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  1. name: "MobileNet-YOLO"
  2. input: "data"
  3. input_shape {
  4. dim: 1
  5. dim: 3
  6. dim: 416
  7. dim: 416
  8. }
  9.  
  10.  
  11. layer {
  12. name: "conv0"
  13. type: "Convolution"
  14. bottom: "data"
  15. top: "conv0"
  16. param {
  17. lr_mult: 0.1
  18. decay_mult: 0.1
  19. }
  20. convolution_param {
  21. num_output: 32
  22. bias_term: false
  23. pad: 1
  24. kernel_size: 3
  25. stride: 2
  26. weight_filler {
  27. type: "msra"
  28. }
  29. }
  30. }
  31. layer {
  32. name: "conv0/bn"
  33. type: "BatchNorm"
  34. bottom: "conv0"
  35. top: "conv0"
  36. param {
  37. lr_mult: 0
  38. decay_mult: 0
  39. }
  40. param {
  41. lr_mult: 0
  42. decay_mult: 0
  43. }
  44. param {
  45. lr_mult: 0
  46. decay_mult: 0
  47. }
  48. }
  49. layer {
  50. name: "conv0/scale"
  51. type: "Scale"
  52. bottom: "conv0"
  53. top: "conv0"
  54. param {
  55. lr_mult: 0.1
  56. decay_mult: 0.0
  57. }
  58. param {
  59. lr_mult: 0.2
  60. decay_mult: 0.0
  61. }
  62. scale_param {
  63. filler {
  64. value: 1
  65. }
  66. bias_term: true
  67. bias_filler {
  68. value: 0
  69. }
  70. }
  71. }
  72. layer {
  73. name: "conv0/relu"
  74. type: "ReLU"
  75. bottom: "conv0"
  76. top: "conv0"
  77. }
  78. layer {
  79. name: "conv1/dw"
  80. type: "DepthwiseConvolution"
  81. bottom: "conv0"
  82. top: "conv1/dw"
  83. param {
  84. lr_mult: 0.1
  85. decay_mult: 0.1
  86. }
  87. convolution_param {
  88. num_output: 32
  89. bias_term: false
  90. pad: 1
  91. kernel_size: 3
  92. group: 32
  93. engine: CAFFE
  94. weight_filler {
  95. type: "msra"
  96. }
  97. }
  98. }
  99. layer {
  100. name: "conv1/dw/bn"
  101. type: "BatchNorm"
  102. bottom: "conv1/dw"
  103. top: "conv1/dw"
  104. param {
  105. lr_mult: 0
  106. decay_mult: 0
  107. }
  108. param {
  109. lr_mult: 0
  110. decay_mult: 0
  111. }
  112. param {
  113. lr_mult: 0
  114. decay_mult: 0
  115. }
  116. }
  117. layer {
  118. name: "conv1/dw/scale"
  119. type: "Scale"
  120. bottom: "conv1/dw"
  121. top: "conv1/dw"
  122. param {
  123. lr_mult: 0.1
  124. decay_mult: 0.0
  125. }
  126. param {
  127. lr_mult: 0.2
  128. decay_mult: 0.0
  129. }
  130. scale_param {
  131. filler {
  132. value: 1
  133. }
  134. bias_term: true
  135. bias_filler {
  136. value: 0
  137. }
  138. }
  139. }
  140. layer {
  141. name: "conv1/dw/relu"
  142. type: "ReLU"
  143. bottom: "conv1/dw"
  144. top: "conv1/dw"
  145. }
  146. layer {
  147. name: "conv1"
  148. type: "Convolution"
  149. bottom: "conv1/dw"
  150. top: "conv1"
  151. param {
  152. lr_mult: 0.1
  153. decay_mult: 0.1
  154. }
  155. convolution_param {
  156. num_output: 64
  157. bias_term: false
  158. kernel_size: 1
  159. weight_filler {
  160. type: "msra"
  161. }
  162. }
  163. }
  164. layer {
  165. name: "conv1/bn"
  166. type: "BatchNorm"
  167. bottom: "conv1"
  168. top: "conv1"
  169. param {
  170. lr_mult: 0
  171. decay_mult: 0
  172. }
  173. param {
  174. lr_mult: 0
  175. decay_mult: 0
  176. }
  177. param {
  178. lr_mult: 0
  179. decay_mult: 0
  180. }
  181. }
  182. layer {
  183. name: "conv1/scale"
  184. type: "Scale"
  185. bottom: "conv1"
  186. top: "conv1"
  187. param {
  188. lr_mult: 0.1
  189. decay_mult: 0.0
  190. }
  191. param {
  192. lr_mult: 0.2
  193. decay_mult: 0.0
  194. }
  195. scale_param {
  196. filler {
  197. value: 1
  198. }
  199. bias_term: true
  200. bias_filler {
  201. value: 0
  202. }
  203. }
  204. }
  205. layer {
  206. name: "conv1/relu"
  207. type: "ReLU"
  208. bottom: "conv1"
  209. top: "conv1"
  210. }
  211. layer {
  212. name: "conv2/dw"
  213. type: "DepthwiseConvolution"
  214. bottom: "conv1"
  215. top: "conv2/dw"
  216. param {
  217. lr_mult: 0.1
  218. decay_mult: 0.1
  219. }
  220. convolution_param {
  221. num_output: 64
  222. bias_term: false
  223. pad: 1
  224. kernel_size: 3
  225. stride: 2
  226. group: 64
  227. engine: CAFFE
  228. weight_filler {
  229. type: "msra"
  230. }
  231. }
  232. }
  233. layer {
  234. name: "conv2/dw/bn"
  235. type: "BatchNorm"
  236. bottom: "conv2/dw"
  237. top: "conv2/dw"
  238. param {
  239. lr_mult: 0
  240. decay_mult: 0
  241. }
  242. param {
  243. lr_mult: 0
  244. decay_mult: 0
  245. }
  246. param {
  247. lr_mult: 0
  248. decay_mult: 0
  249. }
  250. }
  251. layer {
  252. name: "conv2/dw/scale"
  253. type: "Scale"
  254. bottom: "conv2/dw"
  255. top: "conv2/dw"
  256. param {
  257. lr_mult: 0.1
  258. decay_mult: 0.0
  259. }
  260. param {
  261. lr_mult: 0.2
  262. decay_mult: 0.0
  263. }
  264. scale_param {
  265. filler {
  266. value: 1
  267. }
  268. bias_term: true
  269. bias_filler {
  270. value: 0
  271. }
  272. }
  273. }
  274. layer {
  275. name: "conv2/dw/relu"
  276. type: "ReLU"
  277. bottom: "conv2/dw"
  278. top: "conv2/dw"
  279. }
  280. layer {
  281. name: "conv2"
  282. type: "Convolution"
  283. bottom: "conv2/dw"
  284. top: "conv2"
  285. param {
  286. lr_mult: 0.1
  287. decay_mult: 0.1
  288. }
  289. convolution_param {
  290. num_output: 128
  291. bias_term: false
  292. kernel_size: 1
  293. weight_filler {
  294. type: "msra"
  295. }
  296. }
  297. }
  298. layer {
  299. name: "conv2/bn"
  300. type: "BatchNorm"
  301. bottom: "conv2"
  302. top: "conv2"
  303. param {
  304. lr_mult: 0
  305. decay_mult: 0
  306. }
  307. param {
  308. lr_mult: 0
  309. decay_mult: 0
  310. }
  311. param {
  312. lr_mult: 0
  313. decay_mult: 0
  314. }
  315. }
  316. layer {
  317. name: "conv2/scale"
  318. type: "Scale"
  319. bottom: "conv2"
  320. top: "conv2"
  321. param {
  322. lr_mult: 0.1
  323. decay_mult: 0.0
  324. }
  325. param {
  326. lr_mult: 0.2
  327. decay_mult: 0.0
  328. }
  329. scale_param {
  330. filler {
  331. value: 1
  332. }
  333. bias_term: true
  334. bias_filler {
  335. value: 0
  336. }
  337. }
  338. }
  339. layer {
  340. name: "conv2/relu"
  341. type: "ReLU"
  342. bottom: "conv2"
  343. top: "conv2"
  344. }
  345. layer {
  346. name: "conv3/dw"
  347. type: "DepthwiseConvolution"
  348. bottom: "conv2"
  349. top: "conv3/dw"
  350. param {
  351. lr_mult: 0.1
  352. decay_mult: 0.1
  353. }
  354. convolution_param {
  355. num_output: 128
  356. bias_term: false
  357. pad: 1
  358. kernel_size: 3
  359. group: 128
  360. engine: CAFFE
  361. weight_filler {
  362. type: "msra"
  363. }
  364. }
  365. }
  366. layer {
  367. name: "conv3/dw/bn"
  368. type: "BatchNorm"
  369. bottom: "conv3/dw"
  370. top: "conv3/dw"
  371. param {
  372. lr_mult: 0
  373. decay_mult: 0
  374. }
  375. param {
  376. lr_mult: 0
  377. decay_mult: 0
  378. }
  379. param {
  380. lr_mult: 0
  381. decay_mult: 0
  382. }
  383. }
  384. layer {
  385. name: "conv3/dw/scale"
  386. type: "Scale"
  387. bottom: "conv3/dw"
  388. top: "conv3/dw"
  389. param {
  390. lr_mult: 0.1
  391. decay_mult: 0.0
  392. }
  393. param {
  394. lr_mult: 0.2
  395. decay_mult: 0.0
  396. }
  397. scale_param {
  398. filler {
  399. value: 1
  400. }
  401. bias_term: true
  402. bias_filler {
  403. value: 0
  404. }
  405. }
  406. }
  407. layer {
  408. name: "conv3/dw/relu"
  409. type: "ReLU"
  410. bottom: "conv3/dw"
  411. top: "conv3/dw"
  412. }
  413. layer {
  414. name: "conv3"
  415. type: "Convolution"
  416. bottom: "conv3/dw"
  417. top: "conv3"
  418. param {
  419. lr_mult: 0.1
  420. decay_mult: 0.1
  421. }
  422. convolution_param {
  423. num_output: 128
  424. bias_term: false
  425. kernel_size: 1
  426. weight_filler {
  427. type: "msra"
  428. }
  429. }
  430. }
  431. layer {
  432. name: "conv3/bn"
  433. type: "BatchNorm"
  434. bottom: "conv3"
  435. top: "conv3"
  436. param {
  437. lr_mult: 0
  438. decay_mult: 0
  439. }
  440. param {
  441. lr_mult: 0
  442. decay_mult: 0
  443. }
  444. param {
  445. lr_mult: 0
  446. decay_mult: 0
  447. }
  448. }
  449. layer {
  450. name: "conv3/scale"
  451. type: "Scale"
  452. bottom: "conv3"
  453. top: "conv3"
  454. param {
  455. lr_mult: 0.1
  456. decay_mult: 0.0
  457. }
  458. param {
  459. lr_mult: 0.2
  460. decay_mult: 0.0
  461. }
  462. scale_param {
  463. filler {
  464. value: 1
  465. }
  466. bias_term: true
  467. bias_filler {
  468. value: 0
  469. }
  470. }
  471. }
  472. layer {
  473. name: "conv3/relu"
  474. type: "ReLU"
  475. bottom: "conv3"
  476. top: "conv3"
  477. }
  478. layer {
  479. name: "conv4/dw"
  480. type: "DepthwiseConvolution"
  481. bottom: "conv3"
  482. top: "conv4/dw"
  483. param {
  484. lr_mult: 0.1
  485. decay_mult: 0.1
  486. }
  487. convolution_param {
  488. num_output: 128
  489. bias_term: false
  490. pad: 1
  491. kernel_size: 3
  492. stride: 2
  493. group: 128
  494. engine: CAFFE
  495. weight_filler {
  496. type: "msra"
  497. }
  498. }
  499. }
  500. layer {
  501. name: "conv4/dw/bn"
  502. type: "BatchNorm"
  503. bottom: "conv4/dw"
  504. top: "conv4/dw"
  505. param {
  506. lr_mult: 0
  507. decay_mult: 0
  508. }
  509. param {
  510. lr_mult: 0
  511. decay_mult: 0
  512. }
  513. param {
  514. lr_mult: 0
  515. decay_mult: 0
  516. }
  517. }
  518. layer {
  519. name: "conv4/dw/scale"
  520. type: "Scale"
  521. bottom: "conv4/dw"
  522. top: "conv4/dw"
  523. param {
  524. lr_mult: 0.1
  525. decay_mult: 0.0
  526. }
  527. param {
  528. lr_mult: 0.2
  529. decay_mult: 0.0
  530. }
  531. scale_param {
  532. filler {
  533. value: 1
  534. }
  535. bias_term: true
  536. bias_filler {
  537. value: 0
  538. }
  539. }
  540. }
  541. layer {
  542. name: "conv4/dw/relu"
  543. type: "ReLU"
  544. bottom: "conv4/dw"
  545. top: "conv4/dw"
  546. }
  547. layer {
  548. name: "conv4"
  549. type: "Convolution"
  550. bottom: "conv4/dw"
  551. top: "conv4"
  552. param {
  553. lr_mult: 0.1
  554. decay_mult: 0.1
  555. }
  556. convolution_param {
  557. num_output: 256
  558. bias_term: false
  559. kernel_size: 1
  560. weight_filler {
  561. type: "msra"
  562. }
  563. }
  564. }
  565. layer {
  566. name: "conv4/bn"
  567. type: "BatchNorm"
  568. bottom: "conv4"
  569. top: "conv4"
  570. param {
  571. lr_mult: 0
  572. decay_mult: 0
  573. }
  574. param {
  575. lr_mult: 0
  576. decay_mult: 0
  577. }
  578. param {
  579. lr_mult: 0
  580. decay_mult: 0
  581. }
  582. }
  583. layer {
  584. name: "conv4/scale"
  585. type: "Scale"
  586. bottom: "conv4"
  587. top: "conv4"
  588. param {
  589. lr_mult: 0.1
  590. decay_mult: 0.0
  591. }
  592. param {
  593. lr_mult: 0.2
  594. decay_mult: 0.0
  595. }
  596. scale_param {
  597. filler {
  598. value: 1
  599. }
  600. bias_term: true
  601. bias_filler {
  602. value: 0
  603. }
  604. }
  605. }
  606. layer {
  607. name: "conv4/relu"
  608. type: "ReLU"
  609. bottom: "conv4"
  610. top: "conv4"
  611. }
  612. layer {
  613. name: "conv5/dw"
  614. type: "DepthwiseConvolution"
  615. bottom: "conv4"
  616. top: "conv5/dw"
  617. param {
  618. lr_mult: 0.1
  619. decay_mult: 0.1
  620. }
  621. convolution_param {
  622. num_output: 256
  623. bias_term: false
  624. pad: 1
  625. kernel_size: 3
  626. group: 256
  627. engine: CAFFE
  628. weight_filler {
  629. type: "msra"
  630. }
  631. }
  632. }
  633. layer {
  634. name: "conv5/dw/bn"
  635. type: "BatchNorm"
  636. bottom: "conv5/dw"
  637. top: "conv5/dw"
  638. param {
  639. lr_mult: 0
  640. decay_mult: 0
  641. }
  642. param {
  643. lr_mult: 0
  644. decay_mult: 0
  645. }
  646. param {
  647. lr_mult: 0
  648. decay_mult: 0
  649. }
  650. }
  651. layer {
  652. name: "conv5/dw/scale"
  653. type: "Scale"
  654. bottom: "conv5/dw"
  655. top: "conv5/dw"
  656. param {
  657. lr_mult: 0.1
  658. decay_mult: 0.0
  659. }
  660. param {
  661. lr_mult: 0.2
  662. decay_mult: 0.0
  663. }
  664. scale_param {
  665. filler {
  666. value: 1
  667. }
  668. bias_term: true
  669. bias_filler {
  670. value: 0
  671. }
  672. }
  673. }
  674. layer {
  675. name: "conv5/dw/relu"
  676. type: "ReLU"
  677. bottom: "conv5/dw"
  678. top: "conv5/dw"
  679. }
  680. layer {
  681. name: "conv5"
  682. type: "Convolution"
  683. bottom: "conv5/dw"
  684. top: "conv5"
  685. param {
  686. lr_mult: 0.1
  687. decay_mult: 0.1
  688. }
  689. convolution_param {
  690. num_output: 256
  691. bias_term: false
  692. kernel_size: 1
  693. weight_filler {
  694. type: "msra"
  695. }
  696. }
  697. }
  698. layer {
  699. name: "conv5/bn"
  700. type: "BatchNorm"
  701. bottom: "conv5"
  702. top: "conv5"
  703. param {
  704. lr_mult: 0
  705. decay_mult: 0
  706. }
  707. param {
  708. lr_mult: 0
  709. decay_mult: 0
  710. }
  711. param {
  712. lr_mult: 0
  713. decay_mult: 0
  714. }
  715. }
  716. layer {
  717. name: "conv5/scale"
  718. type: "Scale"
  719. bottom: "conv5"
  720. top: "conv5"
  721. param {
  722. lr_mult: 0.1
  723. decay_mult: 0.0
  724. }
  725. param {
  726. lr_mult: 0.2
  727. decay_mult: 0.0
  728. }
  729. scale_param {
  730. filler {
  731. value: 1
  732. }
  733. bias_term: true
  734. bias_filler {
  735. value: 0
  736. }
  737. }
  738. }
  739. layer {
  740. name: "conv5/relu"
  741. type: "ReLU"
  742. bottom: "conv5"
  743. top: "conv5"
  744. }
  745. layer {
  746. name: "conv6/dw"
  747. type: "DepthwiseConvolution"
  748. bottom: "conv5"
  749. top: "conv6/dw"
  750. param {
  751. lr_mult: 0.1
  752. decay_mult: 0.1
  753. }
  754. convolution_param {
  755. num_output: 256
  756. bias_term: false
  757. pad: 1
  758. kernel_size: 3
  759. stride: 2
  760. group: 256
  761. engine: CAFFE
  762. weight_filler {
  763. type: "msra"
  764. }
  765. }
  766. }
  767. layer {
  768. name: "conv6/dw/bn"
  769. type: "BatchNorm"
  770. bottom: "conv6/dw"
  771. top: "conv6/dw"
  772. param {
  773. lr_mult: 0
  774. decay_mult: 0
  775. }
  776. param {
  777. lr_mult: 0
  778. decay_mult: 0
  779. }
  780. param {
  781. lr_mult: 0
  782. decay_mult: 0
  783. }
  784. }
  785. layer {
  786. name: "conv6/dw/scale"
  787. type: "Scale"
  788. bottom: "conv6/dw"
  789. top: "conv6/dw"
  790. param {
  791. lr_mult: 0.1
  792. decay_mult: 0.0
  793. }
  794. param {
  795. lr_mult: 0.2
  796. decay_mult: 0.0
  797. }
  798. scale_param {
  799. filler {
  800. value: 1
  801. }
  802. bias_term: true
  803. bias_filler {
  804. value: 0
  805. }
  806. }
  807. }
  808. layer {
  809. name: "conv6/dw/relu"
  810. type: "ReLU"
  811. bottom: "conv6/dw"
  812. top: "conv6/dw"
  813. }
  814. layer {
  815. name: "conv6"
  816. type: "Convolution"
  817. bottom: "conv6/dw"
  818. top: "conv6"
  819. param {
  820. lr_mult: 0.1
  821. decay_mult: 0.1
  822. }
  823. convolution_param {
  824. num_output: 512
  825. bias_term: false
  826. kernel_size: 1
  827. weight_filler {
  828. type: "msra"
  829. }
  830. }
  831. }
  832. layer {
  833. name: "conv6/bn"
  834. type: "BatchNorm"
  835. bottom: "conv6"
  836. top: "conv6"
  837. param {
  838. lr_mult: 0
  839. decay_mult: 0
  840. }
  841. param {
  842. lr_mult: 0
  843. decay_mult: 0
  844. }
  845. param {
  846. lr_mult: 0
  847. decay_mult: 0
  848. }
  849. }
  850. layer {
  851. name: "conv6/scale"
  852. type: "Scale"
  853. bottom: "conv6"
  854. top: "conv6"
  855. param {
  856. lr_mult: 0.1
  857. decay_mult: 0.0
  858. }
  859. param {
  860. lr_mult: 0.2
  861. decay_mult: 0.0
  862. }
  863. scale_param {
  864. filler {
  865. value: 1
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  1671. }
  1672. layer {
  1673. name: "conv12/relu"
  1674. type: "ReLU"
  1675. bottom: "conv12"
  1676. top: "conv12"
  1677. }
  1678. layer {
  1679. name: "conv13/dw"
  1680. type: "DepthwiseConvolution"
  1681. bottom: "conv12"
  1682. top: "conv13/dw"
  1683. param {
  1684. lr_mult: 0.1
  1685. decay_mult: 0.1
  1686. }
  1687. convolution_param {
  1688. num_output: 1024
  1689. bias_term: false
  1690. pad: 1
  1691. kernel_size: 3
  1692. group: 1024
  1693. engine: CAFFE
  1694. weight_filler {
  1695. type: "msra"
  1696. }
  1697. }
  1698. }
  1699. layer {
  1700. name: "conv13/dw/bn"
  1701. type: "BatchNorm"
  1702. bottom: "conv13/dw"
  1703. top: "conv13/dw"
  1704. param {
  1705. lr_mult: 0
  1706. decay_mult: 0
  1707. }
  1708. param {
  1709. lr_mult: 0
  1710. decay_mult: 0
  1711. }
  1712. param {
  1713. lr_mult: 0
  1714. decay_mult: 0
  1715. }
  1716. }
  1717. layer {
  1718. name: "conv13/dw/scale"
  1719. type: "Scale"
  1720. bottom: "conv13/dw"
  1721. top: "conv13/dw"
  1722. param {
  1723. lr_mult: 0.1
  1724. decay_mult: 0.0
  1725. }
  1726. param {
  1727. lr_mult: 0.2
  1728. decay_mult: 0.0
  1729. }
  1730. scale_param {
  1731. filler {
  1732. value: 1
  1733. }
  1734. bias_term: true
  1735. bias_filler {
  1736. value: 0
  1737. }
  1738. }
  1739. }
  1740. layer {
  1741. name: "conv13/dw/relu"
  1742. type: "ReLU"
  1743. bottom: "conv13/dw"
  1744. top: "conv13/dw"
  1745. }
  1746. layer {
  1747. name: "conv13"
  1748. type: "Convolution"
  1749. bottom: "conv13/dw"
  1750. top: "conv13"
  1751. param {
  1752. lr_mult: 0.1
  1753. decay_mult: 0.1
  1754. }
  1755. convolution_param {
  1756. num_output: 1024
  1757. bias_term: false
  1758. kernel_size: 1
  1759. weight_filler {
  1760. type: "msra"
  1761. }
  1762. }
  1763. }
  1764. layer {
  1765. name: "conv13/bn"
  1766. type: "BatchNorm"
  1767. bottom: "conv13"
  1768. top: "conv13"
  1769. param {
  1770. lr_mult: 0
  1771. decay_mult: 0
  1772. }
  1773. param {
  1774. lr_mult: 0
  1775. decay_mult: 0
  1776. }
  1777. param {
  1778. lr_mult: 0
  1779. decay_mult: 0
  1780. }
  1781. }
  1782. layer {
  1783. name: "conv13/scale"
  1784. type: "Scale"
  1785. bottom: "conv13"
  1786. top: "conv13"
  1787. param {
  1788. lr_mult: 0.1
  1789. decay_mult: 0.0
  1790. }
  1791. param {
  1792. lr_mult: 0.2
  1793. decay_mult: 0.0
  1794. }
  1795. scale_param {
  1796. filler {
  1797. value: 1
  1798. }
  1799. bias_term: true
  1800. bias_filler {
  1801. value: 0
  1802. }
  1803. }
  1804. }
  1805. layer {
  1806. name: "conv13/relu"
  1807. type: "ReLU"
  1808. bottom: "conv13"
  1809. top: "conv13"
  1810. }
  1811.  
  1812. #layer {
  1813. # name: "concat1"
  1814. # type: "Concat"
  1815. # bottom: "conv11"
  1816. # top: "concat1"
  1817. #}
  1818.  
  1819. #layer {
  1820. # name: "reorg1"
  1821. # type: "Reorg"
  1822. # bottom: "concat1"
  1823. # top: "reorg1"
  1824. # reorg_param {
  1825. # stride: 2
  1826. # }
  1827. #}
  1828.  
  1829. #layer {
  1830. # name: "concat2"
  1831. # type: "Concat"
  1832. # bottom: "reorg1"
  1833. # bottom: "conv13"
  1834. # top: "concat2"
  1835. #}
  1836. layer {
  1837. name: "conv16/dw"
  1838. type: "DepthwiseConvolution"
  1839. bottom: "conv13"
  1840. top: "conv16/dw"
  1841. param {
  1842. lr_mult: 0.1
  1843. decay_mult: 0.1
  1844. }
  1845. convolution_param {
  1846. num_output: 1024
  1847. bias_term: false
  1848. pad: 1
  1849. kernel_size: 3
  1850. group: 1024
  1851. engine: CAFFE
  1852. weight_filler {
  1853. type: "msra"
  1854. }
  1855. }
  1856. }
  1857. layer {
  1858. name: "conv16/dw/bn"
  1859. type: "BatchNorm"
  1860. bottom: "conv16/dw"
  1861. top: "conv16/dw"
  1862. param {
  1863. lr_mult: 0
  1864. decay_mult: 0
  1865. }
  1866. param {
  1867. lr_mult: 0
  1868. decay_mult: 0
  1869. }
  1870. param {
  1871. lr_mult: 0
  1872. decay_mult: 0
  1873. }
  1874. }
  1875. layer {
  1876. name: "conv16/dw/scale"
  1877. type: "Scale"
  1878. bottom: "conv16/dw"
  1879. top: "conv16/dw"
  1880. param {
  1881. lr_mult: 0.1
  1882. decay_mult: 0.0
  1883. }
  1884. param {
  1885. lr_mult: 0.2
  1886. decay_mult: 0.0
  1887. }
  1888. scale_param {
  1889. filler {
  1890. value: 1
  1891. }
  1892. bias_term: true
  1893. bias_filler {
  1894. value: 0
  1895. }
  1896. }
  1897. }
  1898. layer {
  1899. name: "conv16/dw/relu"
  1900. type: "ReLU"
  1901. bottom: "conv16/dw"
  1902. top: "conv16/dw"
  1903. }
  1904. layer {
  1905. name: "conv17"
  1906. type: "Convolution"
  1907. bottom: "conv16/dw"
  1908. top: "conv17"
  1909. param {
  1910. lr_mult: 0.1
  1911. decay_mult: 0.1
  1912. }
  1913. convolution_param {
  1914. num_output: 1024
  1915. bias_term: false
  1916. kernel_size: 1
  1917. weight_filler {
  1918. type: "msra"
  1919. }
  1920. }
  1921. }
  1922. layer {
  1923. name: "conv17/bn"
  1924. type: "BatchNorm"
  1925. bottom: "conv17"
  1926. top: "conv17"
  1927. param {
  1928. lr_mult: 0
  1929. decay_mult: 0
  1930. }
  1931. param {
  1932. lr_mult: 0
  1933. decay_mult: 0
  1934. }
  1935. param {
  1936. lr_mult: 0
  1937. decay_mult: 0
  1938. }
  1939. }
  1940. layer {
  1941. name: "conv17/scale"
  1942. type: "Scale"
  1943. bottom: "conv17"
  1944. top: "conv17"
  1945. param {
  1946. lr_mult: 0.1
  1947. decay_mult: 0.0
  1948. }
  1949. param {
  1950. lr_mult: 0.2
  1951. decay_mult: 0.0
  1952. }
  1953. scale_param {
  1954. filler {
  1955. value: 1
  1956. }
  1957. bias_term: true
  1958. bias_filler {
  1959. value: 0
  1960. }
  1961. }
  1962. }
  1963. layer {
  1964. name: "conv17/relu"
  1965. type: "ReLU"
  1966. bottom: "conv17"
  1967. top: "conv17"
  1968. }
  1969. layer {
  1970. name: "upsample"
  1971. type: "Deconvolution"
  1972. bottom: "conv17"
  1973. top: "upsample"
  1974. param { lr_mult: 0 decay_mult: 0 }
  1975. convolution_param {
  1976. num_output: 512
  1977. kernel_size: 4 stride: 2 pad: 1
  1978. group: 512
  1979. weight_filler: { type: "bilinear" }
  1980. bias_term: false
  1981. }
  1982. }
  1983. layer {
  1984. name: "conv_18/sum"
  1985. type: "Eltwise"
  1986. bottom: "conv11"
  1987. bottom: "upsample"
  1988. top: "conv_18/sum"
  1989. }
  1990. layer {
  1991. name: "conv19/dw"
  1992. type: "DepthwiseConvolution"
  1993. bottom: "conv_18/sum"
  1994. top: "conv19/dw"
  1995. param {
  1996. lr_mult: 0.1
  1997. decay_mult: 0.1
  1998. }
  1999. convolution_param {
  2000. num_output: 512
  2001. bias_term: false
  2002. pad: 1
  2003. kernel_size: 3
  2004. group: 512
  2005. engine: CAFFE
  2006. weight_filler {
  2007. type: "msra"
  2008. }
  2009. }
  2010. }
  2011. layer {
  2012. name: "conv19/dw/bn"
  2013. type: "BatchNorm"
  2014. bottom: "conv19/dw"
  2015. top: "conv19/dw"
  2016. param {
  2017. lr_mult: 0
  2018. decay_mult: 0
  2019. }
  2020. param {
  2021. lr_mult: 0
  2022. decay_mult: 0
  2023. }
  2024. param {
  2025. lr_mult: 0
  2026. decay_mult: 0
  2027. }
  2028. }
  2029. layer {
  2030. name: "conv19/dw/scale"
  2031. type: "Scale"
  2032. bottom: "conv19/dw"
  2033. top: "conv19/dw"
  2034. param {
  2035. lr_mult: 0.1
  2036. decay_mult: 0.0
  2037. }
  2038. param {
  2039. lr_mult: 0.2
  2040. decay_mult: 0.0
  2041. }
  2042. scale_param {
  2043. filler {
  2044. value: 1
  2045. }
  2046. bias_term: true
  2047. bias_filler {
  2048. value: 0
  2049. }
  2050. }
  2051. }
  2052. layer {
  2053. name: "conv19/dw/relu"
  2054. type: "ReLU"
  2055. bottom: "conv19/dw"
  2056. top: "conv19/dw"
  2057. }
  2058. layer {
  2059. name: "conv20"
  2060. type: "Convolution"
  2061. bottom: "conv19/dw"
  2062. top: "conv20"
  2063. param {
  2064. lr_mult: 0.1
  2065. decay_mult: 0.1
  2066. }
  2067. convolution_param {
  2068. num_output: 1024
  2069. bias_term: false
  2070. kernel_size: 1
  2071. weight_filler {
  2072. type: "msra"
  2073. }
  2074. }
  2075. }
  2076. layer {
  2077. name: "conv20/bn"
  2078. type: "BatchNorm"
  2079. bottom: "conv20"
  2080. top: "conv20"
  2081. param {
  2082. lr_mult: 0
  2083. decay_mult: 0
  2084. }
  2085. param {
  2086. lr_mult: 0
  2087. decay_mult: 0
  2088. }
  2089. param {
  2090. lr_mult: 0
  2091. decay_mult: 0
  2092. }
  2093. }
  2094. layer {
  2095. name: "conv20/scale"
  2096. type: "Scale"
  2097. bottom: "conv20"
  2098. top: "conv20"
  2099. param {
  2100. lr_mult: 0.1
  2101. decay_mult: 0.0
  2102. }
  2103. param {
  2104. lr_mult: 0.2
  2105. decay_mult: 0.0
  2106. }
  2107. scale_param {
  2108. filler {
  2109. value: 1
  2110. }
  2111. bias_term: true
  2112. bias_filler {
  2113. value: 0
  2114. }
  2115. }
  2116. }
  2117. layer {
  2118. name: "conv20/relu"
  2119. type: "ReLU"
  2120. bottom: "conv20"
  2121. top: "conv20"
  2122. }
  2123.  
  2124. layer {
  2125. name: "conv22_indoor"
  2126. type: "Convolution"
  2127. bottom: "conv17"
  2128. top: "conv22"
  2129. param {
  2130. lr_mult: 1
  2131. decay_mult: 1
  2132. }
  2133. param {
  2134. lr_mult: 2
  2135. decay_mult: 0
  2136. }
  2137. convolution_param {
  2138. num_output: 125
  2139. kernel_size: 1
  2140. pad: 0
  2141. stride: 1
  2142. weight_filler {
  2143. type: "msra"
  2144. }
  2145. bias_filler {
  2146. value: 0
  2147. }
  2148. }
  2149. }
  2150. layer {
  2151. name: "conv23_indoor"
  2152. type: "Convolution"
  2153. bottom: "conv20"
  2154. top: "conv23"
  2155. param {
  2156. lr_mult: 1
  2157. decay_mult: 1
  2158. }
  2159. param {
  2160. lr_mult: 2
  2161. decay_mult: 0
  2162. }
  2163. convolution_param {
  2164. num_output: 125
  2165. kernel_size: 1
  2166. pad: 0
  2167. stride: 1
  2168. weight_filler {
  2169. type: "msra"
  2170. }
  2171. bias_filler {
  2172. value: 0
  2173. }
  2174. }
  2175. }
  2176.  
  2177. layer {
  2178. name: "detection_out"
  2179. type: "YoloDetectionOutput"
  2180. bottom: "conv22"
  2181. bottom: "conv23"
  2182. top: "detection_out"
  2183. include {
  2184. phase: TEST
  2185. }
  2186. yolo_detection_output_param {
  2187. num_classes: 20
  2188. coords: 4
  2189. confidence_threshold: 0.40
  2190. nms_threshold: 0.45
  2191.  
  2192. biases: 1.08
  2193. biases: 1.19
  2194. biases: 3.42
  2195. biases: 4.41
  2196. biases: 6.63
  2197. biases: 11.38
  2198. biases: 9.42
  2199. biases: 5.11
  2200. biases: 16.62
  2201. biases: 10.52
  2202. }
  2203. }
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