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  1. name: "MobileNetv2-SSDLite"
  2. input: "data"
  3. input_shape {
  4. dim: 1
  5. dim: 3
  6. dim: 300
  7. dim: 300
  8. }
  9. layer {
  10. name: "Conv"
  11. type: "Convolution"
  12. bottom: "data"
  13. top: "Conv"
  14. param {
  15. lr_mult: 1.0
  16. decay_mult: 1.0
  17. }
  18. param {
  19. lr_mult: 2.0
  20. decay_mult: 0.0
  21. }
  22. convolution_param {
  23. num_output: 32
  24. pad: 1
  25. kernel_size: 3
  26. stride: 2
  27. weight_filler {
  28. type: "msra"
  29. }
  30. bias_filler {
  31. type: "constant"
  32. value: 0.0
  33. }
  34. }
  35. }
  36. layer {
  37. name: "Conv/relu"
  38. type: "ReLU"
  39. bottom: "Conv"
  40. top: "Conv"
  41. }
  42. layer {
  43. name: "conv/depthwise"
  44. type: "Convolution"
  45. bottom: "Conv"
  46. top: "conv/depthwise"
  47. param {
  48. lr_mult: 1.0
  49. decay_mult: 1.0
  50. }
  51. param {
  52. lr_mult: 2.0
  53. decay_mult: 0.0
  54. }
  55. convolution_param {
  56. num_output: 32
  57. pad: 1
  58. kernel_size: 3
  59. group: 32
  60. #engine: CAFFE
  61. weight_filler {
  62. type: "msra"
  63. }
  64. bias_filler {
  65. type: "constant"
  66. value: 0.0
  67. }
  68. }
  69. }
  70. layer {
  71. name: "conv/depthwise/relu"
  72. type: "ReLU"
  73. bottom: "conv/depthwise"
  74. top: "conv/depthwise"
  75. }
  76. layer {
  77. name: "conv/project"
  78. type: "Convolution"
  79. bottom: "conv/depthwise"
  80. top: "conv/project"
  81. param {
  82. lr_mult: 1.0
  83. decay_mult: 1.0
  84. }
  85. param {
  86. lr_mult: 2.0
  87. decay_mult: 0.0
  88. }
  89. convolution_param {
  90. num_output: 16
  91. kernel_size: 1
  92. weight_filler {
  93. type: "msra"
  94. }
  95. bias_filler {
  96. type: "constant"
  97. value: 0.0
  98. }
  99. }
  100. }
  101. layer {
  102. name: "conv_1/expand"
  103. type: "Convolution"
  104. bottom: "conv/project"
  105. top: "conv_1/expand"
  106. param {
  107. lr_mult: 1.0
  108. decay_mult: 1.0
  109. }
  110. param {
  111. lr_mult: 2.0
  112. decay_mult: 0.0
  113. }
  114. convolution_param {
  115. num_output: 96
  116. kernel_size: 1
  117. weight_filler {
  118. type: "msra"
  119. }
  120. bias_filler {
  121. type: "constant"
  122. value: 0.0
  123. }
  124. }
  125. }
  126. layer {
  127. name: "conv_1/expand/relu"
  128. type: "ReLU"
  129. bottom: "conv_1/expand"
  130. top: "conv_1/expand"
  131. }
  132. layer {
  133. name: "conv_1/depthwise"
  134. type: "Convolution"
  135. bottom: "conv_1/expand"
  136. top: "conv_1/depthwise"
  137. param {
  138. lr_mult: 1.0
  139. decay_mult: 1.0
  140. }
  141. param {
  142. lr_mult: 2.0
  143. decay_mult: 0.0
  144. }
  145. convolution_param {
  146. num_output: 96
  147. pad: 1
  148. kernel_size: 3
  149. stride: 2
  150. group: 96
  151. #engine: CAFFE
  152. weight_filler {
  153. type: "msra"
  154. }
  155. bias_filler {
  156. type: "constant"
  157. value: 0.0
  158. }
  159. }
  160. }
  161. layer {
  162. name: "conv_1/depthwise/relu"
  163. type: "ReLU"
  164. bottom: "conv_1/depthwise"
  165. top: "conv_1/depthwise"
  166. }
  167. layer {
  168. name: "conv_1/project"
  169. type: "Convolution"
  170. bottom: "conv_1/depthwise"
  171. top: "conv_1/project"
  172. param {
  173. lr_mult: 1.0
  174. decay_mult: 1.0
  175. }
  176. param {
  177. lr_mult: 2.0
  178. decay_mult: 0.0
  179. }
  180. convolution_param {
  181. num_output: 24
  182. kernel_size: 1
  183. weight_filler {
  184. type: "msra"
  185. }
  186. bias_filler {
  187. type: "constant"
  188. value: 0.0
  189. }
  190. }
  191. }
  192. layer {
  193. name: "conv_2/expand"
  194. type: "Convolution"
  195. bottom: "conv_1/project"
  196. top: "conv_2/expand"
  197. param {
  198. lr_mult: 1.0
  199. decay_mult: 1.0
  200. }
  201. param {
  202. lr_mult: 2.0
  203. decay_mult: 0.0
  204. }
  205. convolution_param {
  206. num_output: 144
  207. kernel_size: 1
  208. weight_filler {
  209. type: "msra"
  210. }
  211. bias_filler {
  212. type: "constant"
  213. value: 0.0
  214. }
  215. }
  216. }
  217. layer {
  218. name: "conv_2/expand/relu"
  219. type: "ReLU"
  220. bottom: "conv_2/expand"
  221. top: "conv_2/expand"
  222. }
  223. layer {
  224. name: "conv_2/depthwise"
  225. type: "Convolution"
  226. bottom: "conv_2/expand"
  227. top: "conv_2/depthwise"
  228. param {
  229. lr_mult: 1.0
  230. decay_mult: 1.0
  231. }
  232. param {
  233. lr_mult: 2.0
  234. decay_mult: 0.0
  235. }
  236. convolution_param {
  237. num_output: 144
  238. pad: 1
  239. kernel_size: 3
  240. group: 144
  241. #engine: CAFFE
  242. weight_filler {
  243. type: "msra"
  244. }
  245. bias_filler {
  246. type: "constant"
  247. value: 0.0
  248. }
  249. }
  250. }
  251. layer {
  252. name: "conv_2/depthwise/relu"
  253. type: "ReLU"
  254. bottom: "conv_2/depthwise"
  255. top: "conv_2/depthwise"
  256. }
  257. layer {
  258. name: "conv_2/project"
  259. type: "Convolution"
  260. bottom: "conv_2/depthwise"
  261. top: "conv_2/project"
  262. param {
  263. lr_mult: 1.0
  264. decay_mult: 1.0
  265. }
  266. param {
  267. lr_mult: 2.0
  268. decay_mult: 0.0
  269. }
  270. convolution_param {
  271. num_output: 24
  272. kernel_size: 1
  273. weight_filler {
  274. type: "msra"
  275. }
  276. bias_filler {
  277. type: "constant"
  278. value: 0.0
  279. }
  280. }
  281. }
  282. layer {
  283. name: "conv_2/sum"
  284. type: "Eltwise"
  285. bottom: "conv_1/project"
  286. bottom: "conv_2/project"
  287. top: "conv_2"
  288. }
  289. layer {
  290. name: "conv_3/expand"
  291. type: "Convolution"
  292. bottom: "conv_2"
  293. top: "conv_3/expand"
  294. param {
  295. lr_mult: 1.0
  296. decay_mult: 1.0
  297. }
  298. param {
  299. lr_mult: 2.0
  300. decay_mult: 0.0
  301. }
  302. convolution_param {
  303. num_output: 144
  304. kernel_size: 1
  305. weight_filler {
  306. type: "msra"
  307. }
  308. bias_filler {
  309. type: "constant"
  310. value: 0.0
  311. }
  312. }
  313. }
  314. layer {
  315. name: "conv_3/expand/relu"
  316. type: "ReLU"
  317. bottom: "conv_3/expand"
  318. top: "conv_3/expand"
  319. }
  320. layer {
  321. name: "conv_3/depthwise"
  322. type: "Convolution"
  323. bottom: "conv_3/expand"
  324. top: "conv_3/depthwise"
  325. param {
  326. lr_mult: 1.0
  327. decay_mult: 1.0
  328. }
  329. param {
  330. lr_mult: 2.0
  331. decay_mult: 0.0
  332. }
  333. convolution_param {
  334. num_output: 144
  335. pad: 1
  336. kernel_size: 3
  337. stride: 2
  338. group: 144
  339. #engine: CAFFE
  340. weight_filler {
  341. type: "msra"
  342. }
  343. bias_filler {
  344. type: "constant"
  345. value: 0.0
  346. }
  347. }
  348. }
  349. layer {
  350. name: "conv_3/depthwise/relu"
  351. type: "ReLU"
  352. bottom: "conv_3/depthwise"
  353. top: "conv_3/depthwise"
  354. }
  355. layer {
  356. name: "conv_3/project"
  357. type: "Convolution"
  358. bottom: "conv_3/depthwise"
  359. top: "conv_3/project"
  360. param {
  361. lr_mult: 1.0
  362. decay_mult: 1.0
  363. }
  364. param {
  365. lr_mult: 2.0
  366. decay_mult: 0.0
  367. }
  368. convolution_param {
  369. num_output: 32
  370. kernel_size: 1
  371. weight_filler {
  372. type: "msra"
  373. }
  374. bias_filler {
  375. type: "constant"
  376. value: 0.0
  377. }
  378. }
  379. }
  380. layer {
  381. name: "conv_4/expand"
  382. type: "Convolution"
  383. bottom: "conv_3/project"
  384. top: "conv_4/expand"
  385. param {
  386. lr_mult: 1.0
  387. decay_mult: 1.0
  388. }
  389. param {
  390. lr_mult: 2.0
  391. decay_mult: 0.0
  392. }
  393. convolution_param {
  394. num_output: 192
  395. kernel_size: 1
  396. weight_filler {
  397. type: "msra"
  398. }
  399. bias_filler {
  400. type: "constant"
  401. value: 0.0
  402. }
  403. }
  404. }
  405. layer {
  406. name: "conv_4/expand/relu"
  407. type: "ReLU"
  408. bottom: "conv_4/expand"
  409. top: "conv_4/expand"
  410. }
  411. layer {
  412. name: "conv_4/depthwise"
  413. type: "Convolution"
  414. bottom: "conv_4/expand"
  415. top: "conv_4/depthwise"
  416. param {
  417. lr_mult: 1.0
  418. decay_mult: 1.0
  419. }
  420. param {
  421. lr_mult: 2.0
  422. decay_mult: 0.0
  423. }
  424. convolution_param {
  425. num_output: 192
  426. pad: 1
  427. kernel_size: 3
  428. group: 192
  429. #engine: CAFFE
  430. weight_filler {
  431. type: "msra"
  432. }
  433. bias_filler {
  434. type: "constant"
  435. value: 0.0
  436. }
  437. }
  438. }
  439. layer {
  440. name: "conv_4/depthwise/relu"
  441. type: "ReLU"
  442. bottom: "conv_4/depthwise"
  443. top: "conv_4/depthwise"
  444. }
  445. layer {
  446. name: "conv_4/project"
  447. type: "Convolution"
  448. bottom: "conv_4/depthwise"
  449. top: "conv_4/project"
  450. param {
  451. lr_mult: 1.0
  452. decay_mult: 1.0
  453. }
  454. param {
  455. lr_mult: 2.0
  456. decay_mult: 0.0
  457. }
  458. convolution_param {
  459. num_output: 32
  460. kernel_size: 1
  461. weight_filler {
  462. type: "msra"
  463. }
  464. bias_filler {
  465. type: "constant"
  466. value: 0.0
  467. }
  468. }
  469. }
  470. layer {
  471. name: "conv_4/sum"
  472. type: "Eltwise"
  473. bottom: "conv_3/project"
  474. bottom: "conv_4/project"
  475. top: "conv_4"
  476. }
  477. layer {
  478. name: "conv_5/expand"
  479. type: "Convolution"
  480. bottom: "conv_4"
  481. top: "conv_5/expand"
  482. param {
  483. lr_mult: 1.0
  484. decay_mult: 1.0
  485. }
  486. param {
  487. lr_mult: 2.0
  488. decay_mult: 0.0
  489. }
  490. convolution_param {
  491. num_output: 192
  492. kernel_size: 1
  493. weight_filler {
  494. type: "msra"
  495. }
  496. bias_filler {
  497. type: "constant"
  498. value: 0.0
  499. }
  500. }
  501. }
  502. layer {
  503. name: "conv_5/expand/relu"
  504. type: "ReLU"
  505. bottom: "conv_5/expand"
  506. top: "conv_5/expand"
  507. }
  508. layer {
  509. name: "conv_5/depthwise"
  510. type: "Convolution"
  511. bottom: "conv_5/expand"
  512. top: "conv_5/depthwise"
  513. param {
  514. lr_mult: 1.0
  515. decay_mult: 1.0
  516. }
  517. param {
  518. lr_mult: 2.0
  519. decay_mult: 0.0
  520. }
  521. convolution_param {
  522. num_output: 192
  523. pad: 1
  524. kernel_size: 3
  525. group: 192
  526. #engine: CAFFE
  527. weight_filler {
  528. type: "msra"
  529. }
  530. bias_filler {
  531. type: "constant"
  532. value: 0.0
  533. }
  534. }
  535. }
  536. layer {
  537. name: "conv_5/depthwise/relu"
  538. type: "ReLU"
  539. bottom: "conv_5/depthwise"
  540. top: "conv_5/depthwise"
  541. }
  542. layer {
  543. name: "conv_5/project"
  544. type: "Convolution"
  545. bottom: "conv_5/depthwise"
  546. top: "conv_5/project"
  547. param {
  548. lr_mult: 1.0
  549. decay_mult: 1.0
  550. }
  551. param {
  552. lr_mult: 2.0
  553. decay_mult: 0.0
  554. }
  555. convolution_param {
  556. num_output: 32
  557. kernel_size: 1
  558. weight_filler {
  559. type: "msra"
  560. }
  561. bias_filler {
  562. type: "constant"
  563. value: 0.0
  564. }
  565. }
  566. }
  567. layer {
  568. name: "conv_5/sum"
  569. type: "Eltwise"
  570. bottom: "conv_4"
  571. bottom: "conv_5/project"
  572. top: "conv_5"
  573. }
  574. layer {
  575. name: "conv_6/expand"
  576. type: "Convolution"
  577. bottom: "conv_5"
  578. top: "conv_6/expand"
  579. param {
  580. lr_mult: 1.0
  581. decay_mult: 1.0
  582. }
  583. param {
  584. lr_mult: 2.0
  585. decay_mult: 0.0
  586. }
  587. convolution_param {
  588. num_output: 192
  589. kernel_size: 1
  590. weight_filler {
  591. type: "msra"
  592. }
  593. bias_filler {
  594. type: "constant"
  595. value: 0.0
  596. }
  597. }
  598. }
  599. layer {
  600. name: "conv_6/expand/relu"
  601. type: "ReLU"
  602. bottom: "conv_6/expand"
  603. top: "conv_6/expand"
  604. }
  605. layer {
  606. name: "conv_6/depthwise"
  607. type: "Convolution"
  608. bottom: "conv_6/expand"
  609. top: "conv_6/depthwise"
  610. param {
  611. lr_mult: 1.0
  612. decay_mult: 1.0
  613. }
  614. param {
  615. lr_mult: 2.0
  616. decay_mult: 0.0
  617. }
  618. convolution_param {
  619. num_output: 192
  620. pad: 1
  621. kernel_size: 3
  622. stride: 2
  623. group: 192
  624. #engine: CAFFE
  625. weight_filler {
  626. type: "msra"
  627. }
  628. bias_filler {
  629. type: "constant"
  630. value: 0.0
  631. }
  632. }
  633. }
  634. layer {
  635. name: "conv_6/depthwise/relu"
  636. type: "ReLU"
  637. bottom: "conv_6/depthwise"
  638. top: "conv_6/depthwise"
  639. }
  640. layer {
  641. name: "conv_6/project"
  642. type: "Convolution"
  643. bottom: "conv_6/depthwise"
  644. top: "conv_6/project"
  645. param {
  646. lr_mult: 1.0
  647. decay_mult: 1.0
  648. }
  649. param {
  650. lr_mult: 2.0
  651. decay_mult: 0.0
  652. }
  653. convolution_param {
  654. num_output: 64
  655. kernel_size: 1
  656. weight_filler {
  657. type: "msra"
  658. }
  659. bias_filler {
  660. type: "constant"
  661. value: 0.0
  662. }
  663. }
  664. }
  665. layer {
  666. name: "conv_7/expand"
  667. type: "Convolution"
  668. bottom: "conv_6/project"
  669. top: "conv_7/expand"
  670. param {
  671. lr_mult: 1.0
  672. decay_mult: 1.0
  673. }
  674. param {
  675. lr_mult: 2.0
  676. decay_mult: 0.0
  677. }
  678. convolution_param {
  679. num_output: 384
  680. kernel_size: 1
  681. weight_filler {
  682. type: "msra"
  683. }
  684. bias_filler {
  685. type: "constant"
  686. value: 0.0
  687. }
  688. }
  689. }
  690. layer {
  691. name: "conv_7/expand/relu"
  692. type: "ReLU"
  693. bottom: "conv_7/expand"
  694. top: "conv_7/expand"
  695. }
  696. layer {
  697. name: "conv_7/depthwise"
  698. type: "Convolution"
  699. bottom: "conv_7/expand"
  700. top: "conv_7/depthwise"
  701. param {
  702. lr_mult: 1.0
  703. decay_mult: 1.0
  704. }
  705. param {
  706. lr_mult: 2.0
  707. decay_mult: 0.0
  708. }
  709. convolution_param {
  710. num_output: 384
  711. pad: 1
  712. kernel_size: 3
  713. group: 384
  714. #engine: CAFFE
  715. weight_filler {
  716. type: "msra"
  717. }
  718. bias_filler {
  719. type: "constant"
  720. value: 0.0
  721. }
  722. }
  723. }
  724. layer {
  725. name: "conv_7/depthwise/relu"
  726. type: "ReLU"
  727. bottom: "conv_7/depthwise"
  728. top: "conv_7/depthwise"
  729. }
  730. layer {
  731. name: "conv_7/project"
  732. type: "Convolution"
  733. bottom: "conv_7/depthwise"
  734. top: "conv_7/project"
  735. param {
  736. lr_mult: 1.0
  737. decay_mult: 1.0
  738. }
  739. param {
  740. lr_mult: 2.0
  741. decay_mult: 0.0
  742. }
  743. convolution_param {
  744. num_output: 64
  745. kernel_size: 1
  746. weight_filler {
  747. type: "msra"
  748. }
  749. bias_filler {
  750. type: "constant"
  751. value: 0.0
  752. }
  753. }
  754. }
  755. layer {
  756. name: "conv_7/sum"
  757. type: "Eltwise"
  758. bottom: "conv_6/project"
  759. bottom: "conv_7/project"
  760. top: "conv_7"
  761. }
  762. layer {
  763. name: "conv_8/expand"
  764. type: "Convolution"
  765. bottom: "conv_7"
  766. top: "conv_8/expand"
  767. param {
  768. lr_mult: 1.0
  769. decay_mult: 1.0
  770. }
  771. param {
  772. lr_mult: 2.0
  773. decay_mult: 0.0
  774. }
  775. convolution_param {
  776. num_output: 384
  777. kernel_size: 1
  778. weight_filler {
  779. type: "msra"
  780. }
  781. bias_filler {
  782. type: "constant"
  783. value: 0.0
  784. }
  785. }
  786. }
  787. layer {
  788. name: "conv_8/expand/relu"
  789. type: "ReLU"
  790. bottom: "conv_8/expand"
  791. top: "conv_8/expand"
  792. }
  793. layer {
  794. name: "conv_8/depthwise"
  795. type: "Convolution"
  796. bottom: "conv_8/expand"
  797. top: "conv_8/depthwise"
  798. param {
  799. lr_mult: 1.0
  800. decay_mult: 1.0
  801. }
  802. param {
  803. lr_mult: 2.0
  804. decay_mult: 0.0
  805. }
  806. convolution_param {
  807. num_output: 384
  808. pad: 1
  809. kernel_size: 3
  810. group: 384
  811. #engine: CAFFE
  812. weight_filler {
  813. type: "msra"
  814. }
  815. bias_filler {
  816. type: "constant"
  817. value: 0.0
  818. }
  819. }
  820. }
  821. layer {
  822. name: "conv_8/depthwise/relu"
  823. type: "ReLU"
  824. bottom: "conv_8/depthwise"
  825. top: "conv_8/depthwise"
  826. }
  827. layer {
  828. name: "conv_8/project"
  829. type: "Convolution"
  830. bottom: "conv_8/depthwise"
  831. top: "conv_8/project"
  832. param {
  833. lr_mult: 1.0
  834. decay_mult: 1.0
  835. }
  836. param {
  837. lr_mult: 2.0
  838. decay_mult: 0.0
  839. }
  840. convolution_param {
  841. num_output: 64
  842. kernel_size: 1
  843. weight_filler {
  844. type: "msra"
  845. }
  846. bias_filler {
  847. type: "constant"
  848. value: 0.0
  849. }
  850. }
  851. }
  852. layer {
  853. name: "conv_8/sum"
  854. type: "Eltwise"
  855. bottom: "conv_7"
  856. bottom: "conv_8/project"
  857. top: "conv_8"
  858. }
  859. layer {
  860. name: "conv_9/expand"
  861. type: "Convolution"
  862. bottom: "conv_8"
  863. top: "conv_9/expand"
  864. param {
  865. lr_mult: 1.0
  866. decay_mult: 1.0
  867. }
  868. param {
  869. lr_mult: 2.0
  870. decay_mult: 0.0
  871. }
  872. convolution_param {
  873. num_output: 384
  874. kernel_size: 1
  875. weight_filler {
  876. type: "msra"
  877. }
  878. bias_filler {
  879. type: "constant"
  880. value: 0.0
  881. }
  882. }
  883. }
  884. layer {
  885. name: "conv_9/expand/relu"
  886. type: "ReLU"
  887. bottom: "conv_9/expand"
  888. top: "conv_9/expand"
  889. }
  890. layer {
  891. name: "conv_9/depthwise"
  892. type: "Convolution"
  893. bottom: "conv_9/expand"
  894. top: "conv_9/depthwise"
  895. param {
  896. lr_mult: 1.0
  897. decay_mult: 1.0
  898. }
  899. param {
  900. lr_mult: 2.0
  901. decay_mult: 0.0
  902. }
  903. convolution_param {
  904. num_output: 384
  905. pad: 1
  906. kernel_size: 3
  907. group: 384
  908. #engine: CAFFE
  909. weight_filler {
  910. type: "msra"
  911. }
  912. bias_filler {
  913. type: "constant"
  914. value: 0.0
  915. }
  916. }
  917. }
  918. layer {
  919. name: "conv_9/depthwise/relu"
  920. type: "ReLU"
  921. bottom: "conv_9/depthwise"
  922. top: "conv_9/depthwise"
  923. }
  924. layer {
  925. name: "conv_9/project"
  926. type: "Convolution"
  927. bottom: "conv_9/depthwise"
  928. top: "conv_9/project"
  929. param {
  930. lr_mult: 1.0
  931. decay_mult: 1.0
  932. }
  933. param {
  934. lr_mult: 2.0
  935. decay_mult: 0.0
  936. }
  937. convolution_param {
  938. num_output: 64
  939. kernel_size: 1
  940. weight_filler {
  941. type: "msra"
  942. }
  943. bias_filler {
  944. type: "constant"
  945. value: 0.0
  946. }
  947. }
  948. }
  949. layer {
  950. name: "conv_9/sum"
  951. type: "Eltwise"
  952. bottom: "conv_8"
  953. bottom: "conv_9/project"
  954. top: "conv_9"
  955. }
  956. layer {
  957. name: "conv_10/expand"
  958. type: "Convolution"
  959. bottom: "conv_9"
  960. top: "conv_10/expand"
  961. param {
  962. lr_mult: 1.0
  963. decay_mult: 1.0
  964. }
  965. param {
  966. lr_mult: 2.0
  967. decay_mult: 0.0
  968. }
  969. convolution_param {
  970. num_output: 384
  971. kernel_size: 1
  972. weight_filler {
  973. type: "msra"
  974. }
  975. bias_filler {
  976. type: "constant"
  977. value: 0.0
  978. }
  979. }
  980. }
  981. layer {
  982. name: "conv_10/expand/relu"
  983. type: "ReLU"
  984. bottom: "conv_10/expand"
  985. top: "conv_10/expand"
  986. }
  987. layer {
  988. name: "conv_10/depthwise"
  989. type: "Convolution"
  990. bottom: "conv_10/expand"
  991. top: "conv_10/depthwise"
  992. param {
  993. lr_mult: 1.0
  994. decay_mult: 1.0
  995. }
  996. param {
  997. lr_mult: 2.0
  998. decay_mult: 0.0
  999. }
  1000. convolution_param {
  1001. num_output: 384
  1002. pad: 1
  1003. kernel_size: 3
  1004. group: 384
  1005. #engine: CAFFE
  1006. weight_filler {
  1007. type: "msra"
  1008. }
  1009. bias_filler {
  1010. type: "constant"
  1011. value: 0.0
  1012. }
  1013. }
  1014. }
  1015. layer {
  1016. name: "conv_10/depthwise/relu"
  1017. type: "ReLU"
  1018. bottom: "conv_10/depthwise"
  1019. top: "conv_10/depthwise"
  1020. }
  1021. layer {
  1022. name: "conv_10/project"
  1023. type: "Convolution"
  1024. bottom: "conv_10/depthwise"
  1025. top: "conv_10/project"
  1026. param {
  1027. lr_mult: 1.0
  1028. decay_mult: 1.0
  1029. }
  1030. param {
  1031. lr_mult: 2.0
  1032. decay_mult: 0.0
  1033. }
  1034. convolution_param {
  1035. num_output: 96
  1036. kernel_size: 1
  1037. weight_filler {
  1038. type: "msra"
  1039. }
  1040. bias_filler {
  1041. type: "constant"
  1042. value: 0.0
  1043. }
  1044. }
  1045. }
  1046. layer {
  1047. name: "conv_11/expand"
  1048. type: "Convolution"
  1049. bottom: "conv_10/project"
  1050. top: "conv_11/expand"
  1051. param {
  1052. lr_mult: 1.0
  1053. decay_mult: 1.0
  1054. }
  1055. param {
  1056. lr_mult: 2.0
  1057. decay_mult: 0.0
  1058. }
  1059. convolution_param {
  1060. num_output: 576
  1061. kernel_size: 1
  1062. weight_filler {
  1063. type: "msra"
  1064. }
  1065. bias_filler {
  1066. type: "constant"
  1067. value: 0.0
  1068. }
  1069. }
  1070. }
  1071. layer {
  1072. name: "conv_11/expand/relu"
  1073. type: "ReLU"
  1074. bottom: "conv_11/expand"
  1075. top: "conv_11/expand"
  1076. }
  1077. layer {
  1078. name: "conv_11/depthwise"
  1079. type: "Convolution"
  1080. bottom: "conv_11/expand"
  1081. top: "conv_11/depthwise"
  1082. param {
  1083. lr_mult: 1.0
  1084. decay_mult: 1.0
  1085. }
  1086. param {
  1087. lr_mult: 2.0
  1088. decay_mult: 0.0
  1089. }
  1090. convolution_param {
  1091. num_output: 576
  1092. pad: 1
  1093. kernel_size: 3
  1094. group: 576
  1095. #engine: CAFFE
  1096. weight_filler {
  1097. type: "msra"
  1098. }
  1099. bias_filler {
  1100. type: "constant"
  1101. value: 0.0
  1102. }
  1103. }
  1104. }
  1105. layer {
  1106. name: "conv_11/depthwise/relu"
  1107. type: "ReLU"
  1108. bottom: "conv_11/depthwise"
  1109. top: "conv_11/depthwise"
  1110. }
  1111. layer {
  1112. name: "conv_11/project"
  1113. type: "Convolution"
  1114. bottom: "conv_11/depthwise"
  1115. top: "conv_11/project"
  1116. param {
  1117. lr_mult: 1.0
  1118. decay_mult: 1.0
  1119. }
  1120. param {
  1121. lr_mult: 2.0
  1122. decay_mult: 0.0
  1123. }
  1124. convolution_param {
  1125. num_output: 96
  1126. kernel_size: 1
  1127. weight_filler {
  1128. type: "msra"
  1129. }
  1130. bias_filler {
  1131. type: "constant"
  1132. value: 0.0
  1133. }
  1134. }
  1135. }
  1136. layer {
  1137. name: "conv_11/sum"
  1138. type: "Eltwise"
  1139. bottom: "conv_10/project"
  1140. bottom: "conv_11/project"
  1141. top: "conv_11"
  1142. }
  1143. layer {
  1144. name: "conv_12/expand"
  1145. type: "Convolution"
  1146. bottom: "conv_11"
  1147. top: "conv_12/expand"
  1148. param {
  1149. lr_mult: 1.0
  1150. decay_mult: 1.0
  1151. }
  1152. param {
  1153. lr_mult: 2.0
  1154. decay_mult: 0.0
  1155. }
  1156. convolution_param {
  1157. num_output: 576
  1158. kernel_size: 1
  1159. weight_filler {
  1160. type: "msra"
  1161. }
  1162. bias_filler {
  1163. type: "constant"
  1164. value: 0.0
  1165. }
  1166. }
  1167. }
  1168. layer {
  1169. name: "conv_12/expand/relu"
  1170. type: "ReLU"
  1171. bottom: "conv_12/expand"
  1172. top: "conv_12/expand"
  1173. }
  1174. layer {
  1175. name: "conv_12/depthwise"
  1176. type: "Convolution"
  1177. bottom: "conv_12/expand"
  1178. top: "conv_12/depthwise"
  1179. param {
  1180. lr_mult: 1.0
  1181. decay_mult: 1.0
  1182. }
  1183. param {
  1184. lr_mult: 2.0
  1185. decay_mult: 0.0
  1186. }
  1187. convolution_param {
  1188. num_output: 576
  1189. pad: 1
  1190. kernel_size: 3
  1191. group: 576
  1192. #engine: CAFFE
  1193. weight_filler {
  1194. type: "msra"
  1195. }
  1196. bias_filler {
  1197. type: "constant"
  1198. value: 0.0
  1199. }
  1200. }
  1201. }
  1202. layer {
  1203. name: "conv_12/depthwise/relu"
  1204. type: "ReLU"
  1205. bottom: "conv_12/depthwise"
  1206. top: "conv_12/depthwise"
  1207. }
  1208. layer {
  1209. name: "conv_12/project"
  1210. type: "Convolution"
  1211. bottom: "conv_12/depthwise"
  1212. top: "conv_12/project"
  1213. param {
  1214. lr_mult: 1.0
  1215. decay_mult: 1.0
  1216. }
  1217. param {
  1218. lr_mult: 2.0
  1219. decay_mult: 0.0
  1220. }
  1221. convolution_param {
  1222. num_output: 96
  1223. kernel_size: 1
  1224. weight_filler {
  1225. type: "msra"
  1226. }
  1227. bias_filler {
  1228. type: "constant"
  1229. value: 0.0
  1230. }
  1231. }
  1232. }
  1233. layer {
  1234. name: "conv_12/sum"
  1235. type: "Eltwise"
  1236. bottom: "conv_11"
  1237. bottom: "conv_12/project"
  1238. top: "conv_12"
  1239. }
  1240. layer {
  1241. name: "conv_13/expand"
  1242. type: "Convolution"
  1243. bottom: "conv_12"
  1244. top: "conv_13/expand"
  1245. param {
  1246. lr_mult: 1.0
  1247. decay_mult: 1.0
  1248. }
  1249. param {
  1250. lr_mult: 2.0
  1251. decay_mult: 0.0
  1252. }
  1253. convolution_param {
  1254. num_output: 576
  1255. kernel_size: 1
  1256. weight_filler {
  1257. type: "msra"
  1258. }
  1259. bias_filler {
  1260. type: "constant"
  1261. value: 0.0
  1262. }
  1263. }
  1264. }
  1265. layer {
  1266. name: "conv_13/expand/relu"
  1267. type: "ReLU"
  1268. bottom: "conv_13/expand"
  1269. top: "conv_13/expand"
  1270. }
  1271. layer {
  1272. name: "conv_13/depthwise"
  1273. type: "Convolution"
  1274. bottom: "conv_13/expand"
  1275. top: "conv_13/depthwise"
  1276. param {
  1277. lr_mult: 1.0
  1278. decay_mult: 1.0
  1279. }
  1280. param {
  1281. lr_mult: 2.0
  1282. decay_mult: 0.0
  1283. }
  1284. convolution_param {
  1285. num_output: 576
  1286. pad: 1
  1287. kernel_size: 3
  1288. stride: 2
  1289. group: 576
  1290. #engine: CAFFE
  1291. weight_filler {
  1292. type: "msra"
  1293. }
  1294. bias_filler {
  1295. type: "constant"
  1296. value: 0.0
  1297. }
  1298. }
  1299. }
  1300. layer {
  1301. name: "conv_13/depthwise/relu"
  1302. type: "ReLU"
  1303. bottom: "conv_13/depthwise"
  1304. top: "conv_13/depthwise"
  1305. }
  1306. layer {
  1307. name: "conv_13/project"
  1308. type: "Convolution"
  1309. bottom: "conv_13/depthwise"
  1310. top: "conv_13/project"
  1311. param {
  1312. lr_mult: 1.0
  1313. decay_mult: 1.0
  1314. }
  1315. param {
  1316. lr_mult: 2.0
  1317. decay_mult: 0.0
  1318. }
  1319. convolution_param {
  1320. num_output: 160
  1321. kernel_size: 1
  1322. weight_filler {
  1323. type: "msra"
  1324. }
  1325. bias_filler {
  1326. type: "constant"
  1327. value: 0.0
  1328. }
  1329. }
  1330. }
  1331. layer {
  1332. name: "conv_14/expand"
  1333. type: "Convolution"
  1334. bottom: "conv_13/project"
  1335. top: "conv_14/expand"
  1336. param {
  1337. lr_mult: 1.0
  1338. decay_mult: 1.0
  1339. }
  1340. param {
  1341. lr_mult: 2.0
  1342. decay_mult: 0.0
  1343. }
  1344. convolution_param {
  1345. num_output: 960
  1346. kernel_size: 1
  1347. weight_filler {
  1348. type: "msra"
  1349. }
  1350. bias_filler {
  1351. type: "constant"
  1352. value: 0.0
  1353. }
  1354. }
  1355. }
  1356. layer {
  1357. name: "conv_14/expand/relu"
  1358. type: "ReLU"
  1359. bottom: "conv_14/expand"
  1360. top: "conv_14/expand"
  1361. }
  1362. layer {
  1363. name: "conv_14/depthwise"
  1364. type: "Convolution"
  1365. bottom: "conv_14/expand"
  1366. top: "conv_14/depthwise"
  1367. param {
  1368. lr_mult: 1.0
  1369. decay_mult: 1.0
  1370. }
  1371. param {
  1372. lr_mult: 2.0
  1373. decay_mult: 0.0
  1374. }
  1375. convolution_param {
  1376. num_output: 960
  1377. pad: 1
  1378. kernel_size: 3
  1379. group: 960
  1380. #engine: CAFFE
  1381. weight_filler {
  1382. type: "msra"
  1383. }
  1384. bias_filler {
  1385. type: "constant"
  1386. value: 0.0
  1387. }
  1388. }
  1389. }
  1390. layer {
  1391. name: "conv_14/depthwise/relu"
  1392. type: "ReLU"
  1393. bottom: "conv_14/depthwise"
  1394. top: "conv_14/depthwise"
  1395. }
  1396. layer {
  1397. name: "conv_14/project"
  1398. type: "Convolution"
  1399. bottom: "conv_14/depthwise"
  1400. top: "conv_14/project"
  1401. param {
  1402. lr_mult: 1.0
  1403. decay_mult: 1.0
  1404. }
  1405. param {
  1406. lr_mult: 2.0
  1407. decay_mult: 0.0
  1408. }
  1409. convolution_param {
  1410. num_output: 160
  1411. kernel_size: 1
  1412. weight_filler {
  1413. type: "msra"
  1414. }
  1415. bias_filler {
  1416. type: "constant"
  1417. value: 0.0
  1418. }
  1419. }
  1420. }
  1421. layer {
  1422. name: "conv_14/sum"
  1423. type: "Eltwise"
  1424. bottom: "conv_13/project"
  1425. bottom: "conv_14/project"
  1426. top: "conv_14"
  1427. }
  1428. layer {
  1429. name: "conv_15/expand"
  1430. type: "Convolution"
  1431. bottom: "conv_14"
  1432. top: "conv_15/expand"
  1433. param {
  1434. lr_mult: 1.0
  1435. decay_mult: 1.0
  1436. }
  1437. param {
  1438. lr_mult: 2.0
  1439. decay_mult: 0.0
  1440. }
  1441. convolution_param {
  1442. num_output: 960
  1443. kernel_size: 1
  1444. weight_filler {
  1445. type: "msra"
  1446. }
  1447. bias_filler {
  1448. type: "constant"
  1449. value: 0.0
  1450. }
  1451. }
  1452. }
  1453. layer {
  1454. name: "conv_15/expand/relu"
  1455. type: "ReLU"
  1456. bottom: "conv_15/expand"
  1457. top: "conv_15/expand"
  1458. }
  1459. layer {
  1460. name: "conv_15/depthwise"
  1461. type: "Convolution"
  1462. bottom: "conv_15/expand"
  1463. top: "conv_15/depthwise"
  1464. param {
  1465. lr_mult: 1.0
  1466. decay_mult: 1.0
  1467. }
  1468. param {
  1469. lr_mult: 2.0
  1470. decay_mult: 0.0
  1471. }
  1472. convolution_param {
  1473. num_output: 960
  1474. pad: 1
  1475. kernel_size: 3
  1476. group: 960
  1477. #engine: CAFFE
  1478. weight_filler {
  1479. type: "msra"
  1480. }
  1481. bias_filler {
  1482. type: "constant"
  1483. value: 0.0
  1484. }
  1485. }
  1486. }
  1487. layer {
  1488. name: "conv_15/depthwise/relu"
  1489. type: "ReLU"
  1490. bottom: "conv_15/depthwise"
  1491. top: "conv_15/depthwise"
  1492. }
  1493. layer {
  1494. name: "conv_15/project"
  1495. type: "Convolution"
  1496. bottom: "conv_15/depthwise"
  1497. top: "conv_15/project"
  1498. param {
  1499. lr_mult: 1.0
  1500. decay_mult: 1.0
  1501. }
  1502. param {
  1503. lr_mult: 2.0
  1504. decay_mult: 0.0
  1505. }
  1506. convolution_param {
  1507. num_output: 160
  1508. kernel_size: 1
  1509. weight_filler {
  1510. type: "msra"
  1511. }
  1512. bias_filler {
  1513. type: "constant"
  1514. value: 0.0
  1515. }
  1516. }
  1517. }
  1518. layer {
  1519. name: "conv_15/sum"
  1520. type: "Eltwise"
  1521. bottom: "conv_14"
  1522. bottom: "conv_15/project"
  1523. top: "conv_15"
  1524. }
  1525. layer {
  1526. name: "conv_16/expand"
  1527. type: "Convolution"
  1528. bottom: "conv_15"
  1529. top: "conv_16/expand"
  1530. param {
  1531. lr_mult: 1.0
  1532. decay_mult: 1.0
  1533. }
  1534. param {
  1535. lr_mult: 2.0
  1536. decay_mult: 0.0
  1537. }
  1538. convolution_param {
  1539. num_output: 960
  1540. kernel_size: 1
  1541. weight_filler {
  1542. type: "msra"
  1543. }
  1544. bias_filler {
  1545. type: "constant"
  1546. value: 0.0
  1547. }
  1548. }
  1549. }
  1550. layer {
  1551. name: "conv_16/expand/relu"
  1552. type: "ReLU"
  1553. bottom: "conv_16/expand"
  1554. top: "conv_16/expand"
  1555. }
  1556. layer {
  1557. name: "conv_16/depthwise"
  1558. type: "Convolution"
  1559. bottom: "conv_16/expand"
  1560. top: "conv_16/depthwise"
  1561. param {
  1562. lr_mult: 1.0
  1563. decay_mult: 1.0
  1564. }
  1565. param {
  1566. lr_mult: 2.0
  1567. decay_mult: 0.0
  1568. }
  1569. convolution_param {
  1570. num_output: 960
  1571. pad: 1
  1572. kernel_size: 3
  1573. group: 960
  1574. #engine: CAFFE
  1575. weight_filler {
  1576. type: "msra"
  1577. }
  1578. bias_filler {
  1579. type: "constant"
  1580. value: 0.0
  1581. }
  1582. }
  1583. }
  1584. layer {
  1585. name: "conv_16/depthwise/relu"
  1586. type: "ReLU"
  1587. bottom: "conv_16/depthwise"
  1588. top: "conv_16/depthwise"
  1589. }
  1590. layer {
  1591. name: "conv_16/project"
  1592. type: "Convolution"
  1593. bottom: "conv_16/depthwise"
  1594. top: "conv_16/project"
  1595. param {
  1596. lr_mult: 1.0
  1597. decay_mult: 1.0
  1598. }
  1599. param {
  1600. lr_mult: 2.0
  1601. decay_mult: 0.0
  1602. }
  1603. convolution_param {
  1604. num_output: 320
  1605. kernel_size: 1
  1606. weight_filler {
  1607. type: "msra"
  1608. }
  1609. bias_filler {
  1610. type: "constant"
  1611. value: 0.0
  1612. }
  1613. }
  1614. }
  1615. layer {
  1616. name: "Conv_1"
  1617. type: "Convolution"
  1618. bottom: "conv_16/project"
  1619. top: "Conv_1"
  1620. param {
  1621. lr_mult: 1.0
  1622. decay_mult: 1.0
  1623. }
  1624. param {
  1625. lr_mult: 2.0
  1626. decay_mult: 0.0
  1627. }
  1628. convolution_param {
  1629. num_output: 1280
  1630. kernel_size: 1
  1631. weight_filler {
  1632. type: "msra"
  1633. }
  1634. bias_filler {
  1635. type: "constant"
  1636. value: 0.0
  1637. }
  1638. }
  1639. }
  1640. layer {
  1641. name: "Conv_1/relu"
  1642. type: "ReLU"
  1643. bottom: "Conv_1"
  1644. top: "Conv_1"
  1645. }
  1646. layer {
  1647. name: "layer_19_1_2"
  1648. type: "Convolution"
  1649. bottom: "Conv_1"
  1650. top: "layer_19_1_2"
  1651. param {
  1652. lr_mult: 1.0
  1653. decay_mult: 1.0
  1654. }
  1655. param {
  1656. lr_mult: 2.0
  1657. decay_mult: 0.0
  1658. }
  1659. convolution_param {
  1660. num_output: 256
  1661. kernel_size: 1
  1662. weight_filler {
  1663. type: "msra"
  1664. }
  1665. bias_filler {
  1666. type: "constant"
  1667. value: 0.0
  1668. }
  1669. }
  1670. }
  1671. layer {
  1672. name: "layer_19_1_2/relu"
  1673. type: "ReLU"
  1674. bottom: "layer_19_1_2"
  1675. top: "layer_19_1_2"
  1676. }
  1677. layer {
  1678. name: "layer_19_2_2/depthwise"
  1679. type: "Convolution"
  1680. bottom: "layer_19_1_2"
  1681. top: "layer_19_2_2/depthwise"
  1682. param {
  1683. lr_mult: 1.0
  1684. decay_mult: 1.0
  1685. }
  1686. param {
  1687. lr_mult: 2.0
  1688. decay_mult: 0.0
  1689. }
  1690. convolution_param {
  1691. num_output: 256
  1692. pad: 1
  1693. kernel_size: 3
  1694. stride: 2
  1695. group: 256
  1696. #engine: CAFFE
  1697. weight_filler {
  1698. type: "msra"
  1699. }
  1700. bias_filler {
  1701. type: "constant"
  1702. value: 0.0
  1703. }
  1704. }
  1705. }
  1706. layer {
  1707. name: "layer_19_2_2/depthwise/relu"
  1708. type: "ReLU"
  1709. bottom: "layer_19_2_2/depthwise"
  1710. top: "layer_19_2_2/depthwise"
  1711. }
  1712. layer {
  1713. name: "layer_19_2_2"
  1714. type: "Convolution"
  1715. bottom: "layer_19_2_2/depthwise"
  1716. top: "layer_19_2_2"
  1717. param {
  1718. lr_mult: 1.0
  1719. decay_mult: 1.0
  1720. }
  1721. param {
  1722. lr_mult: 2.0
  1723. decay_mult: 0.0
  1724. }
  1725. convolution_param {
  1726. num_output: 512
  1727. kernel_size: 1
  1728. weight_filler {
  1729. type: "msra"
  1730. }
  1731. bias_filler {
  1732. type: "constant"
  1733. value: 0.0
  1734. }
  1735. }
  1736. }
  1737. layer {
  1738. name: "layer_19_2_2/relu"
  1739. type: "ReLU"
  1740. bottom: "layer_19_2_2"
  1741. top: "layer_19_2_2"
  1742. }
  1743. layer {
  1744. name: "layer_19_1_3"
  1745. type: "Convolution"
  1746. bottom: "layer_19_2_2"
  1747. top: "layer_19_1_3"
  1748. param {
  1749. lr_mult: 1.0
  1750. decay_mult: 1.0
  1751. }
  1752. param {
  1753. lr_mult: 2.0
  1754. decay_mult: 0.0
  1755. }
  1756. convolution_param {
  1757. num_output: 128
  1758. kernel_size: 1
  1759. weight_filler {
  1760. type: "msra"
  1761. }
  1762. bias_filler {
  1763. type: "constant"
  1764. value: 0.0
  1765. }
  1766. }
  1767. }
  1768. layer {
  1769. name: "layer_19_1_3/relu"
  1770. type: "ReLU"
  1771. bottom: "layer_19_1_3"
  1772. top: "layer_19_1_3"
  1773. }
  1774. layer {
  1775. name: "layer_19_2_3/depthwise"
  1776. type: "Convolution"
  1777. bottom: "layer_19_1_3"
  1778. top: "layer_19_2_3/depthwise"
  1779. param {
  1780. lr_mult: 1.0
  1781. decay_mult: 1.0
  1782. }
  1783. param {
  1784. lr_mult: 2.0
  1785. decay_mult: 0.0
  1786. }
  1787. convolution_param {
  1788. num_output: 128
  1789. pad: 1
  1790. kernel_size: 3
  1791. stride: 2
  1792. group: 128
  1793. #engine: CAFFE
  1794. weight_filler {
  1795. type: "msra"
  1796. }
  1797. bias_filler {
  1798. type: "constant"
  1799. value: 0.0
  1800. }
  1801. }
  1802. }
  1803. layer {
  1804. name: "layer_19_2_3/depthwise/relu"
  1805. type: "ReLU"
  1806. bottom: "layer_19_2_3/depthwise"
  1807. top: "layer_19_2_3/depthwise"
  1808. }
  1809. layer {
  1810. name: "layer_19_2_3"
  1811. type: "Convolution"
  1812. bottom: "layer_19_2_3/depthwise"
  1813. top: "layer_19_2_3"
  1814. param {
  1815. lr_mult: 1.0
  1816. decay_mult: 1.0
  1817. }
  1818. param {
  1819. lr_mult: 2.0
  1820. decay_mult: 0.0
  1821. }
  1822. convolution_param {
  1823. num_output: 256
  1824. kernel_size: 1
  1825. weight_filler {
  1826. type: "msra"
  1827. }
  1828. bias_filler {
  1829. type: "constant"
  1830. value: 0.0
  1831. }
  1832. }
  1833. }
  1834. layer {
  1835. name: "layer_19_2_3/relu"
  1836. type: "ReLU"
  1837. bottom: "layer_19_2_3"
  1838. top: "layer_19_2_3"
  1839. }
  1840. layer {
  1841. name: "layer_19_1_4"
  1842. type: "Convolution"
  1843. bottom: "layer_19_2_3"
  1844. top: "layer_19_1_4"
  1845. param {
  1846. lr_mult: 1.0
  1847. decay_mult: 1.0
  1848. }
  1849. param {
  1850. lr_mult: 2.0
  1851. decay_mult: 0.0
  1852. }
  1853. convolution_param {
  1854. num_output: 128
  1855. kernel_size: 1
  1856. weight_filler {
  1857. type: "msra"
  1858. }
  1859. bias_filler {
  1860. type: "constant"
  1861. value: 0.0
  1862. }
  1863. }
  1864. }
  1865. layer {
  1866. name: "layer_19_1_4/relu"
  1867. type: "ReLU"
  1868. bottom: "layer_19_1_4"
  1869. top: "layer_19_1_4"
  1870. }
  1871. layer {
  1872. name: "layer_19_2_4/depthwise"
  1873. type: "Convolution"
  1874. bottom: "layer_19_1_4"
  1875. top: "layer_19_2_4/depthwise"
  1876. param {
  1877. lr_mult: 1.0
  1878. decay_mult: 1.0
  1879. }
  1880. param {
  1881. lr_mult: 2.0
  1882. decay_mult: 0.0
  1883. }
  1884. convolution_param {
  1885. num_output: 128
  1886. pad: 1
  1887. kernel_size: 3
  1888. stride: 2
  1889. group: 128
  1890. #engine: CAFFE
  1891. weight_filler {
  1892. type: "msra"
  1893. }
  1894. bias_filler {
  1895. type: "constant"
  1896. value: 0.0
  1897. }
  1898. }
  1899. }
  1900. layer {
  1901. name: "layer_19_2_4/depthwise/relu"
  1902. type: "ReLU"
  1903. bottom: "layer_19_2_4/depthwise"
  1904. top: "layer_19_2_4/depthwise"
  1905. }
  1906. layer {
  1907. name: "layer_19_2_4"
  1908. type: "Convolution"
  1909. bottom: "layer_19_2_4/depthwise"
  1910. top: "layer_19_2_4"
  1911. param {
  1912. lr_mult: 1.0
  1913. decay_mult: 1.0
  1914. }
  1915. param {
  1916. lr_mult: 2.0
  1917. decay_mult: 0.0
  1918. }
  1919. convolution_param {
  1920. num_output: 256
  1921. kernel_size: 1
  1922. weight_filler {
  1923. type: "msra"
  1924. }
  1925. bias_filler {
  1926. type: "constant"
  1927. value: 0.0
  1928. }
  1929. }
  1930. }
  1931. layer {
  1932. name: "layer_19_2_4/relu"
  1933. type: "ReLU"
  1934. bottom: "layer_19_2_4"
  1935. top: "layer_19_2_4"
  1936. }
  1937. layer {
  1938. name: "layer_19_1_5"
  1939. type: "Convolution"
  1940. bottom: "layer_19_2_4"
  1941. top: "layer_19_1_5"
  1942. param {
  1943. lr_mult: 1.0
  1944. decay_mult: 1.0
  1945. }
  1946. param {
  1947. lr_mult: 2.0
  1948. decay_mult: 0.0
  1949. }
  1950. convolution_param {
  1951. num_output: 64
  1952. kernel_size: 1
  1953. weight_filler {
  1954. type: "msra"
  1955. }
  1956. bias_filler {
  1957. type: "constant"
  1958. value: 0.0
  1959. }
  1960. }
  1961. }
  1962. layer {
  1963. name: "layer_19_1_5/relu"
  1964. type: "ReLU"
  1965. bottom: "layer_19_1_5"
  1966. top: "layer_19_1_5"
  1967. }
  1968. layer {
  1969. name: "layer_19_2_5/depthwise"
  1970. type: "Convolution"
  1971. bottom: "layer_19_1_5"
  1972. top: "layer_19_2_5/depthwise"
  1973. param {
  1974. lr_mult: 1.0
  1975. decay_mult: 1.0
  1976. }
  1977. param {
  1978. lr_mult: 2.0
  1979. decay_mult: 0.0
  1980. }
  1981. convolution_param {
  1982. num_output: 64
  1983. pad: 1
  1984. kernel_size: 3
  1985. stride: 2
  1986. group: 64
  1987. #engine: CAFFE
  1988. weight_filler {
  1989. type: "msra"
  1990. }
  1991. bias_filler {
  1992. type: "constant"
  1993. value: 0.0
  1994. }
  1995. }
  1996. }
  1997. layer {
  1998. name: "layer_19_2_5/depthwise/relu"
  1999. type: "ReLU"
  2000. bottom: "layer_19_2_5/depthwise"
  2001. top: "layer_19_2_5/depthwise"
  2002. }
  2003. layer {
  2004. name: "layer_19_2_5"
  2005. type: "Convolution"
  2006. bottom: "layer_19_2_5/depthwise"
  2007. top: "layer_19_2_5"
  2008. param {
  2009. lr_mult: 1.0
  2010. decay_mult: 1.0
  2011. }
  2012. param {
  2013. lr_mult: 2.0
  2014. decay_mult: 0.0
  2015. }
  2016. convolution_param {
  2017. num_output: 128
  2018. kernel_size: 1
  2019. weight_filler {
  2020. type: "msra"
  2021. }
  2022. bias_filler {
  2023. type: "constant"
  2024. value: 0.0
  2025. }
  2026. }
  2027. }
  2028. layer {
  2029. name: "layer_19_2_5/relu"
  2030. type: "ReLU"
  2031. bottom: "layer_19_2_5"
  2032. top: "layer_19_2_5"
  2033. }
  2034. layer {
  2035. name: "conv_13/expand_mbox_loc/depthwise"
  2036. type: "Convolution"
  2037. bottom: "conv_13/expand"
  2038. top: "conv_13/expand_mbox_loc/depthwise"
  2039. param {
  2040. lr_mult: 1.0
  2041. decay_mult: 1.0
  2042. }
  2043. convolution_param {
  2044. num_output: 576
  2045. bias_term: false
  2046. pad: 1
  2047. kernel_size: 3
  2048. group: 576
  2049. #engine: CAFFE
  2050. weight_filler {
  2051. type: "msra"
  2052. }
  2053. }
  2054. }
  2055. layer {
  2056. name: "conv_13/expand_mbox_loc/depthwise/bn"
  2057. type: "BatchNorm"
  2058. bottom: "conv_13/expand_mbox_loc/depthwise"
  2059. top: "conv_13/expand_mbox_loc/depthwise"
  2060. batch_norm_param {
  2061. eps: 0.001
  2062. }
  2063. param {
  2064. lr_mult: 0
  2065. decay_mult: 0
  2066. }
  2067. param {
  2068. lr_mult: 0
  2069. decay_mult: 0
  2070. }
  2071. param {
  2072. lr_mult: 0
  2073. decay_mult: 0
  2074. }
  2075. }
  2076. layer {
  2077. name: "conv_13/expand_mbox_loc/depthwise/scale"
  2078. type: "Scale"
  2079. bottom: "conv_13/expand_mbox_loc/depthwise"
  2080. top: "conv_13/expand_mbox_loc/depthwise"
  2081. param {
  2082. lr_mult: 1.0
  2083. decay_mult: 0.0
  2084. }
  2085. param {
  2086. lr_mult: 2.0
  2087. decay_mult: 0.0
  2088. }
  2089. scale_param {
  2090. filler {
  2091. value: 1
  2092. }
  2093. bias_term: true
  2094. bias_filler {
  2095. value: 0
  2096. }
  2097. }
  2098. }
  2099. layer {
  2100. name: "conv_13/expand_mbox_loc/depthwise/relu"
  2101. type: "ReLU"
  2102. bottom: "conv_13/expand_mbox_loc/depthwise"
  2103. top: "conv_13/expand_mbox_loc/depthwise"
  2104. }
  2105. layer {
  2106. name: "conv_13/expand_mbox_loc"
  2107. type: "Convolution"
  2108. bottom: "conv_13/expand_mbox_loc/depthwise"
  2109. top: "conv_13/expand_mbox_loc"
  2110. param {
  2111. lr_mult: 1.0
  2112. decay_mult: 1.0
  2113. }
  2114. param {
  2115. lr_mult: 2.0
  2116. decay_mult: 0.0
  2117. }
  2118. convolution_param {
  2119. num_output: 12
  2120. kernel_size: 1
  2121. weight_filler {
  2122. type: "msra"
  2123. }
  2124. bias_filler {
  2125. type: "constant"
  2126. value: 0.0
  2127. }
  2128. }
  2129. }
  2130. layer {
  2131. name: "conv_13/expand_mbox_loc_flat"
  2132. type: "Flatten"
  2133. bottom: "conv_13/expand_mbox_loc_perm"
  2134. top: "conv_13/expand_mbox_loc_flat"
  2135. flatten_param {
  2136. axis: 1
  2137. }
  2138. }
  2139. layer {
  2140. name: "conv_13/expand_mbox_conf/depthwise"
  2141. type: "Convolution"
  2142. bottom: "conv_13/expand"
  2143. top: "conv_13/expand_mbox_conf/depthwise"
  2144. param {
  2145. lr_mult: 1.0
  2146. decay_mult: 1.0
  2147. }
  2148. convolution_param {
  2149. num_output: 576
  2150. bias_term: false
  2151. pad: 1
  2152. kernel_size: 3
  2153. group: 576
  2154. #engine: CAFFE
  2155. weight_filler {
  2156. type: "msra"
  2157. }
  2158. }
  2159. }
  2160. layer {
  2161. name: "conv_13/expand_mbox_conf/depthwise/bn"
  2162. type: "BatchNorm"
  2163. bottom: "conv_13/expand_mbox_conf/depthwise"
  2164. top: "conv_13/expand_mbox_conf/depthwise"
  2165. batch_norm_param {
  2166. eps: 0.001
  2167. }
  2168. param {
  2169. lr_mult: 0
  2170. decay_mult: 0
  2171. }
  2172. param {
  2173. lr_mult: 0
  2174. decay_mult: 0
  2175. }
  2176. param {
  2177. lr_mult: 0
  2178. decay_mult: 0
  2179. }
  2180. }
  2181. layer {
  2182. name: "conv_13/expand_mbox_conf/depthwise/scale"
  2183. type: "Scale"
  2184. bottom: "conv_13/expand_mbox_conf/depthwise"
  2185. top: "conv_13/expand_mbox_conf/depthwise"
  2186. param {
  2187. lr_mult: 1.0
  2188. decay_mult: 0.0
  2189. }
  2190. param {
  2191. lr_mult: 2.0
  2192. decay_mult: 0.0
  2193. }
  2194. scale_param {
  2195. filler {
  2196. value: 1
  2197. }
  2198. bias_term: true
  2199. bias_filler {
  2200. value: 0
  2201. }
  2202. }
  2203. }
  2204. layer {
  2205. name: "conv_13/expand_mbox_conf/depthwise/relu"
  2206. type: "ReLU"
  2207. bottom: "conv_13/expand_mbox_conf/depthwise"
  2208. top: "conv_13/expand_mbox_conf/depthwise"
  2209. }
  2210. layer {
  2211. name: "conv_13/expand_mbox_conf"
  2212. type: "Convolution"
  2213. bottom: "conv_13/expand_mbox_conf/depthwise"
  2214. top: "conv_13/expand_mbox_conf"
  2215. param {
  2216. lr_mult: 1.0
  2217. decay_mult: 1.0
  2218. }
  2219. param {
  2220. lr_mult: 2.0
  2221. decay_mult: 0.0
  2222. }
  2223. convolution_param {
  2224. num_output: 273
  2225. kernel_size: 1
  2226. weight_filler {
  2227. type: "msra"
  2228. }
  2229. bias_filler {
  2230. type: "constant"
  2231. value: 0.0
  2232. }
  2233. }
  2234. }
  2235. layer {
  2236. name: "conv_13/expand_mbox_conf_flat"
  2237. type: "Flatten"
  2238. bottom: "conv_13/expand_mbox_conf_perm"
  2239. top: "conv_13/expand_mbox_conf_flat"
  2240. flatten_param {
  2241. axis: 1
  2242. }
  2243. }
  2244. layer {
  2245. name: "conv_13/expand_mbox_priorbox"
  2246. type: "PriorBox"
  2247. bottom: "conv_13/expand"
  2248. bottom: "data"
  2249. top: "conv_13/expand_mbox_priorbox"
  2250. prior_box_param {
  2251. min_size: 60.0
  2252. aspect_ratio: 2.0
  2253. flip: true
  2254. clip: false
  2255. variance: 0.1
  2256. variance: 0.1
  2257. variance: 0.2
  2258. variance: 0.2
  2259. offset: 0.5
  2260. }
  2261. }
  2262. layer {
  2263. name: "Conv_1_mbox_loc/depthwise"
  2264. type: "Convolution"
  2265. bottom: "Conv_1"
  2266. top: "Conv_1_mbox_loc/depthwise"
  2267. param {
  2268. lr_mult: 1.0
  2269. decay_mult: 1.0
  2270. }
  2271. convolution_param {
  2272. num_output: 1280
  2273. bias_term: false
  2274. pad: 1
  2275. kernel_size: 3
  2276. group: 1280
  2277. #engine: CAFFE
  2278. weight_filler {
  2279. type: "msra"
  2280. }
  2281. }
  2282. }
  2283. layer {
  2284. name: "Conv_1_mbox_loc/depthwise/bn"
  2285. type: "BatchNorm"
  2286. bottom: "Conv_1_mbox_loc/depthwise"
  2287. top: "Conv_1_mbox_loc/depthwise"
  2288. batch_norm_param {
  2289. eps: 0.001
  2290. }
  2291. param {
  2292. lr_mult: 0
  2293. decay_mult: 0
  2294. }
  2295. param {
  2296. lr_mult: 0
  2297. decay_mult: 0
  2298. }
  2299. param {
  2300. lr_mult: 0
  2301. decay_mult: 0
  2302. }
  2303. }
  2304. layer {
  2305. name: "Conv_1_mbox_loc/depthwise/scale"
  2306. type: "Scale"
  2307. bottom: "Conv_1_mbox_loc/depthwise"
  2308. top: "Conv_1_mbox_loc/depthwise"
  2309. param {
  2310. lr_mult: 1.0
  2311. decay_mult: 0.0
  2312. }
  2313. param {
  2314. lr_mult: 2.0
  2315. decay_mult: 0.0
  2316. }
  2317. scale_param {
  2318. filler {
  2319. value: 1
  2320. }
  2321. bias_term: true
  2322. bias_filler {
  2323. value: 0
  2324. }
  2325. }
  2326. }
  2327. layer {
  2328. name: "Conv_1_mbox_loc/depthwise/relu"
  2329. type: "ReLU"
  2330. bottom: "Conv_1_mbox_loc/depthwise"
  2331. top: "Conv_1_mbox_loc/depthwise"
  2332. }
  2333. layer {
  2334. name: "Conv_1_mbox_loc"
  2335. type: "Convolution"
  2336. bottom: "Conv_1_mbox_loc/depthwise"
  2337. top: "Conv_1_mbox_loc"
  2338. param {
  2339. lr_mult: 1.0
  2340. decay_mult: 1.0
  2341. }
  2342. param {
  2343. lr_mult: 2.0
  2344. decay_mult: 0.0
  2345. }
  2346. convolution_param {
  2347. num_output: 24
  2348. kernel_size: 1
  2349. weight_filler {
  2350. type: "msra"
  2351. }
  2352. bias_filler {
  2353. type: "constant"
  2354. value: 0.0
  2355. }
  2356. }
  2357. }
  2358. layer {
  2359. name: "Conv_1_mbox_loc_flat"
  2360. type: "Flatten"
  2361. bottom: "Conv_1_mbox_loc_perm"
  2362. top: "Conv_1_mbox_loc_flat"
  2363. flatten_param {
  2364. axis: 1
  2365. }
  2366. }
  2367. layer {
  2368. name: "Conv_1_mbox_conf/depthwise"
  2369. type: "Convolution"
  2370. bottom: "Conv_1"
  2371. top: "Conv_1_mbox_conf/depthwise"
  2372. param {
  2373. lr_mult: 1.0
  2374. decay_mult: 1.0
  2375. }
  2376. convolution_param {
  2377. num_output: 1280
  2378. bias_term: false
  2379. pad: 1
  2380. kernel_size: 3
  2381. group: 1280
  2382. #engine: CAFFE
  2383. weight_filler {
  2384. type: "msra"
  2385. }
  2386. }
  2387. }
  2388. layer {
  2389. name: "Conv_1_mbox_conf/depthwise/bn"
  2390. type: "BatchNorm"
  2391. bottom: "Conv_1_mbox_conf/depthwise"
  2392. top: "Conv_1_mbox_conf/depthwise"
  2393. batch_norm_param {
  2394. eps: 0.001
  2395. }
  2396. param {
  2397. lr_mult: 0
  2398. decay_mult: 0
  2399. }
  2400. param {
  2401. lr_mult: 0
  2402. decay_mult: 0
  2403. }
  2404. param {
  2405. lr_mult: 0
  2406. decay_mult: 0
  2407. }
  2408. }
  2409. layer {
  2410. name: "Conv_1_mbox_conf/depthwise/scale"
  2411. type: "Scale"
  2412. bottom: "Conv_1_mbox_conf/depthwise"
  2413. top: "Conv_1_mbox_conf/depthwise"
  2414. param {
  2415. lr_mult: 1.0
  2416. decay_mult: 0.0
  2417. }
  2418. param {
  2419. lr_mult: 2.0
  2420. decay_mult: 0.0
  2421. }
  2422. scale_param {
  2423. filler {
  2424. value: 1
  2425. }
  2426. bias_term: true
  2427. bias_filler {
  2428. value: 0
  2429. }
  2430. }
  2431. }
  2432. layer {
  2433. name: "Conv_1_mbox_conf/depthwise/relu"
  2434. type: "ReLU"
  2435. bottom: "Conv_1_mbox_conf/depthwise"
  2436. top: "Conv_1_mbox_conf/depthwise"
  2437. }
  2438. layer {
  2439. name: "Conv_1_mbox_conf"
  2440. type: "Convolution"
  2441. bottom: "Conv_1_mbox_conf/depthwise"
  2442. top: "Conv_1_mbox_conf"
  2443. param {
  2444. lr_mult: 1.0
  2445. decay_mult: 1.0
  2446. }
  2447. param {
  2448. lr_mult: 2.0
  2449. decay_mult: 0.0
  2450. }
  2451. convolution_param {
  2452. num_output: 546
  2453. kernel_size: 1
  2454. weight_filler {
  2455. type: "msra"
  2456. }
  2457. bias_filler {
  2458. type: "constant"
  2459. value: 0.0
  2460. }
  2461. }
  2462. }
  2463. layer {
  2464. name: "Conv_1_mbox_conf_flat"
  2465. type: "Flatten"
  2466. bottom: "Conv_1_mbox_conf_perm"
  2467. top: "Conv_1_mbox_conf_flat"
  2468. flatten_param {
  2469. axis: 1
  2470. }
  2471. }
  2472. layer {
  2473. name: "Conv_1_mbox_priorbox"
  2474. type: "PriorBox"
  2475. bottom: "Conv_1"
  2476. bottom: "data"
  2477. top: "Conv_1_mbox_priorbox"
  2478. prior_box_param {
  2479. min_size: 105.0
  2480. max_size: 150.0
  2481. aspect_ratio: 2.0
  2482. aspect_ratio: 3.0
  2483. flip: true
  2484. clip: false
  2485. variance: 0.1
  2486. variance: 0.1
  2487. variance: 0.2
  2488. variance: 0.2
  2489. offset: 0.5
  2490. }
  2491. }
  2492. layer {
  2493. name: "layer_19_2_2_mbox_loc/depthwise"
  2494. type: "Convolution"
  2495. bottom: "layer_19_2_2"
  2496. top: "layer_19_2_2_mbox_loc/depthwise"
  2497. param {
  2498. lr_mult: 1.0
  2499. decay_mult: 1.0
  2500. }
  2501. convolution_param {
  2502. num_output: 512
  2503. bias_term: false
  2504. pad: 1
  2505. kernel_size: 3
  2506. group: 512
  2507. #engine: CAFFE
  2508. weight_filler {
  2509. type: "msra"
  2510. }
  2511. }
  2512. }
  2513. layer {
  2514. name: "layer_19_2_2_mbox_loc/depthwise/bn"
  2515. type: "BatchNorm"
  2516. bottom: "layer_19_2_2_mbox_loc/depthwise"
  2517. top: "layer_19_2_2_mbox_loc/depthwise"
  2518. batch_norm_param {
  2519. eps: 0.001
  2520. }
  2521. param {
  2522. lr_mult: 0
  2523. decay_mult: 0
  2524. }
  2525. param {
  2526. lr_mult: 0
  2527. decay_mult: 0
  2528. }
  2529. param {
  2530. lr_mult: 0
  2531. decay_mult: 0
  2532. }
  2533. }
  2534. layer {
  2535. name: "layer_19_2_2_mbox_loc/depthwise/scale"
  2536. type: "Scale"
  2537. bottom: "layer_19_2_2_mbox_loc/depthwise"
  2538. top: "layer_19_2_2_mbox_loc/depthwise"
  2539. param {
  2540. lr_mult: 1.0
  2541. decay_mult: 0.0
  2542. }
  2543. param {
  2544. lr_mult: 2.0
  2545. decay_mult: 0.0
  2546. }
  2547. scale_param {
  2548. filler {
  2549. value: 1
  2550. }
  2551. bias_term: true
  2552. bias_filler {
  2553. value: 0
  2554. }
  2555. }
  2556. }
  2557. layer {
  2558. name: "layer_19_2_2_mbox_loc/depthwise/relu"
  2559. type: "ReLU"
  2560. bottom: "layer_19_2_2_mbox_loc/depthwise"
  2561. top: "layer_19_2_2_mbox_loc/depthwise"
  2562. }
  2563. layer {
  2564. name: "layer_19_2_2_mbox_loc"
  2565. type: "Convolution"
  2566. bottom: "layer_19_2_2_mbox_loc/depthwise"
  2567. top: "layer_19_2_2_mbox_loc"
  2568. param {
  2569. lr_mult: 1.0
  2570. decay_mult: 1.0
  2571. }
  2572. param {
  2573. lr_mult: 2.0
  2574. decay_mult: 0.0
  2575. }
  2576. convolution_param {
  2577. num_output: 24
  2578. kernel_size: 1
  2579. weight_filler {
  2580. type: "msra"
  2581. }
  2582. bias_filler {
  2583. type: "constant"
  2584. value: 0.0
  2585. }
  2586. }
  2587. }
  2588.  
  2589. layer {
  2590. name: "layer_19_2_2_mbox_loc_flat"
  2591. type: "Flatten"
  2592. bottom: "layer_19_2_2_mbox_loc_perm"
  2593. top: "layer_19_2_2_mbox_loc_flat"
  2594. flatten_param {
  2595. axis: 1
  2596. }
  2597. }
  2598. layer {
  2599. name: "layer_19_2_2_mbox_conf/depthwise"
  2600. type: "Convolution"
  2601. bottom: "layer_19_2_2"
  2602. top: "layer_19_2_2_mbox_conf/depthwise"
  2603. param {
  2604. lr_mult: 1.0
  2605. decay_mult: 1.0
  2606. }
  2607. convolution_param {
  2608. num_output: 512
  2609. bias_term: false
  2610. pad: 1
  2611. kernel_size: 3
  2612. group: 512
  2613. #engine: CAFFE
  2614. weight_filler {
  2615. type: "msra"
  2616. }
  2617. }
  2618. }
  2619. layer {
  2620. name: "layer_19_2_2_mbox_conf/depthwise/bn"
  2621. type: "BatchNorm"
  2622. bottom: "layer_19_2_2_mbox_conf/depthwise"
  2623. top: "layer_19_2_2_mbox_conf/depthwise"
  2624. batch_norm_param {
  2625. eps: 0.001
  2626. }
  2627. param {
  2628. lr_mult: 0
  2629. decay_mult: 0
  2630. }
  2631. param {
  2632. lr_mult: 0
  2633. decay_mult: 0
  2634. }
  2635. param {
  2636. lr_mult: 0
  2637. decay_mult: 0
  2638. }
  2639. }
  2640. layer {
  2641. name: "layer_19_2_2_mbox_conf/depthwise/scale"
  2642. type: "Scale"
  2643. bottom: "layer_19_2_2_mbox_conf/depthwise"
  2644. top: "layer_19_2_2_mbox_conf/depthwise"
  2645. param {
  2646. lr_mult: 1.0
  2647. decay_mult: 0.0
  2648. }
  2649. param {
  2650. lr_mult: 2.0
  2651. decay_mult: 0.0
  2652. }
  2653. scale_param {
  2654. filler {
  2655. value: 1
  2656. }
  2657. bias_term: true
  2658. bias_filler {
  2659. value: 0
  2660. }
  2661. }
  2662. }
  2663. layer {
  2664. name: "layer_19_2_2_mbox_conf/depthwise/relu"
  2665. type: "ReLU"
  2666. bottom: "layer_19_2_2_mbox_conf/depthwise"
  2667. top: "layer_19_2_2_mbox_conf/depthwise"
  2668. }
  2669. layer {
  2670. name: "layer_19_2_2_mbox_conf"
  2671. type: "Convolution"
  2672. bottom: "layer_19_2_2_mbox_conf/depthwise"
  2673. top: "layer_19_2_2_mbox_conf"
  2674. param {
  2675. lr_mult: 1.0
  2676. decay_mult: 1.0
  2677. }
  2678. param {
  2679. lr_mult: 2.0
  2680. decay_mult: 0.0
  2681. }
  2682. convolution_param {
  2683. num_output: 546
  2684. kernel_size: 1
  2685. weight_filler {
  2686. type: "msra"
  2687. }
  2688. bias_filler {
  2689. type: "constant"
  2690. value: 0.0
  2691. }
  2692. }
  2693. }
  2694. layer {
  2695. name: "layer_19_2_2_mbox_conf_flat"
  2696. type: "Flatten"
  2697. bottom: "layer_19_2_2_mbox_conf_perm"
  2698. top: "layer_19_2_2_mbox_conf_flat"
  2699. flatten_param {
  2700. axis: 1
  2701. }
  2702. }
  2703. layer {
  2704. name: "layer_19_2_2_mbox_priorbox"
  2705. type: "PriorBox"
  2706. bottom: "layer_19_2_2"
  2707. bottom: "data"
  2708. top: "layer_19_2_2_mbox_priorbox"
  2709. prior_box_param {
  2710. min_size: 150.0
  2711. max_size: 195.0
  2712. aspect_ratio: 2.0
  2713. aspect_ratio: 3.0
  2714. flip: true
  2715. clip: false
  2716. variance: 0.1
  2717. variance: 0.1
  2718. variance: 0.2
  2719. variance: 0.2
  2720. offset: 0.5
  2721. }
  2722. }
  2723. layer {
  2724. name: "layer_19_2_3_mbox_loc/depthwise"
  2725. type: "Convolution"
  2726. bottom: "layer_19_2_3"
  2727. top: "layer_19_2_3_mbox_loc/depthwise"
  2728. param {
  2729. lr_mult: 1.0
  2730. decay_mult: 1.0
  2731. }
  2732. convolution_param {
  2733. num_output: 256
  2734. bias_term: false
  2735. pad: 1
  2736. kernel_size: 3
  2737. group: 256
  2738. #engine: CAFFE
  2739. weight_filler {
  2740. type: "msra"
  2741. }
  2742. }
  2743. }
  2744. layer {
  2745. name: "layer_19_2_3_mbox_loc/depthwise/bn"
  2746. type: "BatchNorm"
  2747. bottom: "layer_19_2_3_mbox_loc/depthwise"
  2748. top: "layer_19_2_3_mbox_loc/depthwise"
  2749. batch_norm_param {
  2750. eps: 0.001
  2751. }
  2752. param {
  2753. lr_mult: 0
  2754. decay_mult: 0
  2755. }
  2756. param {
  2757. lr_mult: 0
  2758. decay_mult: 0
  2759. }
  2760. param {
  2761. lr_mult: 0
  2762. decay_mult: 0
  2763. }
  2764. }
  2765. layer {
  2766. name: "layer_19_2_3_mbox_loc/depthwise/scale"
  2767. type: "Scale"
  2768. bottom: "layer_19_2_3_mbox_loc/depthwise"
  2769. top: "layer_19_2_3_mbox_loc/depthwise"
  2770. param {
  2771. lr_mult: 1.0
  2772. decay_mult: 0.0
  2773. }
  2774. param {
  2775. lr_mult: 2.0
  2776. decay_mult: 0.0
  2777. }
  2778. scale_param {
  2779. filler {
  2780. value: 1
  2781. }
  2782. bias_term: true
  2783. bias_filler {
  2784. value: 0
  2785. }
  2786. }
  2787. }
  2788. layer {
  2789. name: "layer_19_2_3_mbox_loc/depthwise/relu"
  2790. type: "ReLU"
  2791. bottom: "layer_19_2_3_mbox_loc/depthwise"
  2792. top: "layer_19_2_3_mbox_loc/depthwise"
  2793. }
  2794. layer {
  2795. name: "layer_19_2_3_mbox_loc"
  2796. type: "Convolution"
  2797. bottom: "layer_19_2_3_mbox_loc/depthwise"
  2798. top: "layer_19_2_3_mbox_loc"
  2799. param {
  2800. lr_mult: 1.0
  2801. decay_mult: 1.0
  2802. }
  2803. param {
  2804. lr_mult: 2.0
  2805. decay_mult: 0.0
  2806. }
  2807. convolution_param {
  2808. num_output: 24
  2809. kernel_size: 1
  2810. weight_filler {
  2811. type: "msra"
  2812. }
  2813. bias_filler {
  2814. type: "constant"
  2815. value: 0.0
  2816. }
  2817. }
  2818. }
  2819. layer {
  2820. name: "layer_19_2_3_mbox_loc_flat"
  2821. type: "Flatten"
  2822. bottom: "layer_19_2_3_mbox_loc_perm"
  2823. top: "layer_19_2_3_mbox_loc_flat"
  2824. flatten_param {
  2825. axis: 1
  2826. }
  2827. }
  2828. layer {
  2829. name: "layer_19_2_3_mbox_conf/depthwise"
  2830. type: "Convolution"
  2831. bottom: "layer_19_2_3"
  2832. top: "layer_19_2_3_mbox_conf/depthwise"
  2833. param {
  2834. lr_mult: 1.0
  2835. decay_mult: 1.0
  2836. }
  2837. convolution_param {
  2838. num_output: 256
  2839. bias_term: false
  2840. pad: 1
  2841. kernel_size: 3
  2842. group: 256
  2843. #engine: CAFFE
  2844. weight_filler {
  2845. type: "msra"
  2846. }
  2847. }
  2848. }
  2849. layer {
  2850. name: "layer_19_2_3_mbox_conf/depthwise/bn"
  2851. type: "BatchNorm"
  2852. bottom: "layer_19_2_3_mbox_conf/depthwise"
  2853. top: "layer_19_2_3_mbox_conf/depthwise"
  2854. batch_norm_param {
  2855. eps: 0.001
  2856. }
  2857. param {
  2858. lr_mult: 0
  2859. decay_mult: 0
  2860. }
  2861. param {
  2862. lr_mult: 0
  2863. decay_mult: 0
  2864. }
  2865. param {
  2866. lr_mult: 0
  2867. decay_mult: 0
  2868. }
  2869. }
  2870. layer {
  2871. name: "layer_19_2_3_mbox_conf/depthwise/scale"
  2872. type: "Scale"
  2873. bottom: "layer_19_2_3_mbox_conf/depthwise"
  2874. top: "layer_19_2_3_mbox_conf/depthwise"
  2875. param {
  2876. lr_mult: 1.0
  2877. decay_mult: 0.0
  2878. }
  2879. param {
  2880. lr_mult: 2.0
  2881. decay_mult: 0.0
  2882. }
  2883. scale_param {
  2884. filler {
  2885. value: 1
  2886. }
  2887. bias_term: true
  2888. bias_filler {
  2889. value: 0
  2890. }
  2891. }
  2892. }
  2893. layer {
  2894. name: "layer_19_2_3_mbox_conf/depthwise/relu"
  2895. type: "ReLU"
  2896. bottom: "layer_19_2_3_mbox_conf/depthwise"
  2897. top: "layer_19_2_3_mbox_conf/depthwise"
  2898. }
  2899. layer {
  2900. name: "layer_19_2_3_mbox_conf"
  2901. type: "Convolution"
  2902. bottom: "layer_19_2_3_mbox_conf/depthwise"
  2903. top: "layer_19_2_3_mbox_conf"
  2904. param {
  2905. lr_mult: 1.0
  2906. decay_mult: 1.0
  2907. }
  2908. param {
  2909. lr_mult: 2.0
  2910. decay_mult: 0.0
  2911. }
  2912. convolution_param {
  2913. num_output: 546
  2914. kernel_size: 1
  2915. weight_filler {
  2916. type: "msra"
  2917. }
  2918. bias_filler {
  2919. type: "constant"
  2920. value: 0.0
  2921. }
  2922. }
  2923. }
  2924. layer {
  2925. name: "layer_19_2_3_mbox_conf_flat"
  2926. type: "Flatten"
  2927. bottom: "layer_19_2_3_mbox_conf_perm"
  2928. top: "layer_19_2_3_mbox_conf_flat"
  2929. flatten_param {
  2930. axis: 1
  2931. }
  2932. }
  2933. layer {
  2934. name: "layer_19_2_3_mbox_priorbox"
  2935. type: "PriorBox"
  2936. bottom: "layer_19_2_3"
  2937. bottom: "data"
  2938. top: "layer_19_2_3_mbox_priorbox"
  2939. prior_box_param {
  2940. min_size: 195.0
  2941. max_size: 240.0
  2942. aspect_ratio: 2.0
  2943. aspect_ratio: 3.0
  2944. flip: true
  2945. clip: false
  2946. variance: 0.1
  2947. variance: 0.1
  2948. variance: 0.2
  2949. variance: 0.2
  2950. offset: 0.5
  2951. }
  2952. }
  2953. layer {
  2954. name: "layer_19_2_4_mbox_loc/depthwise"
  2955. type: "Convolution"
  2956. bottom: "layer_19_2_4"
  2957. top: "layer_19_2_4_mbox_loc/depthwise"
  2958. param {
  2959. lr_mult: 1.0
  2960. decay_mult: 1.0
  2961. }
  2962. convolution_param {
  2963. num_output: 256
  2964. bias_term: false
  2965. pad: 1
  2966. kernel_size: 3
  2967. group: 256
  2968. #engine: CAFFE
  2969. weight_filler {
  2970. type: "msra"
  2971. }
  2972. }
  2973. }
  2974. layer {
  2975. name: "layer_19_2_4_mbox_loc/depthwise/bn"
  2976. type: "BatchNorm"
  2977. bottom: "layer_19_2_4_mbox_loc/depthwise"
  2978. top: "layer_19_2_4_mbox_loc/depthwise"
  2979. batch_norm_param {
  2980. eps: 0.001
  2981. }
  2982. param {
  2983. lr_mult: 0
  2984. decay_mult: 0
  2985. }
  2986. param {
  2987. lr_mult: 0
  2988. decay_mult: 0
  2989. }
  2990. param {
  2991. lr_mult: 0
  2992. decay_mult: 0
  2993. }
  2994. }
  2995. layer {
  2996. name: "layer_19_2_4_mbox_loc/depthwise/scale"
  2997. type: "Scale"
  2998. bottom: "layer_19_2_4_mbox_loc/depthwise"
  2999. top: "layer_19_2_4_mbox_loc/depthwise"
  3000. param {
  3001. lr_mult: 1.0
  3002. decay_mult: 0.0
  3003. }
  3004. param {
  3005. lr_mult: 2.0
  3006. decay_mult: 0.0
  3007. }
  3008. scale_param {
  3009. filler {
  3010. value: 1
  3011. }
  3012. bias_term: true
  3013. bias_filler {
  3014. value: 0
  3015. }
  3016. }
  3017. }
  3018. layer {
  3019. name: "layer_19_2_4_mbox_loc/depthwise/relu"
  3020. type: "ReLU"
  3021. bottom: "layer_19_2_4_mbox_loc/depthwise"
  3022. top: "layer_19_2_4_mbox_loc/depthwise"
  3023. }
  3024. layer {
  3025. name: "layer_19_2_4_mbox_loc"
  3026. type: "Convolution"
  3027. bottom: "layer_19_2_4_mbox_loc/depthwise"
  3028. top: "layer_19_2_4_mbox_loc"
  3029. param {
  3030. lr_mult: 1.0
  3031. decay_mult: 1.0
  3032. }
  3033. param {
  3034. lr_mult: 2.0
  3035. decay_mult: 0.0
  3036. }
  3037. convolution_param {
  3038. num_output: 24
  3039. kernel_size: 1
  3040. weight_filler {
  3041. type: "msra"
  3042. }
  3043. bias_filler {
  3044. type: "constant"
  3045. value: 0.0
  3046. }
  3047. }
  3048. }
  3049. layer {
  3050. name: "layer_19_2_4_mbox_loc_flat"
  3051. type: "Flatten"
  3052. bottom: "layer_19_2_4_mbox_loc_perm"
  3053. top: "layer_19_2_4_mbox_loc_flat"
  3054. flatten_param {
  3055. axis: 1
  3056. }
  3057. }
  3058. layer {
  3059. name: "layer_19_2_4_mbox_conf/depthwise"
  3060. type: "Convolution"
  3061. bottom: "layer_19_2_4"
  3062. top: "layer_19_2_4_mbox_conf/depthwise"
  3063. param {
  3064. lr_mult: 1.0
  3065. decay_mult: 1.0
  3066. }
  3067. convolution_param {
  3068. num_output: 256
  3069. bias_term: false
  3070. pad: 1
  3071. kernel_size: 3
  3072. group: 256
  3073. #engine: CAFFE
  3074. weight_filler {
  3075. type: "msra"
  3076. }
  3077. }
  3078. }
  3079. layer {
  3080. name: "layer_19_2_4_mbox_conf/depthwise/bn"
  3081. type: "BatchNorm"
  3082. bottom: "layer_19_2_4_mbox_conf/depthwise"
  3083. top: "layer_19_2_4_mbox_conf/depthwise"
  3084. batch_norm_param {
  3085. eps: 0.001
  3086. }
  3087. param {
  3088. lr_mult: 0
  3089. decay_mult: 0
  3090. }
  3091. param {
  3092. lr_mult: 0
  3093. decay_mult: 0
  3094. }
  3095. param {
  3096. lr_mult: 0
  3097. decay_mult: 0
  3098. }
  3099. }
  3100. layer {
  3101. name: "layer_19_2_4_mbox_conf/depthwise/scale"
  3102. type: "Scale"
  3103. bottom: "layer_19_2_4_mbox_conf/depthwise"
  3104. top: "layer_19_2_4_mbox_conf/depthwise"
  3105. param {
  3106. lr_mult: 1.0
  3107. decay_mult: 0.0
  3108. }
  3109. param {
  3110. lr_mult: 2.0
  3111. decay_mult: 0.0
  3112. }
  3113. scale_param {
  3114. filler {
  3115. value: 1
  3116. }
  3117. bias_term: true
  3118. bias_filler {
  3119. value: 0
  3120. }
  3121. }
  3122. }
  3123. layer {
  3124. name: "layer_19_2_4_mbox_conf/depthwise/relu"
  3125. type: "ReLU"
  3126. bottom: "layer_19_2_4_mbox_conf/depthwise"
  3127. top: "layer_19_2_4_mbox_conf/depthwise"
  3128. }
  3129. layer {
  3130. name: "layer_19_2_4_mbox_conf"
  3131. type: "Convolution"
  3132. bottom: "layer_19_2_4_mbox_conf/depthwise"
  3133. top: "layer_19_2_4_mbox_conf"
  3134. param {
  3135. lr_mult: 1.0
  3136. decay_mult: 1.0
  3137. }
  3138. param {
  3139. lr_mult: 2.0
  3140. decay_mult: 0.0
  3141. }
  3142. convolution_param {
  3143. num_output: 546
  3144. kernel_size: 1
  3145. weight_filler {
  3146. type: "msra"
  3147. }
  3148. bias_filler {
  3149. type: "constant"
  3150. value: 0.0
  3151. }
  3152. }
  3153. }
  3154. layer {
  3155. name: "layer_19_2_4_mbox_conf_flat"
  3156. type: "Flatten"
  3157. bottom: "layer_19_2_4_mbox_conf_perm"
  3158. top: "layer_19_2_4_mbox_conf_flat"
  3159. flatten_param {
  3160. axis: 1
  3161. }
  3162. }
  3163. layer {
  3164. name: "layer_19_2_4_mbox_priorbox"
  3165. type: "PriorBox"
  3166. bottom: "layer_19_2_4"
  3167. bottom: "data"
  3168. top: "layer_19_2_4_mbox_priorbox"
  3169. prior_box_param {
  3170. min_size: 240.0
  3171. max_size: 285.0
  3172. aspect_ratio: 2.0
  3173. aspect_ratio: 3.0
  3174. flip: true
  3175. clip: false
  3176. variance: 0.1
  3177. variance: 0.1
  3178. variance: 0.2
  3179. variance: 0.2
  3180. offset: 0.5
  3181. }
  3182. }
  3183. layer {
  3184. name: "layer_19_2_5_mbox_loc/depthwise"
  3185. type: "Convolution"
  3186. bottom: "layer_19_2_5"
  3187. top: "layer_19_2_5_mbox_loc/depthwise"
  3188. param {
  3189. lr_mult: 1.0
  3190. decay_mult: 1.0
  3191. }
  3192. convolution_param {
  3193. num_output: 128
  3194. bias_term: false
  3195. pad: 1
  3196. kernel_size: 3
  3197. group: 128
  3198. #engine: CAFFE
  3199. weight_filler {
  3200. type: "msra"
  3201. }
  3202. }
  3203. }
  3204. layer {
  3205. name: "layer_19_2_5_mbox_loc/depthwise/bn"
  3206. type: "BatchNorm"
  3207. bottom: "layer_19_2_5_mbox_loc/depthwise"
  3208. top: "layer_19_2_5_mbox_loc/depthwise"
  3209. batch_norm_param {
  3210. eps: 0.001
  3211. }
  3212. param {
  3213. lr_mult: 0
  3214. decay_mult: 0
  3215. }
  3216. param {
  3217. lr_mult: 0
  3218. decay_mult: 0
  3219. }
  3220. param {
  3221. lr_mult: 0
  3222. decay_mult: 0
  3223. }
  3224. }
  3225. layer {
  3226. name: "layer_19_2_5_mbox_loc/depthwise/scale"
  3227. type: "Scale"
  3228. bottom: "layer_19_2_5_mbox_loc/depthwise"
  3229. top: "layer_19_2_5_mbox_loc/depthwise"
  3230. param {
  3231. lr_mult: 1.0
  3232. decay_mult: 0.0
  3233. }
  3234. param {
  3235. lr_mult: 2.0
  3236. decay_mult: 0.0
  3237. }
  3238. scale_param {
  3239. filler {
  3240. value: 1
  3241. }
  3242. bias_term: true
  3243. bias_filler {
  3244. value: 0
  3245. }
  3246. }
  3247. }
  3248. layer {
  3249. name: "layer_19_2_5_mbox_loc/depthwise/relu"
  3250. type: "ReLU"
  3251. bottom: "layer_19_2_5_mbox_loc/depthwise"
  3252. top: "layer_19_2_5_mbox_loc/depthwise"
  3253. }
  3254. layer {
  3255. name: "layer_19_2_5_mbox_loc"
  3256. type: "Convolution"
  3257. bottom: "layer_19_2_5_mbox_loc/depthwise"
  3258. top: "layer_19_2_5_mbox_loc"
  3259. param {
  3260. lr_mult: 1.0
  3261. decay_mult: 1.0
  3262. }
  3263. param {
  3264. lr_mult: 2.0
  3265. decay_mult: 0.0
  3266. }
  3267. convolution_param {
  3268. num_output: 24
  3269. kernel_size: 1
  3270. weight_filler {
  3271. type: "msra"
  3272. }
  3273. bias_filler {
  3274. type: "constant"
  3275. value: 0.0
  3276. }
  3277. }
  3278. }
  3279. layer {
  3280. name: "layer_19_2_5_mbox_loc_flat"
  3281. type: "Flatten"
  3282. bottom: "layer_19_2_5_mbox_loc_perm"
  3283. top: "layer_19_2_5_mbox_loc_flat"
  3284. flatten_param {
  3285. axis: 1
  3286. }
  3287. }
  3288. layer {
  3289. name: "layer_19_2_5_mbox_conf/depthwise"
  3290. type: "Convolution"
  3291. bottom: "layer_19_2_5"
  3292. top: "layer_19_2_5_mbox_conf/depthwise"
  3293. param {
  3294. lr_mult: 1.0
  3295. decay_mult: 1.0
  3296. }
  3297. convolution_param {
  3298. num_output: 128
  3299. bias_term: false
  3300. pad: 1
  3301. kernel_size: 3
  3302. group: 128
  3303. #engine: CAFFE
  3304. weight_filler {
  3305. type: "msra"
  3306. }
  3307. }
  3308. }
  3309. layer {
  3310. name: "layer_19_2_5_mbox_conf/depthwise/bn"
  3311. type: "BatchNorm"
  3312. bottom: "layer_19_2_5_mbox_conf/depthwise"
  3313. top: "layer_19_2_5_mbox_conf/depthwise"
  3314. batch_norm_param {
  3315. eps: 0.001
  3316. }
  3317. param {
  3318. lr_mult: 0
  3319. decay_mult: 0
  3320. }
  3321. param {
  3322. lr_mult: 0
  3323. decay_mult: 0
  3324. }
  3325. param {
  3326. lr_mult: 0
  3327. decay_mult: 0
  3328. }
  3329. }
  3330. layer {
  3331. name: "layer_19_2_5_mbox_conf/depthwise/scale"
  3332. type: "Scale"
  3333. bottom: "layer_19_2_5_mbox_conf/depthwise"
  3334. top: "layer_19_2_5_mbox_conf/depthwise"
  3335. param {
  3336. lr_mult: 1.0
  3337. decay_mult: 0.0
  3338. }
  3339. param {
  3340. lr_mult: 2.0
  3341. decay_mult: 0.0
  3342. }
  3343. scale_param {
  3344. filler {
  3345. value: 1
  3346. }
  3347. bias_term: true
  3348. bias_filler {
  3349. value: 0
  3350. }
  3351. }
  3352. }
  3353. layer {
  3354. name: "layer_19_2_5_mbox_conf/depthwise/relu"
  3355. type: "ReLU"
  3356. bottom: "layer_19_2_5_mbox_conf/depthwise"
  3357. top: "layer_19_2_5_mbox_conf/depthwise"
  3358. }
  3359. layer {
  3360. name: "layer_19_2_5_mbox_conf"
  3361. type: "Convolution"
  3362. bottom: "layer_19_2_5_mbox_conf/depthwise"
  3363. top: "layer_19_2_5_mbox_conf"
  3364. param {
  3365. lr_mult: 1.0
  3366. decay_mult: 1.0
  3367. }
  3368. param {
  3369. lr_mult: 2.0
  3370. decay_mult: 0.0
  3371. }
  3372. convolution_param {
  3373. num_output: 546
  3374. kernel_size: 1
  3375. weight_filler {
  3376. type: "msra"
  3377. }
  3378. bias_filler {
  3379. type: "constant"
  3380. value: 0.0
  3381. }
  3382. }
  3383. }
  3384. layer {
  3385. name: "layer_19_2_5_mbox_conf_flat"
  3386. type: "Flatten"
  3387. bottom: "layer_19_2_5_mbox_conf_perm"
  3388. top: "layer_19_2_5_mbox_conf_flat"
  3389. flatten_param {
  3390. axis: 1
  3391. }
  3392. }
  3393. layer {
  3394. name: "layer_19_2_5_mbox_priorbox"
  3395. type: "PriorBox"
  3396. bottom: "layer_19_2_5"
  3397. bottom: "data"
  3398. top: "layer_19_2_5_mbox_priorbox"
  3399. prior_box_param {
  3400. min_size: 285.0
  3401. max_size: 300.0
  3402. aspect_ratio: 2.0
  3403. aspect_ratio: 3.0
  3404. flip: true
  3405. clip: false
  3406. variance: 0.1
  3407. variance: 0.1
  3408. variance: 0.2
  3409. variance: 0.2
  3410. offset: 0.5
  3411. }
  3412. }
  3413. layer {
  3414. name: "mbox_loc"
  3415. type: "Concat"
  3416. bottom: "conv_13/expand_mbox_loc_flat"
  3417. bottom: "Conv_1_mbox_loc_flat"
  3418. bottom: "layer_19_2_2_mbox_loc_flat"
  3419. bottom: "layer_19_2_3_mbox_loc_flat"
  3420. bottom: "layer_19_2_4_mbox_loc_flat"
  3421. bottom: "layer_19_2_5_mbox_loc_flat"
  3422. top: "mbox_loc"
  3423. concat_param {
  3424. axis: 1
  3425. }
  3426. }
  3427. layer {
  3428. name: "mbox_conf"
  3429. type: "Concat"
  3430. bottom: "conv_13/expand_mbox_conf_flat"
  3431. bottom: "Conv_1_mbox_conf_flat"
  3432. bottom: "layer_19_2_2_mbox_conf_flat"
  3433. bottom: "layer_19_2_3_mbox_conf_flat"
  3434. bottom: "layer_19_2_4_mbox_conf_flat"
  3435. bottom: "layer_19_2_5_mbox_conf_flat"
  3436. top: "mbox_conf"
  3437. concat_param {
  3438. axis: 1
  3439. }
  3440. }
  3441. layer {
  3442. name: "mbox_priorbox"
  3443. type: "Concat"
  3444. bottom: "conv_13/expand_mbox_priorbox"
  3445. bottom: "Conv_1_mbox_priorbox"
  3446. bottom: "layer_19_2_2_mbox_priorbox"
  3447. bottom: "layer_19_2_3_mbox_priorbox"
  3448. bottom: "layer_19_2_4_mbox_priorbox"
  3449. bottom: "layer_19_2_5_mbox_priorbox"
  3450. top: "mbox_priorbox"
  3451. concat_param {
  3452. axis: 2
  3453. }
  3454. }
  3455. layer {
  3456. name: "mbox_conf_reshape"
  3457. type: "Reshape"
  3458. bottom: "mbox_conf"
  3459. top: "mbox_conf_reshape"
  3460. reshape_param {
  3461. shape {
  3462. dim: 0
  3463. dim: -1
  3464. dim: 91
  3465. }
  3466. }
  3467. }
  3468. layer {
  3469. name: "mbox_conf_sigmoid"
  3470. type: "Sigmoid"
  3471. bottom: "mbox_conf_reshape"
  3472. top: "mbox_conf_sigmoid"
  3473. }
  3474. layer {
  3475. name: "mbox_conf_flatten"
  3476. type: "Flatten"
  3477. bottom: "mbox_conf_sigmoid"
  3478. top: "mbox_conf_flatten"
  3479. flatten_param {
  3480. axis: 1
  3481. }
  3482. }
  3483. layer {
  3484. name: "detection_out"
  3485. type: "DetectionOutput"
  3486. bottom: "mbox_loc"
  3487. bottom: "mbox_conf_flatten"
  3488. bottom: "mbox_priorbox"
  3489. top: "detection_out"
  3490. include {
  3491. phase: TEST
  3492. }
  3493. detection_output_param {
  3494. num_classes: 91
  3495. share_location: true
  3496. background_label_id: 0
  3497. nms_param {
  3498. nms_threshold: 0.45
  3499. top_k: 100
  3500. }
  3501. code_type: CENTER_SIZE
  3502. keep_top_k: 100
  3503. confidence_threshold: 0.35
  3504. }
  3505. }
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