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  1. name: "YOLONET"
  2. layer {
  3. name: "data"
  4. type: "BoxData"
  5. top: "data"
  6. top: "label"
  7. include {
  8. phase: TRAIN
  9. }
  10. transform_param {
  11. mirror: true
  12. force_color: true
  13. mean_value: 104
  14. mean_value: 117
  15. mean_value: 123
  16. }
  17. data_param {
  18. source: "../../data/yolo/lmdb/trainval_lmdb"
  19. batch_size: 1
  20. backend: LMDB
  21. }
  22. }
  23. layer {
  24. name: "data"
  25. type: "BoxData"
  26. top: "data"
  27. top: "label"
  28. include {
  29. phase: TEST
  30. }
  31. transform_param {
  32. mirror: false
  33. mean_value: 104
  34. mean_value: 117
  35. mean_value: 123
  36. }
  37. data_param {
  38. source: "../../data/yolo/lmdb/test_lmdb"
  39. batch_size: 1
  40. backend: LMDB
  41. }
  42. }
  43. layer {
  44. name: "conv1"
  45. type: "Convolution"
  46. bottom: "data"
  47. top: "conv1"
  48. convolution_param {
  49. num_output: 32
  50. kernel_size: 3
  51. pad: 1
  52. stride: 1
  53. bias_term: false
  54. }
  55. }
  56. layer {
  57. name: "bn1"
  58. type: "BatchNorm"
  59. bottom: "conv1"
  60. top: "bn1"
  61. }
  62. layer {
  63. name: "scale1"
  64. type: "Scale"
  65. bottom: "bn1"
  66. top: "scale1"
  67. scale_param {
  68. bias_term: true
  69. }
  70. }
  71. layer {
  72. name: "relu1"
  73. type: "ReLU"
  74. bottom: "scale1"
  75. top: "scale1"
  76. relu_param{
  77. negative_slope: 0.1
  78. }
  79. }
  80. layer {
  81. name: "pool1"
  82. type: "Pooling"
  83. bottom: "scale1"
  84. top: "pool1"
  85. pooling_param {
  86. pool: MAX
  87. kernel_size: 2
  88. stride: 2
  89. }
  90. }
  91. layer{
  92. name: "conv2"
  93. type: "Convolution"
  94. bottom: "pool1"
  95. top: "conv2"
  96. convolution_param {
  97. num_output: 64
  98. kernel_size: 3
  99. pad: 1
  100. stride: 1
  101. bias_term: false
  102. }
  103. }
  104. layer {
  105. name: "bn2"
  106. type: "BatchNorm"
  107. bottom: "conv2"
  108. top: "bn2"
  109. param {
  110. lr_mult: 0
  111. }
  112. param {
  113. lr_mult: 0
  114. }
  115. param {
  116. lr_mult: 0
  117. }
  118. }
  119. layer {
  120. name: "scale2"
  121. type: "Scale"
  122. bottom: "bn2"
  123. top: "scale2"
  124. scale_param {
  125. bias_term: true
  126. }
  127. }
  128. layer {
  129. name: "relu2"
  130. type: "ReLU"
  131. bottom: "scale2"
  132. top: "scale2"
  133. relu_param{
  134. negative_slope: 0.1
  135. }
  136. }
  137. layer {
  138. name: "pool2"
  139. type: "Pooling"
  140. bottom: "scale2"
  141. top: "pool2"
  142. pooling_param {
  143. pool: MAX
  144. kernel_size: 2
  145. stride: 2
  146. }
  147. }
  148.  
  149. layer{
  150. name: "conv3"
  151. type: "Convolution"
  152. bottom: "pool2"
  153. top: "conv3"
  154. convolution_param {
  155. num_output: 128
  156. kernel_size: 3
  157. pad: 1
  158. stride: 1
  159. bias_term: false
  160. }
  161. }
  162. layer {
  163. name: "bn3"
  164. type: "BatchNorm"
  165. bottom: "conv3"
  166. top: "bn3"
  167. param {
  168. lr_mult: 0
  169. }
  170. param {
  171. lr_mult: 0
  172. }
  173. param {
  174. lr_mult: 0
  175. }
  176. }
  177. layer {
  178. name: "scale3"
  179. type: "Scale"
  180. bottom: "bn3"
  181. top: "scale3"
  182. scale_param {
  183. bias_term: true
  184. }
  185. }
  186. layer {
  187. name: "relu3"
  188. type: "ReLU"
  189. bottom: "scale3"
  190. top: "scale3"
  191. relu_param{
  192. negative_slope: 0.1
  193. }
  194. }
  195.  
  196.  
  197. layer{
  198. name: "conv4"
  199. type: "Convolution"
  200. bottom: "scale3"
  201. top: "conv4"
  202. convolution_param {
  203. num_output: 64
  204. kernel_size: 1
  205. pad: 0 #??
  206. stride: 1
  207. bias_term: false
  208. }
  209. }
  210. layer {
  211. name: "bn4"
  212. type: "BatchNorm"
  213. bottom: "conv4"
  214. top: "bn4"
  215. param {
  216. lr_mult: 0
  217. }
  218. param {
  219. lr_mult: 0
  220. }
  221. param {
  222. lr_mult: 0
  223. }
  224. }
  225. layer {
  226. name: "scale4"
  227. type: "Scale"
  228. bottom: "bn4"
  229. top: "scale4"
  230. scale_param {
  231. bias_term: true
  232. }
  233. }
  234. layer {
  235. name: "relu4"
  236. type: "ReLU"
  237. bottom: "scale4"
  238. top: "scale4"
  239. relu_param{
  240. negative_slope: 0.1
  241. }
  242. }
  243.  
  244. layer{
  245. name: "conv5"
  246. type: "Convolution"
  247. bottom: "scale4"
  248. top: "conv5"
  249. convolution_param {
  250. num_output: 128
  251. kernel_size: 3
  252. pad: 1
  253. stride: 1
  254. bias_term: false
  255. }
  256. }
  257. layer {
  258. name: "bn5"
  259. type: "BatchNorm"
  260. bottom: "conv5"
  261. top: "bn5"
  262. param {
  263. lr_mult: 0
  264. }
  265. param {
  266. lr_mult: 0
  267. }
  268. param {
  269. lr_mult: 0
  270. }
  271. }
  272. layer {
  273. name: "scale5"
  274. type: "Scale"
  275. bottom: "bn5"
  276. top: "scale5"
  277. scale_param {
  278. bias_term: true
  279. }
  280. }
  281. layer {
  282. name: "relu5"
  283. type: "ReLU"
  284. bottom: "scale5"
  285. top: "scale5"
  286. relu_param{
  287. negative_slope: 0.1
  288. }
  289. }
  290. layer {
  291. name: "pool5"
  292. type: "Pooling"
  293. bottom: "scale5"
  294. top: "pool5"
  295. pooling_param {
  296. pool: MAX
  297. kernel_size: 2
  298. stride: 2
  299. }
  300. }
  301.  
  302. layer{
  303. name: "conv6"
  304. type: "Convolution"
  305. bottom: "pool5"
  306. top: "conv6"
  307. convolution_param {
  308. num_output: 256
  309. kernel_size: 3
  310. pad: 1
  311. stride: 1
  312. bias_term: false
  313. }
  314. }
  315. layer {
  316. name: "bn6"
  317. type: "BatchNorm"
  318. bottom: "conv6"
  319. top: "bn6"
  320. param {
  321. lr_mult: 0
  322. }
  323. param {
  324. lr_mult: 0
  325. }
  326. param {
  327. lr_mult: 0
  328. }
  329. }
  330. layer {
  331. name: "scale6"
  332. type: "Scale"
  333. bottom: "bn6"
  334. top: "scale6"
  335. scale_param {
  336. bias_term: true
  337. }
  338. }
  339. layer {
  340. name: "relu6"
  341. type: "ReLU"
  342. bottom: "scale6"
  343. top: "scale6"
  344. relu_param{
  345. negative_slope: 0.1
  346. }
  347. }
  348.  
  349. layer{
  350. name: "conv7"
  351. type: "Convolution"
  352. bottom: "scale6"
  353. top: "conv7"
  354. convolution_param {
  355. num_output: 128
  356. kernel_size: 1
  357. pad: 0
  358. stride: 1
  359. bias_term: false
  360. }
  361. }
  362. layer {
  363. name: "bn7"
  364. type: "BatchNorm"
  365. bottom: "conv7"
  366. top: "bn7"
  367. param {
  368. lr_mult: 0
  369. }
  370. param {
  371. lr_mult: 0
  372. }
  373. param {
  374. lr_mult: 0
  375. }
  376. }
  377. layer {
  378. name: "scale7"
  379. type: "Scale"
  380. bottom: "bn7"
  381. top: "scale7"
  382. scale_param {
  383. bias_term: true
  384. }
  385. }
  386. layer {
  387. name: "relu7"
  388. type: "ReLU"
  389. bottom: "scale7"
  390. top: "scale7"
  391. relu_param{
  392. negative_slope: 0.1
  393. }
  394. }
  395.  
  396. layer{
  397. name: "conv8"
  398. type: "Convolution"
  399. bottom: "scale7"
  400. top: "conv8"
  401. convolution_param {
  402. num_output: 256
  403. kernel_size: 3
  404. pad: 1
  405. stride: 1
  406. bias_term: false
  407. }
  408. }
  409. layer {
  410. name: "bn8"
  411. type: "BatchNorm"
  412. bottom: "conv8"
  413. top: "bn8"
  414. param {
  415. lr_mult: 0
  416. }
  417. param {
  418. lr_mult: 0
  419. }
  420. param {
  421. lr_mult: 0
  422. }
  423. }
  424. layer {
  425. name: "scale8"
  426. type: "Scale"
  427. bottom: "bn8"
  428. top: "scale8"
  429. scale_param {
  430. bias_term: true
  431. }
  432. }
  433. layer {
  434. name: "relu8"
  435. type: "ReLU"
  436. bottom: "scale8"
  437. top: "scale8"
  438. relu_param{
  439. negative_slope: 0.1
  440. }
  441. }
  442. layer {
  443. name: "pool8"
  444. type: "Pooling"
  445. bottom: "scale8"
  446. top: "pool8"
  447. pooling_param {
  448. pool: MAX
  449. kernel_size: 2
  450. stride: 2
  451. }
  452. }
  453.  
  454. layer{
  455. name: "conv9"
  456. type: "Convolution"
  457. bottom: "pool8"
  458. top: "conv9"
  459. convolution_param {
  460. num_output: 512
  461. kernel_size: 3
  462. pad: 1
  463. stride: 1
  464. bias_term: false
  465. }
  466. }
  467. layer {
  468. name: "bn9"
  469. type: "BatchNorm"
  470. bottom: "conv9"
  471. top: "bn9"
  472. param {
  473. lr_mult: 0
  474. }
  475. param {
  476. lr_mult: 0
  477. }
  478. param {
  479. lr_mult: 0
  480. }
  481. }
  482. layer {
  483. name: "scale9"
  484. type: "Scale"
  485. bottom: "bn9"
  486. top: "scale9"
  487. scale_param {
  488. bias_term: true
  489. }
  490. }
  491. layer {
  492. name: "relu9"
  493. type: "ReLU"
  494. bottom: "scale9"
  495. top: "scale9"
  496. relu_param{
  497. negative_slope: 0.1
  498. }
  499. }
  500.  
  501. layer{
  502. name: "conv10"
  503. type: "Convolution"
  504. bottom: "scale9"
  505. top: "conv10"
  506. convolution_param {
  507. num_output: 256
  508. kernel_size: 1
  509. pad: 0
  510. stride: 1
  511. bias_term: false
  512. }
  513. }
  514. layer {
  515. name: "bn10"
  516. type: "BatchNorm"
  517. bottom: "conv10"
  518. top: "bn10"
  519. param {
  520. lr_mult: 0
  521. }
  522. param {
  523. lr_mult: 0
  524. }
  525. param {
  526. lr_mult: 0
  527. }
  528. }
  529. layer {
  530. name: "scale10"
  531. type: "Scale"
  532. bottom: "bn10"
  533. top: "scale10"
  534. scale_param {
  535. bias_term: true
  536. }
  537. }
  538. layer {
  539. name: "relu10"
  540. type: "ReLU"
  541. bottom: "scale10"
  542. top: "scale10"
  543. relu_param{
  544. negative_slope: 0.1
  545. }
  546. }
  547.  
  548. layer{
  549. name: "conv11"
  550. type: "Convolution"
  551. bottom: "scale10"
  552. top: "conv11"
  553. convolution_param {
  554. num_output: 512
  555. kernel_size: 3
  556. pad: 1
  557. stride: 1
  558. bias_term: false
  559. }
  560. }
  561. layer {
  562. name: "bn11"
  563. type: "BatchNorm"
  564. bottom: "conv11"
  565. top: "bn11"
  566. param {
  567. lr_mult: 0
  568. }
  569. param {
  570. lr_mult: 0
  571. }
  572. param {
  573. lr_mult: 0
  574. }
  575. }
  576. layer {
  577. name: "scale11"
  578. type: "Scale"
  579. bottom: "bn11"
  580. top: "scale11"
  581. scale_param {
  582. bias_term: true
  583. }
  584. }
  585. layer {
  586. name: "relu11"
  587. type: "ReLU"
  588. bottom: "scale11"
  589. top: "scale11"
  590. relu_param{
  591. negative_slope: 0.1
  592. }
  593. }
  594.  
  595. layer{
  596. name: "conv12"
  597. type: "Convolution"
  598. bottom: "scale11"
  599. top: "conv12"
  600. convolution_param {
  601. num_output: 256
  602. kernel_size: 1
  603. pad: 0
  604. stride: 1
  605. bias_term: false
  606. }
  607. }
  608. layer {
  609. name: "bn12"
  610. type: "BatchNorm"
  611. bottom: "conv12"
  612. top: "bn12"
  613. param {
  614. lr_mult: 0
  615. }
  616. param {
  617. lr_mult: 0
  618. }
  619. param {
  620. lr_mult: 0
  621. }
  622. }
  623. layer {
  624. name: "scale12"
  625. type: "Scale"
  626. bottom: "bn12"
  627. top: "scale12"
  628. scale_param {
  629. bias_term: true
  630. }
  631. }
  632. layer {
  633. name: "relu12"
  634. type: "ReLU"
  635. bottom: "scale12"
  636. top: "scale12"
  637. relu_param{
  638. negative_slope: 0.1
  639. }
  640. }
  641.  
  642. layer{
  643. name: "conv13"
  644. type: "Convolution"
  645. bottom: "scale12"
  646. top: "conv13"
  647. convolution_param {
  648. num_output: 512
  649. kernel_size: 3
  650. pad: 1
  651. stride: 1
  652. bias_term: false
  653. }
  654. }
  655. layer {
  656. name: "bn13"
  657. type: "BatchNorm"
  658. bottom: "conv13"
  659. top: "bn13"
  660. param {
  661. lr_mult: 0
  662. }
  663. param {
  664. lr_mult: 0
  665. }
  666. param {
  667. lr_mult: 0
  668. }
  669. }
  670. layer {
  671. name: "scale13"
  672. type: "Scale"
  673. bottom: "bn13"
  674. top: "scale13"
  675. scale_param {
  676. bias_term: true
  677. }
  678. }
  679. layer {
  680. name: "relu13"
  681. type: "ReLU"
  682. bottom: "scale13"
  683. top: "scale13"
  684. relu_param{
  685. negative_slope: 0.1
  686. }
  687. }
  688. layer {
  689. name: "pool13"
  690. type: "Pooling"
  691. bottom: "scale13"
  692. top: "pool13"
  693. pooling_param {
  694. pool: MAX
  695. kernel_size: 2
  696. stride: 2
  697. }
  698. }
  699.  
  700. layer{
  701. name: "conv14"
  702. type: "Convolution"
  703. bottom: "pool13"
  704. top: "conv14"
  705. convolution_param {
  706. num_output: 1024
  707. kernel_size: 3
  708. pad: 1
  709. stride: 1
  710. bias_term: false
  711. }
  712. }
  713. layer {
  714. name: "bn14"
  715. type: "BatchNorm"
  716. bottom: "conv14"
  717. top: "bn14"
  718. param {
  719. lr_mult: 0
  720. }
  721. param {
  722. lr_mult: 0
  723. }
  724. param {
  725. lr_mult: 0
  726. }
  727. }
  728. layer {
  729. name: "scale14"
  730. type: "Scale"
  731. bottom: "bn14"
  732. top: "scale14"
  733. scale_param {
  734. bias_term: true
  735. }
  736. }
  737. layer {
  738. name: "relu14"
  739. type: "ReLU"
  740. bottom: "scale14"
  741. top: "scale14"
  742. relu_param{
  743. negative_slope: 0.1
  744. }
  745. }
  746.  
  747. layer{
  748. name: "conv15"
  749. type: "Convolution"
  750. bottom: "scale14"
  751. top: "conv15"
  752. convolution_param {
  753. num_output: 512
  754. kernel_size: 1
  755. pad: 0
  756. stride: 1
  757. bias_term: false
  758. }
  759. }
  760. layer {
  761. name: "bn15"
  762. type: "BatchNorm"
  763. bottom: "conv15"
  764. top: "bn15"
  765. param {
  766. lr_mult: 0
  767. }
  768. param {
  769. lr_mult: 0
  770. }
  771. param {
  772. lr_mult: 0
  773. }
  774. }
  775. layer {
  776. name: "scale15"
  777. type: "Scale"
  778. bottom: "bn15"
  779. top: "scale15"
  780. scale_param {
  781. bias_term: true
  782. }
  783. }
  784. layer {
  785. name: "relu15"
  786. type: "ReLU"
  787. bottom: "scale15"
  788. top: "scale15"
  789. relu_param{
  790. negative_slope: 0.1
  791. }
  792. }
  793.  
  794.  
  795. layer{
  796. name: "conv16"
  797. type: "Convolution"
  798. bottom: "scale15"
  799. top: "conv16"
  800. convolution_param {
  801. num_output: 1024
  802. kernel_size: 3
  803. pad: 1
  804. stride: 1
  805. bias_term: false
  806. }
  807. }
  808. layer {
  809. name: "bn16"
  810. type: "BatchNorm"
  811. bottom: "conv16"
  812. top: "bn16"
  813. param {
  814. lr_mult: 0
  815. }
  816. param {
  817. lr_mult: 0
  818. }
  819. param {
  820. lr_mult: 0
  821. }
  822. }
  823. layer {
  824. name: "scale16"
  825. type: "Scale"
  826. bottom: "bn16"
  827. top: "scale16"
  828. scale_param {
  829. bias_term: true
  830. }
  831. }
  832. layer {
  833. name: "relu16"
  834. type: "ReLU"
  835. bottom: "scale16"
  836. top: "scale16"
  837. relu_param{
  838. negative_slope: 0.1
  839. }
  840. }
  841.  
  842. layer{
  843. name: "conv17"
  844. type: "Convolution"
  845. bottom: "scale16"
  846. top: "conv17"
  847. convolution_param {
  848. num_output: 512
  849. kernel_size: 1
  850. pad: 0
  851. stride: 1
  852. bias_term: false
  853. }
  854. }
  855. layer {
  856. name: "bn17"
  857. type: "BatchNorm"
  858. bottom: "conv17"
  859. top: "bn17"
  860. param {
  861. lr_mult: 0
  862. }
  863. param {
  864. lr_mult: 0
  865. }
  866. param {
  867. lr_mult: 0
  868. }
  869. }
  870. layer {
  871. name: "scale17"
  872. type: "Scale"
  873. bottom: "bn17"
  874. top: "scale17"
  875. scale_param {
  876. bias_term: true
  877. }
  878. }
  879. layer {
  880. name: "relu17"
  881. type: "ReLU"
  882. bottom: "scale17"
  883. top: "scale17"
  884. relu_param{
  885. negative_slope: 0.1
  886. }
  887. }
  888.  
  889.  
  890. layer{
  891. name: "conv18"
  892. type: "Convolution"
  893. bottom: "scale17"
  894. top: "conv18"
  895. convolution_param {
  896. num_output: 1024
  897. kernel_size: 3
  898. pad: 1
  899. stride: 1
  900. bias_term: false
  901. }
  902. }
  903. layer {
  904. name: "bn18"
  905. type: "BatchNorm"
  906. bottom: "conv18"
  907. top: "bn18"
  908. param {
  909. lr_mult: 0
  910. }
  911. param {
  912. lr_mult: 0
  913. }
  914. param {
  915. lr_mult: 0
  916. }
  917. }
  918. layer {
  919. name: "scale18"
  920. type: "Scale"
  921. bottom: "bn18"
  922. top: "scale18"
  923. scale_param {
  924. bias_term: true
  925. }
  926. }
  927. layer {
  928. name: "relu18"
  929. type: "ReLU"
  930. bottom: "scale18"
  931. top: "scale18"
  932. relu_param{
  933. negative_slope: 0.1
  934. }
  935. }
  936.  
  937.  
  938.  
  939. layer{
  940. name: "conv19"
  941. type: "Convolution"
  942. bottom: "scale18"
  943. top: "conv19"
  944. convolution_param {
  945. num_output: 1024
  946. kernel_size: 3
  947. pad: 1
  948. stride: 1
  949. bias_term: false
  950. }
  951. }
  952. layer {
  953. name: "bn19"
  954. type: "BatchNorm"
  955. bottom: "conv19"
  956. top: "bn19"
  957. param {
  958. lr_mult: 0
  959. }
  960. param {
  961. lr_mult: 0
  962. }
  963. param {
  964. lr_mult: 0
  965. }
  966. }
  967. layer {
  968. name: "scale19"
  969. type: "Scale"
  970. bottom: "bn19"
  971. top: "scale19"
  972. scale_param {
  973. bias_term: true
  974. }
  975. }
  976. layer {
  977. name: "relu19"
  978. type: "ReLU"
  979. bottom: "scale19"
  980. top: "scale19"
  981. relu_param{
  982. negative_slope: 0.1
  983. }
  984. }
  985.  
  986.  
  987.  
  988. layer{
  989. name: "conv20"
  990. type: "Convolution"
  991. bottom: "scale19"
  992. top: "conv20"
  993. convolution_param {
  994. num_output: 1024
  995. kernel_size: 3
  996. pad: 1
  997. stride: 1
  998. bias_term: false
  999. }
  1000.  
  1001. }
  1002. layer {
  1003. name: "bn20"
  1004. type: "BatchNorm"
  1005. bottom: "conv20"
  1006. top: "bn20"
  1007. param {
  1008. lr_mult: 0
  1009. }
  1010. param {
  1011. lr_mult: 0
  1012. }
  1013. param {
  1014. lr_mult: 0
  1015. }
  1016. }
  1017. layer {
  1018. name: "scale20"
  1019. type: "Scale"
  1020. bottom: "bn20"
  1021. top: "scale20"
  1022. scale_param {
  1023. bias_term: true
  1024. }
  1025. }
  1026. layer {
  1027. name: "relu20"
  1028. type: "ReLU"
  1029. bottom: "scale20"
  1030. top: "scale20"
  1031. relu_param {
  1032. negative_slope: 0.1
  1033. }
  1034. }
  1035.  
  1036. layer {
  1037. name: "concat1"
  1038. type: "Concat"
  1039. bottom: "scale13"
  1040. top: "concat1"
  1041. }
  1042.  
  1043. layer {
  1044. name: "reorg1"
  1045. type: "Reorg"
  1046. bottom: "concat1"
  1047. top: "reorg1"
  1048. reorg_param {
  1049. stride: 2
  1050. }
  1051. }
  1052.  
  1053. layer {
  1054. name: "concat2"
  1055. type: "Concat"
  1056. bottom: "reorg1"
  1057. bottom: "scale20"
  1058. top: "concat2"
  1059. }
  1060.  
  1061. layer{
  1062. name: "conv21"
  1063. type: "Convolution"
  1064. bottom: "concat2"
  1065. top: "conv21"
  1066. convolution_param {
  1067. num_output: 1024
  1068. kernel_size: 3
  1069. pad: 1
  1070. stride: 1
  1071. bias_term: false
  1072. }
  1073. }
  1074. layer {
  1075. name: "bn21"
  1076. type: "BatchNorm"
  1077. bottom: "conv21"
  1078. top: "bn21"
  1079. param {
  1080. lr_mult: 0
  1081. }
  1082. param {
  1083. lr_mult: 0
  1084. }
  1085. param {
  1086. lr_mult: 0
  1087. }
  1088. }
  1089. layer {
  1090. name: "scale21"
  1091. type: "Scale"
  1092. bottom: "bn21"
  1093. top: "scale21"
  1094. scale_param {
  1095. bias_term: true
  1096. }
  1097. }
  1098. layer {
  1099. name: "relu21"
  1100. type: "ReLU"
  1101. bottom: "scale21"
  1102. top: "scale21"
  1103. relu_param{
  1104. negative_slope: 0.1
  1105. }
  1106. }
  1107.  
  1108. layer {
  1109. name: "conv22_indoor"
  1110. type: "Convolution"
  1111. bottom: "scale21"
  1112. top: "conv22"
  1113. convolution_param {
  1114. num_output: 70
  1115. kernel_size: 1
  1116. pad: 0
  1117. stride: 1
  1118. }
  1119. }
  1120.  
  1121. layer {
  1122. name: "region1"
  1123. type: "RegionLoss"
  1124. bottom: "conv22"
  1125. bottom: "label"
  1126. top: "region1"
  1127. region_loss_param {
  1128. side: 13
  1129. num_class: 9
  1130. coords: 4
  1131. num: 5
  1132. softmax: 1
  1133. jitter: 0.2
  1134. rescore: 1
  1135.  
  1136. object_scale: 5.0
  1137. noobject_scale: 1.0
  1138. class_scale: 1.0
  1139. coord_scale: 1.0
  1140.  
  1141. absolute: 1
  1142. thresh: 0.5
  1143. random: 0
  1144.  
  1145. biases: 0.738768
  1146. biases: 0.874946
  1147. biases: 2.42204
  1148. biases: 2.65704
  1149. biases: 4.30971
  1150. biases: 7.04493
  1151. biases: 10.246
  1152. biases: 4.59428
  1153. biases: 12.6868
  1154. biases: 11.8741
  1155. }
  1156. }
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