API tools faq
paste
Guest User

panet-v4

a guest
Jul 9th, 2019
181
0
Never
Add comment
Not a member of Pastebin yet? Sign Up, it unlocks many cool features!
text 10.29 KB | None | 0 0
raw download clone embed print report
  1. [net]
  2. # Testing
  3. #batch=1
  4. #subdivisions=1
  5. # Training
  6. batch=64
  7. subdivisions=24
  8. width=512
  9. height=512
  10. channels=3
  11. momentum=0.9
  12. decay=0.0005
  13. angle=0
  14. saturation = 0.5
  15. exposure = 1.0
  16. hue=.1
  17.  
  18.  
  19. learning_rate=0.0001
  20. burn_in=1000
  21. max_batches = 100000
  22.  
  23. #policy=steps
  24. #steps=80000,90000
  25. #scales=.1,.1
  26.  
  27. policy=sgdr
  28. sgdr_cycle=1000
  29. sgdr_mult=2
  30. steps=4000,6000,8000,9000
  31. #scales=1, 1, 0.1, 0.1
  32.  
  33. [convolutional]
  34. batch_normalize=1
  35. filters=32
  36. size=3
  37. stride=1
  38. pad=1
  39. activation=leaky
  40.  
  41. # Downsample
  42.  
  43. [convolutional]
  44. batch_normalize=1
  45. filters=64
  46. size=3
  47. stride=2
  48. pad=1
  49. activation=leaky
  50.  
  51. [convolutional]
  52. batch_normalize=1
  53. filters=32
  54. size=1
  55. stride=1
  56. pad=1
  57. activation=leaky
  58.  
  59. [convolutional]
  60. batch_normalize=1
  61. filters=64
  62. size=3
  63. stride=1
  64. pad=1
  65. activation=leaky
  66.  
  67. [shortcut]
  68. from=-3
  69. activation=linear
  70.  
  71. # Downsample
  72.  
  73. [convolutional]
  74. batch_normalize=1
  75. filters=128
  76. size=3
  77. stride=2
  78. pad=1
  79. activation=leaky
  80.  
  81. [convolutional]
  82. batch_normalize=1
  83. filters=64
  84. size=1
  85. stride=1
  86. pad=1
  87. activation=leaky
  88.  
  89. [convolutional]
  90. batch_normalize=1
  91. filters=128
  92. size=3
  93. stride=1
  94. pad=1
  95. activation=leaky
  96.  
  97. [shortcut]
  98. from=-3
  99. activation=linear
  100.  
  101. [convolutional]
  102. batch_normalize=1
  103. filters=64
  104. size=1
  105. stride=1
  106. pad=1
  107. activation=leaky
  108.  
  109. [convolutional]
  110. batch_normalize=1
  111. filters=128
  112. size=3
  113. stride=1
  114. pad=1
  115. activation=leaky
  116.  
  117. [shortcut]
  118. from=-3
  119. activation=linear
  120.  
  121. # Downsample
  122.  
  123. [convolutional]
  124. batch_normalize=1
  125. filters=256
  126. size=3
  127. stride=2
  128. pad=1
  129. activation=leaky
  130.  
  131. [convolutional]
  132. batch_normalize=1
  133. filters=128
  134. size=1
  135. stride=1
  136. pad=1
  137. activation=leaky
  138.  
  139. [convolutional]
  140. batch_normalize=1
  141. filters=256
  142. size=3
  143. stride=1
  144. pad=1
  145. activation=leaky
  146.  
  147. [shortcut]
  148. from=-3
  149. activation=linear
  150.  
  151. [convolutional]
  152. batch_normalize=1
  153. filters=128
  154. size=1
  155. stride=1
  156. pad=1
  157. activation=leaky
  158.  
  159. [convolutional]
  160. batch_normalize=1
  161. filters=256
  162. size=3
  163. stride=1
  164. pad=1
  165. activation=leaky
  166.  
  167. [shortcut]
  168. from=-3
  169. activation=linear
  170.  
  171. [convolutional]
  172. batch_normalize=1
  173. filters=128
  174. size=1
  175. stride=1
  176. pad=1
  177. activation=leaky
  178.  
  179. [convolutional]
  180. batch_normalize=1
  181. filters=256
  182. size=3
  183. stride=1
  184. pad=1
  185. activation=leaky
  186.  
  187. [shortcut]
  188. from=-3
  189. activation=linear
  190.  
  191. [convolutional]
  192. batch_normalize=1
  193. filters=128
  194. size=1
  195. stride=1
  196. pad=1
  197. activation=leaky
  198.  
  199. [convolutional]
  200. batch_normalize=1
  201. filters=256
  202. size=3
  203. stride=1
  204. pad=1
  205. activation=leaky
  206.  
  207. [shortcut]
  208. from=-3
  209. activation=linear
  210.  
  211.  
  212. [convolutional]
  213. batch_normalize=1
  214. filters=128
  215. size=1
  216. stride=1
  217. pad=1
  218. activation=leaky
  219.  
  220. [convolutional]
  221. batch_normalize=1
  222. filters=256
  223. size=3
  224. stride=1
  225. pad=1
  226. activation=leaky
  227.  
  228. [shortcut]
  229. from=-3
  230. activation=linear
  231.  
  232. [convolutional]
  233. batch_normalize=1
  234. filters=128
  235. size=1
  236. stride=1
  237. pad=1
  238. activation=leaky
  239.  
  240. [convolutional]
  241. batch_normalize=1
  242. filters=256
  243. size=3
  244. stride=1
  245. pad=1
  246. activation=leaky
  247.  
  248. [shortcut]
  249. from=-3
  250. activation=linear
  251.  
  252. [convolutional]
  253. batch_normalize=1
  254. filters=128
  255. size=1
  256. stride=1
  257. pad=1
  258. activation=leaky
  259.  
  260. [convolutional]
  261. batch_normalize=1
  262. filters=256
  263. size=3
  264. stride=1
  265. pad=1
  266. activation=leaky
  267.  
  268. [shortcut]
  269. from=-3
  270. activation=linear
  271.  
  272. [convolutional]
  273. batch_normalize=1
  274. filters=128
  275. size=1
  276. stride=1
  277. pad=1
  278. activation=leaky
  279.  
  280. [convolutional]
  281. batch_normalize=1
  282. filters=256
  283. size=3
  284. stride=1
  285. pad=1
  286. activation=leaky
  287.  
  288. [shortcut]
  289. from=-3
  290. activation=linear
  291.  
  292. # Downsample
  293.  
  294. [convolutional]
  295. batch_normalize=1
  296. filters=512
  297. size=3
  298. stride=2
  299. pad=1
  300. activation=leaky
  301.  
  302. [convolutional]
  303. batch_normalize=1
  304. filters=256
  305. size=1
  306. stride=1
  307. pad=1
  308. activation=leaky
  309.  
  310. [convolutional]
  311. batch_normalize=1
  312. filters=512
  313. size=3
  314. stride=1
  315. pad=1
  316. activation=leaky
  317.  
  318. [shortcut]
  319. from=-3
  320. activation=linear
  321.  
  322.  
  323. [convolutional]
  324. batch_normalize=1
  325. filters=256
  326. size=1
  327. stride=1
  328. pad=1
  329. activation=leaky
  330.  
  331. [convolutional]
  332. batch_normalize=1
  333. filters=512
  334. size=3
  335. stride=1
  336. pad=1
  337. activation=leaky
  338.  
  339. [shortcut]
  340. from=-3
  341. activation=linear
  342.  
  343.  
  344. [convolutional]
  345. batch_normalize=1
  346. filters=256
  347. size=1
  348. stride=1
  349. pad=1
  350. activation=leaky
  351.  
  352. [convolutional]
  353. batch_normalize=1
  354. filters=512
  355. size=3
  356. stride=1
  357. pad=1
  358. activation=leaky
  359.  
  360. [shortcut]
  361. from=-3
  362. activation=linear
  363.  
  364.  
  365. [convolutional]
  366. batch_normalize=1
  367. filters=256
  368. size=1
  369. stride=1
  370. pad=1
  371. activation=leaky
  372.  
  373. [convolutional]
  374. batch_normalize=1
  375. filters=512
  376. size=3
  377. stride=1
  378. pad=1
  379. activation=leaky
  380.  
  381. [shortcut]
  382. from=-3
  383. activation=linear
  384.  
  385. [convolutional]
  386. batch_normalize=1
  387. filters=256
  388. size=1
  389. stride=1
  390. pad=1
  391. activation=leaky
  392.  
  393. [convolutional]
  394. batch_normalize=1
  395. filters=512
  396. size=3
  397. stride=1
  398. pad=1
  399. activation=leaky
  400.  
  401. [shortcut]
  402. from=-3
  403. activation=linear
  404.  
  405.  
  406. [convolutional]
  407. batch_normalize=1
  408. filters=256
  409. size=1
  410. stride=1
  411. pad=1
  412. activation=leaky
  413.  
  414. [convolutional]
  415. batch_normalize=1
  416. filters=512
  417. size=3
  418. stride=1
  419. pad=1
  420. activation=leaky
  421.  
  422. [shortcut]
  423. from=-3
  424. activation=linear
  425.  
  426.  
  427. [convolutional]
  428. batch_normalize=1
  429. filters=256
  430. size=1
  431. stride=1
  432. pad=1
  433. activation=leaky
  434.  
  435. [convolutional]
  436. batch_normalize=1
  437. filters=512
  438. size=3
  439. stride=1
  440. pad=1
  441. activation=leaky
  442.  
  443. [shortcut]
  444. from=-3
  445. activation=linear
  446.  
  447. [convolutional]
  448. batch_normalize=1
  449. filters=256
  450. size=1
  451. stride=1
  452. pad=1
  453. activation=leaky
  454.  
  455. [convolutional]
  456. batch_normalize=1
  457. filters=512
  458. size=3
  459. stride=1
  460. pad=1
  461. activation=leaky
  462.  
  463. [shortcut]
  464. from=-3
  465. activation=linear
  466.  
  467. # Downsample
  468.  
  469. [convolutional]
  470. batch_normalize=1
  471. filters=1024
  472. size=3
  473. stride=2
  474. pad=1
  475. activation=leaky
  476.  
  477. [convolutional]
  478. batch_normalize=1
  479. filters=512
  480. size=1
  481. stride=1
  482. pad=1
  483. activation=leaky
  484.  
  485. [convolutional]
  486. batch_normalize=1
  487. filters=1024
  488. size=3
  489. stride=1
  490. pad=1
  491. activation=leaky
  492.  
  493. [shortcut]
  494. from=-3
  495. activation=linear
  496.  
  497. [convolutional]
  498. batch_normalize=1
  499. filters=512
  500. size=1
  501. stride=1
  502. pad=1
  503. activation=leaky
  504.  
  505. [convolutional]
  506. batch_normalize=1
  507. filters=1024
  508. size=3
  509. stride=1
  510. pad=1
  511. activation=leaky
  512.  
  513. [shortcut]
  514. from=-3
  515. activation=linear
  516.  
  517. [convolutional]
  518. batch_normalize=1
  519. filters=512
  520. size=1
  521. stride=1
  522. pad=1
  523. activation=leaky
  524.  
  525. [convolutional]
  526. batch_normalize=1
  527. filters=1024
  528. size=3
  529. stride=1
  530. pad=1
  531. activation=leaky
  532.  
  533. [shortcut]
  534. from=-3
  535. activation=linear
  536.  
  537. [convolutional]
  538. batch_normalize=1
  539. filters=512
  540. size=1
  541. stride=1
  542. pad=1
  543. activation=leaky
  544.  
  545. [convolutional]
  546. batch_normalize=1
  547. filters=1024
  548. size=3
  549. stride=1
  550. pad=1
  551. activation=leaky
  552.  
  553. [shortcut]
  554. from=-3
  555. activation=linear
  556.  
  557. ######################
  558.  
  559. [convolutional]
  560. batch_normalize=1
  561. filters=512
  562. size=1
  563. stride=1
  564. pad=1
  565. activation=leaky
  566.  
  567. [convolutional]
  568. batch_normalize=1
  569. size=3
  570. stride=1
  571. pad=1
  572. filters=1024
  573. activation=leaky
  574.  
  575. [convolutional]
  576. batch_normalize=1
  577. filters=512
  578. size=1
  579. stride=1
  580. pad=1
  581. activation=leaky
  582.  
  583. ### SPP ###
  584. [maxpool]
  585. stride=1
  586. size=5
  587.  
  588. [route]
  589. layers=-2
  590.  
  591. [maxpool]
  592. stride=1
  593. size=9
  594.  
  595. [route]
  596. layers=-4
  597.  
  598. [maxpool]
  599. stride=1
  600. size=13
  601.  
  602. [route]
  603. layers=-1,-3,-5,-6
  604.  
  605. ### End SPP ###
  606.  
  607. [convolutional]
  608. batch_normalize=1
  609. filters=512
  610. size=1
  611. stride=1
  612. pad=1
  613. activation=leaky
  614.  
  615.  
  616. [convolutional]
  617. batch_normalize=1
  618. size=3
  619. stride=1
  620. pad=1
  621. filters=1024
  622. activation=leaky
  623.  
  624. [convolutional]
  625. batch_normalize=1
  626. filters=512
  627. size=1
  628. stride=1
  629. pad=1
  630. activation=leaky
  631.  
  632.  
  633.  
  634. ########### to [yolo-3]
  635.  
  636.  
  637.  
  638. [route]
  639. layers = -4
  640.  
  641. [convolutional]
  642. batch_normalize=1
  643. filters=256
  644. size=1
  645. stride=1
  646. pad=1
  647. activation=leaky
  648.  
  649. [upsample]
  650. stride=2
  651.  
  652. [route]
  653. layers = -1, 61
  654.  
  655.  
  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. batch_normalize=1
  691. filters=256
  692. size=1
  693. stride=1
  694. pad=1
  695. activation=leaky
  696.  
  697.  
  698. ########### to [yolo-2]
  699.  
  700.  
  701.  
  702.  
  703. [route]
  704. layers = -4
  705.  
  706. [convolutional]
  707. batch_normalize=1
  708. filters=128
  709. size=1
  710. stride=1
  711. pad=1
  712. activation=leaky
  713.  
  714. [upsample]
  715. stride=2
  716.  
  717. [route]
  718. layers = -1, 36
  719.  
  720.  
  721.  
  722. [convolutional]
  723. batch_normalize=1
  724. filters=128
  725. size=1
  726. stride=1
  727. pad=1
  728. activation=leaky
  729.  
  730. [convolutional]
  731. batch_normalize=1
  732. size=3
  733. stride=1
  734. pad=1
  735. filters=256
  736. activation=leaky
  737.  
  738. [convolutional]
  739. batch_normalize=1
  740. filters=128
  741. size=1
  742. stride=1
  743. pad=1
  744. activation=leaky
  745.  
  746. [convolutional]
  747. batch_normalize=1
  748. size=3
  749. stride=1
  750. pad=1
  751. filters=256
  752. activation=leaky
  753.  
  754. [convolutional]
  755. batch_normalize=1
  756. filters=128
  757. size=1
  758. stride=1
  759. pad=1
  760. activation=leaky
  761.  
  762.  
  763.  
  764. ########### to [yolo-1]
  765.  
  766.  
  767. ########### features of different layers
  768.  
  769.  
  770. [route]
  771. layers=1
  772.  
  773. [reorg3d]
  774. stride=2
  775.  
  776. [route]
  777. layers=5,-1
  778.  
  779. [reorg3d]
  780. stride=2
  781.  
  782. [route]
  783. layers=12,-1
  784.  
  785. [reorg3d]
  786. stride=2
  787.  
  788. [route]
  789. layers=37,-1
  790.  
  791. [reorg3d]
  792. stride=2
  793.  
  794. [route]
  795. layers=62,-1
  796.  
  797.  
  798.  
  799. ########### [yolo-1]
  800.  
  801. [convolutional]
  802. batch_normalize=1
  803. filters=128
  804. size=1
  805. stride=1
  806. pad=1
  807. activation=leaky
  808.  
  809. [upsample]
  810. stride=4
  811.  
  812. [route]
  813. layers = -1,-12
  814.  
  815.  
  816. [convolutional]
  817. batch_normalize=1
  818. size=3
  819. stride=1
  820. pad=1
  821. filters=256
  822. activation=leaky
  823.  
  824. [convolutional]
  825. size=1
  826. stride=1
  827. pad=1
  828. filters=28
  829. activation=linear
  830.  
  831.  
  832. [yolo]
  833. mask = 0,1,2,3
  834. anchors = 8,8, 10,13, 16,30, 33,23, 32,32, 30,61, 62,45, 64,64, 59,119, 116,90, 156,198, 373,326
  835. classes=2
  836. num=12
  837. jitter=.3
  838. ignore_thresh = .7
  839. truth_thresh = 1
  840. scale_x_y = 1.05
  841. random=0
  842.  
  843.  
  844.  
  845.  
  846. ########### [yolo-2]
  847.  
  848.  
  849. [route]
  850. layers = -7
  851.  
  852. [convolutional]
  853. batch_normalize=1
  854. filters=256
  855. size=1
  856. stride=1
  857. pad=1
  858. activation=leaky
  859.  
  860. [upsample]
  861. stride=2
  862.  
  863. [route]
  864. layers = -1,-28
  865.  
  866.  
  867. [convolutional]
  868. batch_normalize=1
  869. size=3
  870. stride=1
  871. pad=1
  872. filters=512
  873. activation=leaky
  874.  
  875. [convolutional]
  876. size=1
  877. stride=1
  878. pad=1
  879. filters=28
  880. activation=linear
  881.  
  882.  
  883. [yolo]
  884. mask = 4,5,6,7
  885. anchors = 8,8, 10,13, 16,30, 33,23, 32,32, 30,61, 62,45, 64,64, 59,119, 116,90, 156,198, 373,326
  886. classes=2
  887. num=12
  888. jitter=.3
  889. ignore_thresh = .7
  890. truth_thresh = 1
  891. scale_x_y = 1.1
  892. random=0
  893.  
  894.  
  895.  
  896. ########### [yolo-3]
  897.  
  898. [route]
  899. layers = -14
  900.  
  901. [convolutional]
  902. batch_normalize=1
  903. filters=512
  904. size=1
  905. stride=1
  906. pad=1
  907. activation=leaky
  908.  
  909. [route]
  910. layers = -1,-43
  911.  
  912. [convolutional]
  913. batch_normalize=1
  914. size=3
  915. stride=1
  916. pad=1
  917. filters=1024
  918. activation=leaky
  919.  
  920.  
  921. [convolutional]
  922. size=1
  923. stride=1
  924. pad=1
  925. filters=28
  926. activation=linear
  927.  
  928.  
  929. [yolo]
  930. mask = 8,9,10,11
  931. anchors = 8,8, 10,13, 16,30, 33,23, 32,32, 30,61, 62,45, 59,119, 80,80, 116,90, 156,198, 373,326
  932. classes=2
  933. num=12
  934. jitter=.3
  935. ignore_thresh = .7
  936. truth_thresh = 1
  937. scale_x_y = 1.2
  938. random=0
Advertisement
Add Comment
Please, Sign In to add comment
Public Pastes
We use cookies for various purposes including analytics. By continuing to use Pastebin, you agree to our use of cookies as described in the Cookies Policy.  OK, I Understand
Not a member of Pastebin yet?
Sign Up, it unlocks many cool features!