1.5
  15. hue=.1
  16.  
  17. learning_rate=0.0013
  18. burn_in=1000
  19. max_batches = 500500
  20. policy=steps
  21. steps=400000,450000
  22. scales=.1,.1
  23.  
  24. #cutmix=1
  25. mosaic=1
  26.  
  27. #:104x104 54:52x52 85:26x26 104:13x13 for 416
  28.  
  29. [convolutional]
  30. batch_normalize=1
  31. filters=32
  32. size=3
  33. stride=1
  34. pad=1
  35. activation=mish
  36.  
  37. # Downsample
  38.  
  39. [convolutional]
  40. batch_normalize=1
  41. filters=64
  42. size=3
  43. stride=2
  44. pad=1
  45. activation=mish
  46.  
  47. [convolutional]
  48. batch_normalize=1
  49. filters=64
  50. size=1
  51. stride=1
  52. pad=1
  53. activation=mish
  54.  
  55. [route]
  56. layers = -2
  57.  
  58. [convolutional]
  59. batch_normalize=1
  60. filters=64
  61. size=1
  62. stride=1
  63. pad=1
  64. activation=mish
  65.  
  66. [convolutional]
  67. batch_normalize=1
  68. filters=32
  69. size=1
  70. stride=1
  71. pad=1
  72. activation=mish
  73.  
  74. [convolutional]
  75. batch_normalize=1
  76. filters=64
  77. size=3
  78. stride=1
  79. pad=1
  80. activation=mish
  81.  
  82. [shortcut]
  83. from=-3
  84. activation=linear
  85.  
  86. [convolutional]
  87. batch_normalize=1
  88. filters=64
  89. size=1
  90. stride=1
  91. pad=1
  92. activation=mish
  93.  
  94. [route]
  95. layers = -1,-7
  96.  
  97. [convolutional]
  98. batch_normalize=1
  99. filters=64
  100. size=1
  101. stride=1
  102. pad=1
  103. activation=mish
  104.  
  105. # Downsample
  106.  
  107. [convolutional]
  108. batch_normalize=1
  109. filters=128
  110. size=3
  111. stride=2
  112. pad=1
  113. activation=mish
  114.  
  115. [convolutional]
  116. batch_normalize=1
  117. filters=64
  118. size=1
  119. stride=1
  120. pad=1
  121. activation=mish
  122.  
  123. [route]
  124. layers = -2
  125.  
  126. [convolutional]
  127. batch_normalize=1
  128. filters=64
  129. size=1
  130. stride=1
  131. pad=1
  132. activation=mish
  133.  
  134. [convolutional]
  135. batch_normalize=1
  136. filters=64
  137. size=1
  138. stride=1
  139. pad=1
  140. activation=mish
  141.  
  142. [convolutional]
  143. batch_normalize=1
  144. filters=64
  145. size=3
  146. stride=1
  147. pad=1
  148. activation=mish
  149.  
  150. [shortcut]
  151. from=-3
  152. activation=linear
  153.  
  154. [convolutional]
  155. batch_normalize=1
  156. filters=64
  157. size=1
  158. stride=1
  159. pad=1
  160. activation=mish
  161.  
  162. [convolutional]
  163. batch_normalize=1
  164. filters=64
  165. size=3
  166. stride=1
  167. pad=1
  168. activation=mish
  169.  
  170. [shortcut]
  171. from=-3
  172. activation=linear
  173.  
  174. [convolutional]
  175. batch_normalize=1
  176. filters=64
  177. size=1
  178. stride=1
  179. pad=1
  180. activation=mish
  181.  
  182. [route]
  183. layers = -1,-10
  184.  
  185. [convolutional]
  186. batch_normalize=1
  187. filters=128
  188. size=1
  189. stride=1
  190. pad=1
  191. activation=mish
  192.  
  193. # Downsample
  194.  
  195. [convolutional]
  196. batch_normalize=1
  197. filters=256
  198. size=3
  199. stride=2
  200. pad=1
  201. activation=mish
  202.  
  203. [convolutional]
  204. batch_normalize=1
  205. filters=128
  206. size=1
  207. stride=1
  208. pad=1
  209. activation=mish
  210.  
  211. [route]
  212. layers = -2
  213.  
  214. [convolutional]
  215. batch_normalize=1
  216. filters=128
  217. size=1
  218. stride=1
  219. pad=1
  220. activation=mish
  221.  
  222. [convolutional]
  223. batch_normalize=1
  224. filters=128
  225. size=1
  226. stride=1
  227. pad=1
  228. activation=mish
  229.  
  230. [convolutional]
  231. batch_normalize=1
  232. filters=128
  233. size=3
  234. stride=1
  235. pad=1
  236. activation=mish
  237.  
  238. [shortcut]
  239. from=-3
  240. activation=linear
  241.  
  242. [convolutional]
  243. batch_normalize=1
  244. filters=128
  245. size=1
  246. stride=1
  247. pad=1
  248. activation=mish
  249.  
  250. [convolutional]
  251. batch_normalize=1
  252. filters=128
  253. size=3
  254. stride=1
  255. pad=1
  256. activation=mish
  257.  
  258. [shortcut]
  259. from=-3
  260. activation=linear
  261.  
  262. [convolutional]
  263. batch_normalize=1
  264. filters=128
  265. size=1
  266. stride=1
  267. pad=1
  268. activation=mish
  269.  
  270. [convolutional]
  271. batch_normalize=1
  272. filters=128
  273. size=3
  274. stride=1
  275. pad=1
  276. activation=mish
  277.  
  278. [shortcut]
  279. from=-3
  280. activation=linear
  281.  
  282. [convolutional]
  283. batch_normalize=1
  284. filters=128
  285. size=1
  286. stride=1
  287. pad=1
  288. activation=mish
  289.  
  290. [convolutional]
  291. batch_normalize=1
  292. filters=128
  293. size=3
  294. stride=1
  295. pad=1
  296. activation=mish
  297.  
  298. [shortcut]
  299. from=-3
  300. activation=linear
  301.  
  302.  
  303. [convolutional]
  304. batch_normalize=1
  305. filters=128
  306. size=1
  307. stride=1
  308. pad=1
  309. activation=mish
  310.  
  311. [convolutional]
  312. batch_normalize=1
  313. filters=128
  314. size=3
  315. stride=1
  316. pad=1
  317. activation=mish
  318.  
  319. [shortcut]
  320. from=-3
  321. activation=linear
  322.  
  323. [convolutional]
  324. batch_normalize=1
  325. filters=128
  326. size=1
  327. stride=1
  328. pad=1
  329. activation=mish
  330.  
  331. [convolutional]
  332. batch_normalize=1
  333. filters=128
  334. size=3
  335. stride=1
  336. pad=1
  337. activation=mish
  338.  
  339. [shortcut]
  340. from=-3
  341. activation=linear
  342.  
  343. [convolutional]
  344. batch_normalize=1
  345. filters=128
  346. size=1
  347. stride=1
  348. pad=1
  349. activation=mish
  350.  
  351. [convolutional]
  352. batch_normalize=1
  353. filters=128
  354. size=3
  355. stride=1
  356. pad=1
  357. activation=mish
  358.  
  359. [shortcut]
  360. from=-3
  361. activation=linear
  362.  
  363. [convolutional]
  364. batch_normalize=1
  365. filters=128
  366. size=1
  367. stride=1
  368. pad=1
  369. activation=mish
  370.  
  371. [convolutional]
  372. batch_normalize=1
  373. filters=128
  374. size=3
  375. stride=1
  376. pad=1
  377. activation=mish
  378.  
  379. [shortcut]
  380. from=-3
  381. activation=linear
  382.  
  383. [convolutional]
  384. batch_normalize=1
  385. filters=128
  386. size=1
  387. stride=1
  388. pad=1
  389. activation=mish
  390.  
  391. [route]
  392. layers = -1,-28
  393.  
  394. [convolutional]
  395. batch_normalize=1
  396. filters=256
  397. size=1
  398. stride=1
  399. pad=1
  400. activation=mish
  401.  
  402. # Downsample
  403.  
  404. [convolutional]
  405. batch_normalize=1
  406. filters=512
  407. size=3
  408. stride=2
  409. pad=1
  410. activation=mish
  411.  
  412. [convolutional]
  413. batch_normalize=1
  414. filters=256
  415. size=1
  416. stride=1
  417. pad=1
  418. activation=mish
  419.  
  420. [route]
  421. layers = -2
  422.  
  423. [convolutional]
  424. batch_normalize=1
  425. filters=256
  426. size=1
  427. stride=1
  428. pad=1
  429. activation=mish
  430.  
  431. [convolutional]
  432. batch_normalize=1
  433. filters=256
  434. size=1
  435. stride=1
  436. pad=1
  437. activation=mish
  438.  
  439. [convolutional]
  440. batch_normalize=1
  441. filters=256
  442. size=3
  443. stride=1
  444. pad=1
  445. activation=mish
  446.  
  447. [shortcut]
  448. from=-3
  449. activation=linear
  450.  
  451.  
  452. [convolutional]
  453. batch_normalize=1
  454. filters=256
  455. size=1
  456. stride=1
  457. pad=1
  458. activation=mish
  459.  
  460. [convolutional]
  461. batch_normalize=1
  462. filters=256
  463. size=3
  464. stride=1
  465. pad=1
  466. activation=mish
  467.  
  468. [shortcut]
  469. from=-3
  470. activation=linear
  471.  
  472.  
  473. [convolutional]
  474. batch_normalize=1
  475. filters=256
  476. size=1
  477. stride=1
  478. pad=1
  479. activation=mish
  480.  
  481. [convolutional]
  482. batch_normalize=1
  483. filters=256
  484. size=3
  485. stride=1
  486. pad=1
  487. activation=mish
  488.  
  489. [shortcut]
  490. from=-3
  491. activation=linear
  492.  
  493.  
  494. [convolutional]
  495. batch_normalize=1
  496. filters=256
  497. size=1
  498. stride=1
  499. pad=1
  500. activation=mish
  501.  
  502. [convolutional]
  503. batch_normalize=1
  504. filters=256
  505. size=3
  506. stride=1
  507. pad=1
  508. activation=mish
  509.  
  510. [shortcut]
  511. from=-3
  512. activation=linear
  513.  
  514.  
  515. [convolutional]
  516. batch_normalize=1
  517. filters=256
  518. size=1
  519. stride=1
  520. pad=1
  521. activation=mish
  522.  
  523. [convolutional]
  524. batch_normalize=1
  525. filters=256
  526. size=3
  527. stride=1
  528. pad=1
  529. activation=mish
  530.  
  531. [shortcut]
  532. from=-3
  533. activation=linear
  534.  
  535.  
  536. [convolutional]
  537. batch_normalize=1
  538. filters=256
  539. size=1
  540. stride=1
  541. pad=1
  542. activation=mish
  543.  
  544. [convolutional]
  545. batch_normalize=1
  546. filters=256
  547. size=3
  548. stride=1
  549. pad=1
  550. activation=mish
  551.  
  552. [shortcut]
  553. from=-3
  554. activation=linear
  555.  
  556.  
  557. [convolutional]
  558. batch_normalize=1
  559. filters=256
  560. size=1
  561. stride=1
  562. pad=1
  563. activation=mish
  564.  
  565. [convolutional]
  566. batch_normalize=1
  567. filters=256
  568. size=3
  569. stride=1
  570. pad=1
  571. activation=mish
  572.  
  573. [shortcut]
  574. from=-3
  575. activation=linear
  576.  
  577. [convolutional]
  578. batch_normalize=1
  579. filters=256
  580. size=1
  581. stride=1
  582. pad=1
  583. activation=mish
  584.  
  585. [convolutional]
  586. batch_normalize=1
  587. filters=256
  588. size=3
  589. stride=1
  590. pad=1
  591. activation=mish
  592.  
  593. [shortcut]
  594. from=-3
  595. activation=linear
  596.  
  597. [convolutional]
  598. batch_normalize=1
  599. filters=256
  600. size=1
  601. stride=1
  602. pad=1
  603. activation=mish
  604.  
  605. [route]
  606. layers = -1,-28
  607.  
  608. [convolutional]
  609. batch_normalize=1
  610. filters=512
  611. size=1
  612. stride=1
  613. pad=1
  614. activation=mish
  615.  
  616. # Downsample
  617.  
  618. [convolutional]
  619. batch_normalize=1
  620. filters=1024
  621. size=3
  622. stride=2
  623. pad=1
  624. activation=mish
  625.  
  626. [convolutional]
  627. batch_normalize=1
  628. filters=512
  629. size=1
  630. stride=1
  631. pad=1
  632. activation=mish
  633.  
  634. [route]
  635. layers = -2
  636.  
  637. [convolutional]
  638. batch_normalize=1
  639. filters=512
  640. size=1
  641. stride=1
  642. pad=1
  643. activation=mish
  644.  
  645. [convolutional]
  646. batch_normalize=1
  647. filters=512
  648. size=1
  649. stride=1
  650. pad=1
  651. activation=mish
  652.  
  653. [convolutional]
  654. batch_normalize=1
  655. filters=512
  656. size=3
  657. stride=1
  658. pad=1
  659. activation=mish
  660.  
  661. [shortcut]
  662. from=-3
  663. activation=linear
  664.  
  665. [convolutional]
  666. batch_normalize=1
  667. filters=512
  668. size=1
  669. stride=1
  670. pad=1
  671. activation=mish
  672.  
  673. [convolutional]
  674. batch_normalize=1
  675. filters=512
  676. size=3
  677. stride=1
  678. pad=1
  679. activation=mish
  680.  
  681. [shortcut]
  682. from=-3
  683. activation=linear
  684.  
  685. [convolutional]
  686. batch_normalize=1
  687. filters=512
  688. size=1
  689. stride=1
  690. pad=1
  691. activation=mish
  692.  
  693. [convolutional]
  694. batch_normalize=1
  695. filters=512
  696. size=3
  697. stride=1
  698. pad=1
  699. activation=mish
  700.  
  701. [shortcut]
  702. from=-3
  703. activation=linear
  704.  
  705. [convolutional]
  706. batch_normalize=1
  707. filters=512
  708. size=1
  709. stride=1
  710. pad=1
  711. activation=mish
  712.  
  713. [convolutional]
  714. batch_normalize=1
  715. filters=512
  716. size=3
  717. stride=1
  718. pad=1
  719. activation=mish
  720.  
  721. [shortcut]
  722. from=-3
  723. activation=linear
  724.  
  725. [convolutional]
  726. batch_normalize=1
  727. filters=512
  728. size=1
  729. stride=1
  730. pad=1
  731. activation=mish
  732.  
  733. [route]
  734. layers = -1,-16
  735.  
  736. [convolutional]
  737. batch_normalize=1
  738. filters=1024
  739. size=1
  740. stride=1
  741. pad=1
  742. activation=mish
  743.  
  744. ##########################
  745.  
  746. [convolutional]
  747. batch_normalize=1
  748. filters=512
  749. size=1
  750. stride=1
  751. pad=1
  752. activation=leaky
  753.  
  754. [convolutional]
  755. batch_normalize=1
  756. size=3
  757. stride=1
  758. pad=1
  759. filters=1024
  760. activation=leaky
  761.  
  762. [convolutional]
  763. batch_normalize=1
  764. filters=512
  765. size=1
  766. stride=1
  767. pad=1
  768. activation=leaky
  769.  
  770. ### SPP ###
  771. [maxpool]
  772. stride=1
  773. size=5
  774.  
  775. [route]
  776. layers=-2
  777.  
  778. [maxpool]
  779. stride=1
  780. size=9
  781.  
  782. [route]
  783. layers=-4
  784.  
  785. [maxpool]
  786. stride=1
  787. size=13
  788.  
  789. [route]
  790. layers=-1,-3,-5,-6
  791. ### End SPP ###
  792.  
  793. [convolutional]
  794. batch_normalize=1
  795. filters=512
  796. size=1
  797. stride=1
  798. pad=1
  799. activation=leaky
  800.  
  801. [convolutional]
  802. batch_normalize=1
  803. size=3
  804. stride=1
  805. pad=1
  806. filters=1024
  807. activation=leaky
  808.  
  809. [convolutional]
  810. batch_normalize=1
  811. filters=512
  812. size=1
  813. stride=1
  814. pad=1
  815. activation=leaky
  816.  
  817. [convolutional]
  818. batch_normalize=1
  819. filters=256
  820. size=1
  821. stride=1
  822. pad=1
  823. activation=leaky
  824.  
  825. [upsample]
  826. stride=2
  827.  
  828. [route]
  829. layers = 85
  830.  
  831. [convolutional]
  832. batch_normalize=1
  833. filters=256
  834. size=1
  835. stride=1
  836. pad=1
  837. activation=leaky
  838.  
  839. [route]
  840. layers = -1, -3
  841.  
  842. [convolutional]
  843. batch_normalize=1
  844. filters=256
  845. size=1
  846. stride=1
  847. pad=1
  848. activation=leaky
  849.  
  850. [convolutional]
  851. batch_normalize=1
  852. size=3
  853. stride=1
  854. pad=1
  855. filters=512
  856. activation=leaky
  857.  
  858. [convolutional]
  859. batch_normalize=1
  860. filters=256
  861. size=1
  862. stride=1
  863. pad=1
  864. activation=leaky
  865.  
  866. [convolutional]
  867. batch_normalize=1
  868. size=3
  869. stride=1
  870. pad=1
  871. filters=512
  872. activation=leaky
  873.  
  874. [convolutional]
  875. batch_normalize=1
  876. filters=256
  877. size=1
  878. stride=1
  879. pad=1
  880. activation=leaky
  881.  
  882. [convolutional]
  883. batch_normalize=1
  884. filters=128
  885. size=1
  886. stride=1
  887. pad=1
  888. activation=leaky
  889.  
  890. [upsample]
  891. stride=2
  892.  
  893. [route]
  894. layers = 54
  895.  
  896. [convolutional]
  897. batch_normalize=1
  898. filters=128
  899. size=1
  900. stride=1
  901. pad=1
  902. activation=leaky
  903.  
  904. [route]
  905. layers = -1, -3
  906.  
  907. [convolutional]
  908. batch_normalize=1
  909. filters=128
  910. size=1
  911. stride=1
  912. pad=1
  913. activation=leaky
  914.  
  915. [convolutional]
  916. batch_normalize=1
  917. size=3
  918. stride=1
  919. pad=1
  920. filters=256
  921. activation=leaky
  922.  
  923. [convolutional]
  924. batch_normalize=1
  925. filters=128
  926. size=1
  927. stride=1
  928. pad=1
  929. activation=leaky
  930.  
  931. [convolutional]
  932. batch_normalize=1
  933. size=3
  934. stride=1
  935. pad=1
  936. filters=256
  937. activation=leaky
  938.  
  939. [convolutional]
  940. batch_normalize=1
  941. filters=128
  942. size=1
  943. stride=1
  944. pad=1
  945. activation=leaky
  946.  
  947. ##########################
  948.  
  949. [convolutional]
  950. batch_normalize=1
  951. size=3
  952. stride=1
  953. pad=1
  954. filters=256
  955. activation=leaky
  956.  
  957. [convolutional]
  958. size=1
  959. stride=1
  960. pad=1
  961. filters=255
  962. activation=linear
  963.  
  964.  
  965. [yolo]
  966. mask = 0,1,2
  967. anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
  968. classes=80
  969. num=9
  970. jitter=.3
  971. ignore_thresh = .7
  972. truth_thresh = 1
  973. scale_x_y = 1.2
  974. iou_thresh=0.213
  975. cls_normalizer=1.0
  976. iou_normalizer=0.07
  977. iou_loss=ciou
  978. nms_kind=greedynms
  979. beta_nms=0.6
  980. max_delta=5
  981.  
  982.  
  983. [route]
  984. layers = -4
  985.  
  986. [convolutional]
  987. batch_normalize=1
  988. size=3
  989. stride=2
  990. pad=1
  991. filters=256
  992. activation=leaky
  993.  
  994. [route]
  995. layers = -1, -16
  996.  
  997. [convolutional]
  998. batch_normalize=1
  999. filters=256
  1000. size=1
  1001. stride=1
  1002. pad=1
  1003. activation=leaky
  1004.  
  1005. [convolutional]
  1006. batch_normalize=1
  1007. size=3
  1008. stride=1
  1009. pad=1
  1010. filters=512
  1011. activation=leaky
  1012.  
  1013. [convolutional]
  1014. batch_normalize=1
  1015. filters=256
  1016. size=1
  1017. stride=1
  1018. pad=1
  1019. activation=leaky
  1020.  
  1021. [convolutional]
  1022. batch_normalize=1
  1023. size=3
  1024. stride=1
  1025. pad=1
  1026. filters=512
  1027. activation=leaky
  1028.  
  1029. [convolutional]
  1030. batch_normalize=1
  1031. filters=256
  1032. size=1
  1033. stride=1
  1034. pad=1
  1035. activation=leaky
  1036.  
  1037. [convolutional]
  1038. batch_normalize=1
  1039. size=3
  1040. stride=1
  1041. pad=1
  1042. filters=512
  1043. activation=leaky
  1044.  
  1045. [convolutional]
  1046. size=1
  1047. stride=1
  1048. pad=1
  1049. filters=255
  1050. activation=linear
  1051.  
  1052.  
  1053. [yolo]
  1054. mask = 3,4,5
  1055. anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
  1056. classes=80
  1057. num=9
  1058. jitter=.3
  1059. ignore_thresh = .7
  1060. truth_thresh = 1
  1061. scale_x_y = 1.1
  1062. iou_thresh=0.213
  1063. cls_normalizer=1.0
  1064. iou_normalizer=0.07
  1065. iou_loss=ciou
  1066. nms_kind=greedynms
  1067. beta_nms=0.6
  1068. max_delta=5
  1069.  
  1070.  
  1071. [route]
  1072. layers = -4
  1073.  
  1074. [convolutional]
  1075. batch_normalize=1
  1076. size=3
  1077. stride=2
  1078. pad=1
  1079. filters=512
  1080. activation=leaky
  1081.  
  1082. [route]
  1083. layers = -1, -37
  1084.  
  1085. [convolutional]
  1086. batch_normalize=1
  1087. filters=512
  1088. size=1
  1089. stride=1
  1090. pad=1
  1091. activation=leaky
  1092.  
  1093. [convolutional]
  1094. batch_normalize=1
  1095. size=3
  1096. stride=1
  1097. pad=1
  1098. filters=1024
  1099. activation=leaky
  1100.  
  1101. [convolutional]
  1102. batch_normalize=1
  1103. filters=512
  1104. size=1
  1105. stride=1
  1106. pad=1
  1107. activation=leaky
  1108.  
  1109. [convolutional]
  1110. batch_normalize=1
  1111. size=3
  1112. stride=1
  1113. pad=1
  1114. filters=1024
  1115. activation=leaky
  1116.  
  1117. [convolutional]
  1118. batch_normalize=1
  1119. filters=512
  1120. size=1
  1121. stride=1
  1122. pad=1
  1123. activation=leaky
  1124.  
  1125. [convolutional]
  1126. batch_normalize=1
  1127. size=3
  1128. stride=1
  1129. pad=1
  1130. filters=1024
  1131. activation=leaky
  1132.  
  1133. [convolutional]
  1134. size=1
  1135. stride=1
  1136. pad=1
  1137. filters=255
  1138. activation=linear
  1139.  
  1140.  
  1141. [yolo]
  1142. mask = 6,7,8
  1143. anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
  1144. classes=80
  1145. num=9
  1146. jitter=.3
  1147. ignore_thresh = .7
  1148. truth_thresh = 1
  1149. random=1
  1150. scale_x_y = 1.05
  1151. iou_thresh=0.213
  1152. cls_normalizer=1.0
  1153. iou_normalizer=0.07
  1154. iou_loss=ciou
  1155. nms_kind=greedynms
  1156. beta_nms=0.6
  1157. max_delta=5

yolov4-tiny.cfg如下：

  1. [net]
  2. # Testing
  3. #batch=1
  4. #subdivisions=1
  5. # Training
  6. batch=64
  7. subdivisions=1
  8. width=416
  9. height=416
  10. channels=3
  11. momentum=0.9
  12. decay=0.0005
  13. angle=0
  14. saturation = 1.5
  15. exposure = 1.5
  16. hue=.1
  17.  
  18. learning_rate=0.00261
  19. burn_in=1000
  20. max_batches = 500200
  21. policy=steps
  22. steps=400000,450000
  23. scales=.1,.1
  24.  
  25. [convolutional]
  26. batch_normalize=1
  27. filters=32
  28. size=3
  29. stride=2
  30. pad=1
  31. activation=leaky
  32.  
  33. [convolutional]
  34. batch_normalize=1
  35. filters=64
  36. size=3
  37. stride=2
  38. pad=1
  39. activation=leaky
  40.  
  41. [convolutional]
  42. batch_normalize=1
  43. filters=64
  44. size=3
  45. stride=1
  46. pad=1
  47. activation=leaky
  48.  
  49. [route]
  50. layers=-1
  51. groups=2
  52. group_id=1
  53.  
  54. [convolutional]
  55. batch_normalize=1
  56. filters=32
  57. size=3
  58. stride=1
  59. pad=1
  60. activation=leaky
  61.  
  62. [convolutional]
  63. batch_normalize=1
  64. filters=32
  65. size=3
  66. stride=1
  67. pad=1
  68. activation=leaky
  69.  
  70. [route]
  71. layers = -1,-2
  72.  
  73. [convolutional]
  74. batch_normalize=1
  75. filters=64
  76. size=1
  77. stride=1
  78. pad=1
  79. activation=leaky
  80.  
  81. [route]
  82. layers = -6,-1
  83.  
  84. [maxpool]
  85. size=2
  86. stride=2
  87.  
  88. [convolutional]
  89. batch_normalize=1
  90. filters=128
  91. size=3
  92. stride=1
  93. pad=1
  94. activation=leaky
  95.  
  96. [route]
  97. layers=-1
  98. groups=2
  99. group_id=1
  100.  
  101. [convolutional]
  102. batch_normalize=1
  103. filters=64
  104. size=3
  105. stride=1
  106. pad=1
  107. activation=leaky
  108.  
  109. [convolutional]
  110. batch_normalize=1
  111. filters=64
  112. size=3
  113. stride=1
  114. pad=1
  115. activation=leaky
  116.  
  117. [route]
  118. layers = -1,-2
  119.  
  120. [convolutional]
  121. batch_normalize=1
  122. filters=128
  123. size=1
  124. stride=1
  125. pad=1
  126. activation=leaky
  127.  
  128. [route]
  129. layers = -6,-1
  130.  
  131. [maxpool]
  132. size=2
  133. stride=2
  134.  
  135. [convolutional]
  136. batch_normalize=1
  137. filters=256
  138. size=3
  139. stride=1
  140. pad=1
  141. activation=leaky
  142.  
  143. [route]
  144. layers=-1
  145. groups=2
  146. group_id=1
  147.  
  148. [convolutional]
  149. batch_normalize=1
  150. filters=128
  151. size=3
  152. stride=1
  153. pad=1
  154. activation=leaky
  155.  
  156. [convolutional]
  157. batch_normalize=1
  158. filters=128
  159. size=3
  160. stride=1
  161. pad=1
  162. activation=leaky
  163.  
  164. [route]
  165. layers = -1,-2
  166.  
  167. [convolutional]
  168. batch_normalize=1
  169. filters=256
  170. size=1
  171. stride=1
  172. pad=1
  173. activation=leaky
  174.  
  175. [route]
  176. layers = -6,-1
  177.  
  178. [maxpool]
  179. size=2
  180. stride=2
  181.  
  182. [convolutional]
  183. batch_normalize=1
  184. filters=512
  185. size=3
  186. stride=1
  187. pad=1
  188. activation=leaky
  189.  
  190. ##################################
  191.  
  192. [convolutional]
  193. batch_normalize=1
  194. filters=256
  195. size=1
  196. stride=1
  197. pad=1
  198. activation=leaky
  199.  
  200. [convolutional]
  201. batch_normalize=1
  202. filters=512
  203. size=3
  204. stride=1
  205. pad=1
  206. activation=leaky
  207.  
  208. [convolutional]
  209. size=1
  210. stride=1
  211. pad=1
  212. filters=255
  213. activation=linear
  214.  
  215.  
  216.  
  217. [yolo]
  218. mask = 3,4,5
  219. anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
  220. classes=80
  221. num=6
  222. jitter=.3
  223. scale_x_y = 1.05
  224. cls_normalizer=1.0
  225. iou_normalizer=0.07
  226. iou_loss=ciou
  227. ignore_thresh = .7
  228. truth_thresh = 1
  229. random=0
  230. resize=1.5
  231. nms_kind=greedynms
  232. beta_nms=0.6
  233.  
  234. [route]
  235. layers = -4
  236.  
  237. [convolutional]
  238. batch_normalize=1
  239. filters=128
  240. size=1
  241. stride=1
  242. pad=1
  243. activation=leaky
  244.  
  245. [upsample]
  246. stride=2
  247.  
  248. [route]
  249. layers = -1, 23
  250.  
  251. [convolutional]
  252. batch_normalize=1
  253. filters=256
  254. size=3
  255. stride=1
  256. pad=1
  257. activation=leaky
  258.  
  259. [convolutional]
  260. size=1
  261. stride=1
  262. pad=1
  263. filters=255
  264. activation=linear
  265.  
  266. [yolo]
  267. mask = 1,2,3
  268. anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
  269. classes=80
  270. num=6
  271. jitter=.3
  272. scale_x_y = 1.05
  273. cls_normalizer=1.0
  274. iou_normalizer=0.07
  275. iou_loss=ciou
  276. ignore_thresh = .7
  277. truth_thresh = 1
  278. random=0
  279. resize=1.5
  280. nms_kind=greedynms
  281. beta_nms=0.6

最开始的时候还是蛮喜欢这种形式的，非常的简洁，直接使用Darknet框架训练也很方便，到后面随着模型改进各种组件的替换，Darknet变得越发不适用了。YOLOv4的话感觉定位相比于v3和v5来说比较尴尬一些，git里面搜索yolov4，结果如下所示：

排名第一的项目是pytorch-YOLOv4，地址在这里，如下所示：

从说明里面来看，这个只是一个minimal的实现：

官方的实现应该是：

仔细看的话会发现，官方这里提供了YOLOv3风格的实现项目以及YOLOv5风格的实现项目，本文主要是以YOLOv3风格的YOLOv4项目为基准来讲解完整的实践流程，项目地址在这里，如下所示：

首先下载所需要的项目，如下：

下载到本地解压缩后，如下所示：

网上直接百度下载这两个weights文件放在weights目录下，如下所示：

然后随便复制过来一个自己之前yolov5项目的数据集放在当前项目目录下，我是前面刚好基于yolov5做了钢铁缺陷检测项目，数据集可以直接拿来用，如果没有现成的数据集的话可以看我签名yolov5的超详细教程里面可以按照步骤自己创建数据集即可。如下所示：

这里我选择的是基于yolov4-tiny版本的模型来进行开发训练，为的就是计算速度能够更快一些。

修改train.py里面的内容，如下所示：

  1. parser = argparse.ArgumentParser()
  2. parser.add_argument('--weights', type=str, default='weights/yolov4-tiny.weights', help='initial weights path')
  3. parser.add_argument('--cfg', type=str, default='cfg/yolov4-tiny.cfg', help='model.yaml path')
  4. parser.add_argument('--data', type=str, default='data/self.yaml', help='data.yaml path')
  5. parser.add_argument('--hyp', type=str, default='data/hyp.scratch.yaml', help='hyperparameters path')
  6. parser.add_argument('--epochs', type=int, default=100)
  7. parser.add_argument('--batch-size', type=int, default=8, help='total batch size for all GPUs')
  8. parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='[train, test] image sizes')
  9. parser.add_argument('--rect', action='store_true', help='rectangular training')
  10. parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')
  11. parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
  12. parser.add_argument('--notest', action='store_true', help='only test final epoch')
  13. parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check')
  14. parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters')
  15. parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
  16. parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')
  17. parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')
  18. parser.add_argument('--device', default='0', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
  19. parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')
  20. parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
  21. parser.add_argument('--adam', action='store_true', help='use torch.optim.Adam() optimizer')
  22. parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')
  23. parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify')
  24. parser.add_argument('--log-imgs', type=int, default=16, help='number of images for W&B logging, max 100')
  25. parser.add_argument('--workers', type=int, default=8, help='maximum number of dataloader workers')
  26. parser.add_argument('--project', default='runs/train', help='save to project/name')
  27. parser.add_argument('--name', default='exp', help='save to project/name')
  28. parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
  29. opt = parser.parse_args()

终端直接执行：

python train.py

即可。

当然也可以选择基于参数指定的形式启动，如下：

python train.py --device 0 --batch-size 16 --img 640 640 --data self.yaml --cfg cfg/yolov4-tiny.cfg --weights 'weights/yolov4-tiny.weights' --name yolov4-tiny

根据个人喜好来选择即可。

启动训练终端输出如下所示：

训练完成截图如下所示：

 训练完成我们来看下结果文件，如下所示：

可以看到：结果文件直观来看跟yolov5项目差距还是很大的，评估指标只有一个PR图，所以如果是做论文的话最好还是使用yolov5来做会好点。

PR曲线如下所示：

训练可视化如下所示：

LABEL数据可视化如下所示：

weights目录如下所示：

这个跟yolov5项目差异也是很大的，yolov5项目只有两个pt文件，一个是最优的一个是最新的，但是yolov4项目居然产生了19个文件，保存的可以说是非常详细了有点像yolov7，但是比v7维度更多一些。

感兴趣的话都可以按照我上面的教程步骤开发构建自己的目标检测模型。

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