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traffic-AI/resources/trainedModels/yolov3_number_plate_detection/darknet-yolov3.cfg

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  1. [net]

  2. # Testing

  3. # batch=1

  4. # subdivisions=1

  5. # Training

  6. batch=64

  7. subdivisions=16

  8. width=608

  9. height=608

  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=0.001

  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=1

  30. pad=1

  31. activation=leaky

  32. 

  33. # Downsample

  34. 

  35. [convolutional]

  36. batch_normalize=1

  37. filters=64

  38. size=3

  39. stride=2

  40. pad=1

  41. activation=leaky

  42. 

  43. [convolutional]

  44. batch_normalize=1

  45. filters=32

  46. size=1

  47. stride=1

  48. pad=1

  49. activation=leaky

  50. 

  51. [convolutional]

  52. batch_normalize=1

  53. filters=64

  54. size=3

  55. stride=1

  56. pad=1

  57. activation=leaky

  58. 

  59. [shortcut]

  60. from=-3

  61. activation=linear

  62. 

  63. # Downsample

  64. 

  65. [convolutional]

  66. batch_normalize=1

  67. filters=128

  68. size=3

  69. stride=2

  70. pad=1

  71. activation=leaky

  72. 

  73. [convolutional]

  74. batch_normalize=1

  75. filters=64

  76. size=1

  77. stride=1

  78. pad=1

  79. activation=leaky

  80. 

  81. [convolutional]

  82. batch_normalize=1

  83. filters=128

  84. size=3

  85. stride=1

  86. pad=1

  87. activation=leaky

  88. 

  89. [shortcut]

  90. from=-3

  91. activation=linear

  92. 

  93. [convolutional]

  94. batch_normalize=1

  95. filters=64

  96. size=1

  97. stride=1

  98. pad=1

  99. activation=leaky

  100. 

  101. [convolutional]

  102. batch_normalize=1

  103. filters=128

  104. size=3

  105. stride=1

  106. pad=1

  107. activation=leaky

  108. 

  109. [shortcut]

  110. from=-3

  111. activation=linear

  112. 

  113. # Downsample

  114. 

  115. [convolutional]

  116. batch_normalize=1

  117. filters=256

  118. size=3

  119. stride=2

  120. pad=1

  121. activation=leaky

  122. 

  123. [convolutional]

  124. batch_normalize=1

  125. filters=128

  126. size=1

  127. stride=1

  128. pad=1

  129. activation=leaky

  130. 

  131. [convolutional]

  132. batch_normalize=1

  133. filters=256

  134. size=3

  135. stride=1

  136. pad=1

  137. activation=leaky

  138. 

  139. [shortcut]

  140. from=-3

  141. activation=linear

  142. 

  143. [convolutional]

  144. batch_normalize=1

  145. filters=128

  146. size=1

  147. stride=1

  148. pad=1

  149. activation=leaky

  150. 

  151. [convolutional]

  152. batch_normalize=1

  153. filters=256

  154. size=3

  155. stride=1

  156. pad=1

  157. activation=leaky

  158. 

  159. [shortcut]

  160. from=-3

  161. activation=linear

  162. 

  163. [convolutional]

  164. batch_normalize=1

  165. filters=128

  166. size=1

  167. stride=1

  168. pad=1

  169. activation=leaky

  170. 

  171. [convolutional]

  172. batch_normalize=1

  173. filters=256

  174. size=3

  175. stride=1

  176. pad=1

  177. activation=leaky

  178. 

  179. [shortcut]

  180. from=-3

  181. activation=linear

  182. 

  183. [convolutional]

  184. batch_normalize=1

  185. filters=128

  186. size=1

  187. stride=1

  188. pad=1

  189. activation=leaky

  190. 

  191. [convolutional]

  192. batch_normalize=1

  193. filters=256

  194. size=3

  195. stride=1

  196. pad=1

  197. activation=leaky

  198. 

  199. [shortcut]

  200. from=-3

  201. activation=linear

  202. 

  203. 

  204. [convolutional]

  205. batch_normalize=1

  206. filters=128

  207. size=1

  208. stride=1

  209. pad=1

  210. activation=leaky

  211. 

  212. [convolutional]

  213. batch_normalize=1

  214. filters=256

  215. size=3

  216. stride=1

  217. pad=1

  218. activation=leaky

  219. 

  220. [shortcut]

  221. from=-3

  222. activation=linear

  223. 

  224. [convolutional]

  225. batch_normalize=1

  226. filters=128

  227. size=1

  228. stride=1

  229. pad=1

  230. activation=leaky

  231. 

  232. [convolutional]

  233. batch_normalize=1

  234. filters=256

  235. size=3

  236. stride=1

  237. pad=1

  238. activation=leaky

  239. 

  240. [shortcut]

  241. from=-3

  242. activation=linear

  243. 

  244. [convolutional]

  245. batch_normalize=1

  246. filters=128

  247. size=1

  248. stride=1

  249. pad=1

  250. activation=leaky

  251. 

  252. [convolutional]

  253. batch_normalize=1

  254. filters=256

  255. size=3

  256. stride=1

  257. pad=1

  258. activation=leaky

  259. 

  260. [shortcut]

  261. from=-3

  262. activation=linear

  263. 

  264. [convolutional]

  265. batch_normalize=1

  266. filters=128

  267. size=1

  268. stride=1

  269. pad=1

  270. activation=leaky

  271. 

  272. [convolutional]

  273. batch_normalize=1

  274. filters=256

  275. size=3

  276. stride=1

  277. pad=1

  278. activation=leaky

  279. 

  280. [shortcut]

  281. from=-3

  282. activation=linear

  283. 

  284. # Downsample

  285. 

  286. [convolutional]

  287. batch_normalize=1

  288. filters=512

  289. size=3

  290. stride=2

  291. pad=1

  292. activation=leaky

  293. 

  294. [convolutional]

  295. batch_normalize=1

  296. filters=256

  297. size=1

  298. stride=1

  299. pad=1

  300. activation=leaky

  301. 

  302. [convolutional]

  303. batch_normalize=1

  304. filters=512

  305. size=3

  306. stride=1

  307. pad=1

  308. activation=leaky

  309. 

  310. [shortcut]

  311. from=-3

  312. activation=linear

  313. 

  314. 

  315. [convolutional]

  316. batch_normalize=1

  317. filters=256

  318. size=1

  319. stride=1

  320. pad=1

  321. activation=leaky

  322. 

  323. [convolutional]

  324. batch_normalize=1

  325. filters=512

  326. size=3

  327. stride=1

  328. pad=1

  329. activation=leaky

  330. 

  331. [shortcut]

  332. from=-3

  333. activation=linear

  334. 

  335. 

  336. [convolutional]

  337. batch_normalize=1

  338. filters=256

  339. size=1

  340. stride=1

  341. pad=1

  342. activation=leaky

  343. 

  344. [convolutional]

  345. batch_normalize=1

  346. filters=512

  347. size=3

  348. stride=1

  349. pad=1

  350. activation=leaky

  351. 

  352. [shortcut]

  353. from=-3

  354. activation=linear

  355. 

  356. 

  357. [convolutional]

  358. batch_normalize=1

  359. filters=256

  360. size=1

  361. stride=1

  362. pad=1

  363. activation=leaky

  364. 

  365. [convolutional]

  366. batch_normalize=1

  367. filters=512

  368. size=3

  369. stride=1

  370. pad=1

  371. activation=leaky

  372. 

  373. [shortcut]

  374. from=-3

  375. activation=linear

  376. 

  377. [convolutional]

  378. batch_normalize=1

  379. filters=256

  380. size=1

  381. stride=1

  382. pad=1

  383. activation=leaky

  384. 

  385. [convolutional]

  386. batch_normalize=1

  387. filters=512

  388. size=3

  389. stride=1

  390. pad=1

  391. activation=leaky

  392. 

  393. [shortcut]

  394. from=-3

  395. activation=linear

  396. 

  397. 

  398. [convolutional]

  399. batch_normalize=1

  400. filters=256

  401. size=1

  402. stride=1

  403. pad=1

  404. activation=leaky

  405. 

  406. [convolutional]

  407. batch_normalize=1

  408. filters=512

  409. size=3

  410. stride=1

  411. pad=1

  412. activation=leaky

  413. 

  414. [shortcut]

  415. from=-3

  416. activation=linear

  417. 

  418. 

  419. [convolutional]

  420. batch_normalize=1

  421. filters=256

  422. size=1

  423. stride=1

  424. pad=1

  425. activation=leaky

  426. 

  427. [convolutional]

  428. batch_normalize=1

  429. filters=512

  430. size=3

  431. stride=1

  432. pad=1

  433. activation=leaky

  434. 

  435. [shortcut]

  436. from=-3

  437. activation=linear

  438. 

  439. [convolutional]

  440. batch_normalize=1

  441. filters=256

  442. size=1

  443. stride=1

  444. pad=1

  445. activation=leaky

  446. 

  447. [convolutional]

  448. batch_normalize=1

  449. filters=512

  450. size=3

  451. stride=1

  452. pad=1

  453. activation=leaky

  454. 

  455. [shortcut]

  456. from=-3

  457. activation=linear

  458. 

  459. # Downsample

  460. 

  461. [convolutional]

  462. batch_normalize=1

  463. filters=1024

  464. size=3

  465. stride=2

  466. pad=1

  467. activation=leaky

  468. 

  469. [convolutional]

  470. batch_normalize=1

  471. filters=512

  472. size=1

  473. stride=1

  474. pad=1

  475. activation=leaky

  476. 

  477. [convolutional]

  478. batch_normalize=1

  479. filters=1024

  480. size=3

  481. stride=1

  482. pad=1

  483. activation=leaky

  484. 

  485. [shortcut]

  486. from=-3

  487. activation=linear

  488. 

  489. [convolutional]

  490. batch_normalize=1

  491. filters=512

  492. size=1

  493. stride=1

  494. pad=1

  495. activation=leaky

  496. 

  497. [convolutional]

  498. batch_normalize=1

  499. filters=1024

  500. size=3

  501. stride=1

  502. pad=1

  503. activation=leaky

  504. 

  505. [shortcut]

  506. from=-3

  507. activation=linear

  508. 

  509. [convolutional]

  510. batch_normalize=1

  511. filters=512

  512. size=1

  513. stride=1

  514. pad=1

  515. activation=leaky

  516. 

  517. [convolutional]

  518. batch_normalize=1

  519. filters=1024

  520. size=3

  521. stride=1

  522. pad=1

  523. activation=leaky

  524. 

  525. [shortcut]

  526. from=-3

  527. activation=linear

  528. 

  529. [convolutional]

  530. batch_normalize=1

  531. filters=512

  532. size=1

  533. stride=1

  534. pad=1

  535. activation=leaky

  536. 

  537. [convolutional]

  538. batch_normalize=1

  539. filters=1024

  540. size=3

  541. stride=1

  542. pad=1

  543. activation=leaky

  544. 

  545. [shortcut]

  546. from=-3

  547. activation=linear

  548. 

  549. ######################

  550. 

  551. [convolutional]

  552. batch_normalize=1

  553. filters=512

  554. size=1

  555. stride=1

  556. pad=1

  557. activation=leaky

  558. 

  559. [convolutional]

  560. batch_normalize=1

  561. size=3

  562. stride=1

  563. pad=1

  564. filters=1024

  565. activation=leaky

  566. 

  567. [convolutional]

  568. batch_normalize=1

  569. filters=512

  570. size=1

  571. stride=1

  572. pad=1

  573. activation=leaky

  574. 

  575. [convolutional]

  576. batch_normalize=1

  577. size=3

  578. stride=1

  579. pad=1

  580. filters=1024

  581. activation=leaky

  582. 

  583. [convolutional]

  584. batch_normalize=1

  585. filters=512

  586. size=1

  587. stride=1

  588. pad=1

  589. activation=leaky

  590. 

  591. [convolutional]

  592. batch_normalize=1

  593. size=3

  594. stride=1

  595. pad=1

  596. filters=1024

  597. activation=leaky

  598. 

  599. [convolutional]

  600. size=1

  601. stride=1

  602. pad=1

  603. filters=18

  604. activation=linear

  605. 

  606. 

  607. [yolo]

  608. mask = 6,7,8

  609. anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326

  610. classes=1

  611. num=9

  612. jitter=.3

  613. ignore_thresh = .7

  614. truth_thresh = 1

  615. random=1

  616. 

  617. 

  618. [route]

  619. layers = -4

  620. 

  621. [convolutional]

  622. batch_normalize=1

  623. filters=256

  624. size=1

  625. stride=1

  626. pad=1

  627. activation=leaky

  628. 

  629. [upsample]

  630. stride=2

  631. 

  632. [route]

  633. layers = -1, 61

  634. 

  635. 

  636. 

  637. [convolutional]

  638. batch_normalize=1

  639. filters=256

  640. size=1

  641. stride=1

  642. pad=1

  643. activation=leaky

  644. 

  645. [convolutional]

  646. batch_normalize=1

  647. size=3

  648. stride=1

  649. pad=1

  650. filters=512

  651. activation=leaky

  652. 

  653. [convolutional]

  654. batch_normalize=1

  655. filters=256

  656. size=1

  657. stride=1

  658. pad=1

  659. activation=leaky

  660. 

  661. [convolutional]

  662. batch_normalize=1

  663. size=3

  664. stride=1

  665. pad=1

  666. filters=512

  667. activation=leaky

  668. 

  669. [convolutional]

  670. batch_normalize=1

  671. filters=256

  672. size=1

  673. stride=1

  674. pad=1

  675. activation=leaky

  676. 

  677. [convolutional]

  678. batch_normalize=1

  679. size=3

  680. stride=1

  681. pad=1

  682. filters=512

  683. activation=leaky

  684. 

  685. [convolutional]

  686. size=1

  687. stride=1

  688. pad=1

  689. filters=18

  690. activation=linear

  691. 

  692. 

  693. [yolo]

  694. mask = 3,4,5

  695. anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326

  696. classes=1

  697. num=9

  698. jitter=.3

  699. ignore_thresh = .7

  700. truth_thresh = 1

  701. random=1

  702. 

  703. 

  704. 

  705. [route]

  706. layers = -4

  707. 

  708. [convolutional]

  709. batch_normalize=1

  710. filters=128

  711. size=1

  712. stride=1

  713. pad=1

  714. activation=leaky

  715. 

  716. [upsample]

  717. stride=2

  718. 

  719. [route]

  720. layers = -1, 36

  721. 

  722. 

  723. 

  724. [convolutional]

  725. batch_normalize=1

  726. filters=128

  727. size=1

  728. stride=1

  729. pad=1

  730. activation=leaky

  731. 

  732. [convolutional]

  733. batch_normalize=1

  734. size=3

  735. stride=1

  736. pad=1

  737. filters=256

  738. activation=leaky

  739. 

  740. [convolutional]

  741. batch_normalize=1

  742. filters=128

  743. size=1

  744. stride=1

  745. pad=1

  746. activation=leaky

  747. 

  748. [convolutional]

  749. batch_normalize=1

  750. size=3

  751. stride=1

  752. pad=1

  753. filters=256

  754. activation=leaky

  755. 

  756. [convolutional]

  757. batch_normalize=1

  758. filters=128

  759. size=1

  760. stride=1

  761. pad=1

  762. activation=leaky

  763. 

  764. [convolutional]

  765. batch_normalize=1

  766. size=3

  767. stride=1

  768. pad=1

  769. filters=18

  770. activation=leaky

  771. 

  772. [convolutional]

  773. size=1

  774. stride=1

  775. pad=1

  776. filters=18

  777. activation=linear

  778. 

  779. 

  780. [yolo]

  781. mask = 0,1,2

  782. anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326

  783. classes=1

  784. num=9

  785. jitter=.3

  786. ignore_thresh = .7

  787. truth_thresh = 1

  788. random=1