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Repository for M.A.I.L system's analysis server.
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  1. [net]

  2. # Testing

  3. # batch=1

  4. # subdivisions=1

  5. # Training

  6. #Created: 2019-10-08 20:16

  7. #Changes:

  8. #In Line 721 stride=2 to 4

  9. #In Line 724 layers= to -1,11

  10. batch=64

  11. subdivisions=32

  12. width=960

  13. height=540

  14. channels=3

  15. momentum=0.9

  16. decay=0.0005

  17. angle=0

  18. saturation = 1.5

  19. exposure = 1.5

  20. hue=.1

  21. 

  22. learning_rate=0.001

  23. burn_in=1000

  24. max_batches = 500200

  25. policy=steps

  26. steps=400000,450000

  27. scales=.1,.1

  28. 

  29. [convolutional]

  30. batch_normalize=1

  31. filters=32

  32. size=3

  33. stride=1

  34. pad=1

  35. activation=leaky

  36. 

  37. # Downsample

  38. 

  39. [convolutional]

  40. batch_normalize=1

  41. filters=64

  42. size=3

  43. stride=2

  44. pad=1

  45. activation=leaky

  46. 

  47. [convolutional]

  48. batch_normalize=1

  49. filters=32

  50. size=1

  51. stride=1

  52. pad=1

  53. activation=leaky

  54. 

  55. [convolutional]

  56. batch_normalize=1

  57. filters=64

  58. size=3

  59. stride=1

  60. pad=1

  61. activation=leaky

  62. 

  63. [shortcut]

  64. from=-3

  65. activation=linear

  66. 

  67. # Downsample

  68. 

  69. [convolutional]

  70. batch_normalize=1

  71. filters=128

  72. size=3

  73. stride=2

  74. pad=1

  75. activation=leaky

  76. 

  77. [convolutional]

  78. batch_normalize=1

  79. filters=64

  80. size=1

  81. stride=1

  82. pad=1

  83. activation=leaky

  84. 

  85. [convolutional]

  86. batch_normalize=1

  87. filters=128

  88. size=3

  89. stride=1

  90. pad=1

  91. activation=leaky

  92. 

  93. [shortcut]

  94. from=-3

  95. activation=linear

  96. 

  97. [convolutional]

  98. batch_normalize=1

  99. filters=64

  100. size=1

  101. stride=1

  102. pad=1

  103. activation=leaky

  104. 

  105. [convolutional]

  106. batch_normalize=1

  107. filters=128

  108. size=3

  109. stride=1

  110. pad=1

  111. activation=leaky

  112. 

  113. [shortcut]

  114. from=-3

  115. activation=linear

  116. 

  117. # Downsample

  118. 

  119. [convolutional]

  120. batch_normalize=1

  121. filters=256

  122. size=3

  123. stride=2

  124. pad=1

  125. activation=leaky

  126. 

  127. [convolutional]

  128. batch_normalize=1

  129. filters=128

  130. size=1

  131. stride=1

  132. pad=1

  133. activation=leaky

  134. 

  135. [convolutional]

  136. batch_normalize=1

  137. filters=256

  138. size=3

  139. stride=1

  140. pad=1

  141. activation=leaky

  142. 

  143. [shortcut]

  144. from=-3

  145. activation=linear

  146. 

  147. [convolutional]

  148. batch_normalize=1

  149. filters=128

  150. size=1

  151. stride=1

  152. pad=1

  153. activation=leaky

  154. 

  155. [convolutional]

  156. batch_normalize=1

  157. filters=256

  158. size=3

  159. stride=1

  160. pad=1

  161. activation=leaky

  162. 

  163. [shortcut]

  164. from=-3

  165. activation=linear

  166. 

  167. [convolutional]

  168. batch_normalize=1

  169. filters=128

  170. size=1

  171. stride=1

  172. pad=1

  173. activation=leaky

  174. 

  175. [convolutional]

  176. batch_normalize=1

  177. filters=256

  178. size=3

  179. stride=1

  180. pad=1

  181. activation=leaky

  182. 

  183. [shortcut]

  184. from=-3

  185. activation=linear

  186. 

  187. [convolutional]

  188. batch_normalize=1

  189. filters=128

  190. size=1

  191. stride=1

  192. pad=1

  193. activation=leaky

  194. 

  195. [convolutional]

  196. batch_normalize=1

  197. filters=256

  198. size=3

  199. stride=1

  200. pad=1

  201. activation=leaky

  202. 

  203. [shortcut]

  204. from=-3

  205. activation=linear

  206. 

  207. 

  208. [convolutional]

  209. batch_normalize=1

  210. filters=128

  211. size=1

  212. stride=1

  213. pad=1

  214. activation=leaky

  215. 

  216. [convolutional]

  217. batch_normalize=1

  218. filters=256

  219. size=3

  220. stride=1

  221. pad=1

  222. activation=leaky

  223. 

  224. [shortcut]

  225. from=-3

  226. activation=linear

  227. 

  228. [convolutional]

  229. batch_normalize=1

  230. filters=128

  231. size=1

  232. stride=1

  233. pad=1

  234. activation=leaky

  235. 

  236. [convolutional]

  237. batch_normalize=1

  238. filters=256

  239. size=3

  240. stride=1

  241. pad=1

  242. activation=leaky

  243. 

  244. [shortcut]

  245. from=-3

  246. activation=linear

  247. 

  248. [convolutional]

  249. batch_normalize=1

  250. filters=128

  251. size=1

  252. stride=1

  253. pad=1

  254. activation=leaky

  255. 

  256. [convolutional]

  257. batch_normalize=1

  258. filters=256

  259. size=3

  260. stride=1

  261. pad=1

  262. activation=leaky

  263. 

  264. [shortcut]

  265. from=-3

  266. activation=linear

  267. 

  268. [convolutional]

  269. batch_normalize=1

  270. filters=128

  271. size=1

  272. stride=1

  273. pad=1

  274. activation=leaky

  275. 

  276. [convolutional]

  277. batch_normalize=1

  278. filters=256

  279. size=3

  280. stride=1

  281. pad=1

  282. activation=leaky

  283. 

  284. [shortcut]

  285. from=-3

  286. activation=linear

  287. 

  288. # Downsample

  289. 

  290. [convolutional]

  291. batch_normalize=1

  292. filters=512

  293. size=3

  294. stride=2

  295. pad=1

  296. activation=leaky

  297. 

  298. [convolutional]

  299. batch_normalize=1

  300. filters=256

  301. size=1

  302. stride=1

  303. pad=1

  304. activation=leaky

  305. 

  306. [convolutional]

  307. batch_normalize=1

  308. filters=512

  309. size=3

  310. stride=1

  311. pad=1

  312. activation=leaky

  313. 

  314. [shortcut]

  315. from=-3

  316. activation=linear

  317. 

  318. 

  319. [convolutional]

  320. batch_normalize=1

  321. filters=256

  322. size=1

  323. stride=1

  324. pad=1

  325. activation=leaky

  326. 

  327. [convolutional]

  328. batch_normalize=1

  329. filters=512

  330. size=3

  331. stride=1

  332. pad=1

  333. activation=leaky

  334. 

  335. [shortcut]

  336. from=-3

  337. activation=linear

  338. 

  339. 

  340. [convolutional]

  341. batch_normalize=1

  342. filters=256

  343. size=1

  344. stride=1

  345. pad=1

  346. activation=leaky

  347. 

  348. [convolutional]

  349. batch_normalize=1

  350. filters=512

  351. size=3

  352. stride=1

  353. pad=1

  354. activation=leaky

  355. 

  356. [shortcut]

  357. from=-3

  358. activation=linear

  359. 

  360. 

  361. [convolutional]

  362. batch_normalize=1

  363. filters=256

  364. size=1

  365. stride=1

  366. pad=1

  367. activation=leaky

  368. 

  369. [convolutional]

  370. batch_normalize=1

  371. filters=512

  372. size=3

  373. stride=1

  374. pad=1

  375. activation=leaky

  376. 

  377. [shortcut]

  378. from=-3

  379. activation=linear

  380. 

  381. [convolutional]

  382. batch_normalize=1

  383. filters=256

  384. size=1

  385. stride=1

  386. pad=1

  387. activation=leaky

  388. 

  389. [convolutional]

  390. batch_normalize=1

  391. filters=512

  392. size=3

  393. stride=1

  394. pad=1

  395. activation=leaky

  396. 

  397. [shortcut]

  398. from=-3

  399. activation=linear

  400. 

  401. 

  402. [convolutional]

  403. batch_normalize=1

  404. filters=256

  405. size=1

  406. stride=1

  407. pad=1

  408. activation=leaky

  409. 

  410. [convolutional]

  411. batch_normalize=1

  412. filters=512

  413. size=3

  414. stride=1

  415. pad=1

  416. activation=leaky

  417. 

  418. [shortcut]

  419. from=-3

  420. activation=linear

  421. 

  422. 

  423. [convolutional]

  424. batch_normalize=1

  425. filters=256

  426. size=1

  427. stride=1

  428. pad=1

  429. activation=leaky

  430. 

  431. [convolutional]

  432. batch_normalize=1

  433. filters=512

  434. size=3

  435. stride=1

  436. pad=1

  437. activation=leaky

  438. 

  439. [shortcut]

  440. from=-3

  441. activation=linear

  442. 

  443. [convolutional]

  444. batch_normalize=1

  445. filters=256

  446. size=1

  447. stride=1

  448. pad=1

  449. activation=leaky

  450. 

  451. [convolutional]

  452. batch_normalize=1

  453. filters=512

  454. size=3

  455. stride=1

  456. pad=1

  457. activation=leaky

  458. 

  459. [shortcut]

  460. from=-3

  461. activation=linear

  462. 

  463. # Downsample

  464. 

  465. [convolutional]

  466. batch_normalize=1

  467. filters=1024

  468. size=3

  469. stride=2

  470. pad=1

  471. activation=leaky

  472. 

  473. [convolutional]

  474. batch_normalize=1

  475. filters=512

  476. size=1

  477. stride=1

  478. pad=1

  479. activation=leaky

  480. 

  481. [convolutional]

  482. batch_normalize=1

  483. filters=1024

  484. size=3

  485. stride=1

  486. pad=1

  487. activation=leaky

  488. 

  489. [shortcut]

  490. from=-3

  491. activation=linear

  492. 

  493. [convolutional]

  494. batch_normalize=1

  495. filters=512

  496. size=1

  497. stride=1

  498. pad=1

  499. activation=leaky

  500. 

  501. [convolutional]

  502. batch_normalize=1

  503. filters=1024

  504. size=3

  505. stride=1

  506. pad=1

  507. activation=leaky

  508. 

  509. [shortcut]

  510. from=-3

  511. activation=linear

  512. 

  513. [convolutional]

  514. batch_normalize=1

  515. filters=512

  516. size=1

  517. stride=1

  518. pad=1

  519. activation=leaky

  520. 

  521. [convolutional]

  522. batch_normalize=1

  523. filters=1024

  524. size=3

  525. stride=1

  526. pad=1

  527. activation=leaky

  528. 

  529. [shortcut]

  530. from=-3

  531. activation=linear

  532. 

  533. [convolutional]

  534. batch_normalize=1

  535. filters=512

  536. size=1

  537. stride=1

  538. pad=1

  539. activation=leaky

  540. 

  541. [convolutional]

  542. batch_normalize=1

  543. filters=1024

  544. size=3

  545. stride=1

  546. pad=1

  547. activation=leaky

  548. 

  549. [shortcut]

  550. from=-3

  551. activation=linear

  552. 

  553. ######################

  554. 

  555. [convolutional]

  556. batch_normalize=1

  557. filters=512

  558. size=1

  559. stride=1

  560. pad=1

  561. activation=leaky

  562. 

  563. [convolutional]

  564. batch_normalize=1

  565. size=3

  566. stride=1

  567. pad=1

  568. filters=1024

  569. activation=leaky

  570. 

  571. [convolutional]

  572. batch_normalize=1

  573. filters=512

  574. size=1

  575. stride=1

  576. pad=1

  577. activation=leaky

  578. 

  579. [convolutional]

  580. batch_normalize=1

  581. size=3

  582. stride=1

  583. pad=1

  584. filters=1024

  585. activation=leaky

  586. 

  587. [convolutional]

  588. batch_normalize=1

  589. filters=512

  590. size=1

  591. stride=1

  592. pad=1

  593. activation=leaky

  594. 

  595. [convolutional]

  596. batch_normalize=1

  597. size=3

  598. stride=1

  599. pad=1

  600. filters=1024

  601. activation=leaky

  602. 

  603. [convolutional]

  604. size=1

  605. stride=1

  606. pad=1

  607. filters=21

  608. activation=linear

  609. 

  610. 

  611. [yolo]

  612. mask = 6,7,8

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

  614. classes=2

  615. num=9

  616. jitter=.3

  617. ignore_thresh = .7

  618. truth_thresh = 1

  619. random=1

  620. 

  621. 

  622. [route]

  623. layers = -4

  624. 

  625. [convolutional]

  626. batch_normalize=1

  627. filters=256

  628. size=1

  629. stride=1

  630. pad=1

  631. activation=leaky

  632. 

  633. [upsample]

  634. stride=2

  635. 

  636. [route]

  637. layers = -1, 61

  638. 

  639. 

  640. 

  641. [convolutional]

  642. batch_normalize=1

  643. filters=256

  644. size=1

  645. stride=1

  646. pad=1

  647. activation=leaky

  648. 

  649. [convolutional]

  650. batch_normalize=1

  651. size=3

  652. stride=1

  653. pad=1

  654. filters=512

  655. activation=leaky

  656. 

  657. [convolutional]

  658. batch_normalize=1

  659. filters=256

  660. size=1

  661. stride=1

  662. pad=1

  663. activation=leaky

  664. 

  665. [convolutional]

  666. batch_normalize=1

  667. size=3

  668. stride=1

  669. pad=1

  670. filters=512

  671. activation=leaky

  672. 

  673. [convolutional]

  674. batch_normalize=1

  675. filters=256

  676. size=1

  677. stride=1

  678. pad=1

  679. activation=leaky

  680. 

  681. [convolutional]

  682. batch_normalize=1

  683. size=3

  684. stride=1

  685. pad=1

  686. filters=512

  687. activation=leaky

  688. 

  689. [convolutional]

  690. size=1

  691. stride=1

  692. pad=1

  693. filters=21

  694. activation=linear

  695. 

  696. 

  697. [yolo]

  698. mask = 3,4,5

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

  700. classes=2

  701. num=9

  702. jitter=.3

  703. ignore_thresh = .7

  704. truth_thresh = 1

  705. random=1

  706. 

  707. 

  708. 

  709. [route]

  710. layers = -4

  711. 

  712. [convolutional]

  713. batch_normalize=1

  714. filters=128

  715. size=1

  716. stride=1

  717. pad=1

  718. activation=leaky

  719. 

  720. [upsample]

  721. stride=2

  722. 

  723. [route]

  724. layers = -1, 36

  725. 

  726. 

  727. 

  728. [convolutional]

  729. batch_normalize=1

  730. filters=128

  731. size=1

  732. stride=1

  733. pad=1

  734. activation=leaky

  735. 

  736. [convolutional]

  737. batch_normalize=1

  738. size=3

  739. stride=1

  740. pad=1

  741. filters=256

  742. activation=leaky

  743. 

  744. [convolutional]

  745. batch_normalize=1

  746. filters=128

  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=256

  758. activation=leaky

  759. 

  760. [convolutional]

  761. batch_normalize=1

  762. filters=128

  763. size=1

  764. stride=1

  765. pad=1

  766. activation=leaky

  767. 

  768. [convolutional]

  769. batch_normalize=1

  770. size=3

  771. stride=1

  772. pad=1

  773. filters=256

  774. activation=leaky

  775. 

  776. [convolutional]

  777. size=1

  778. stride=1

  779. pad=1

  780. filters=21

  781. activation=linear

  782. 

  783. 

  784. [yolo]

  785. mask = 0,1,2

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

  787. classes=2

  788. num=9

  789. jitter=.3

  790. ignore_thresh = .7

  791. truth_thresh = 1

  792. random=1