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  1. name: "YOLONet"
  2. input: "data"
  3. input_shape {
  4. dim: 1
  5. dim: 3
  6. dim: 448
  7. dim: 448
  8. }
  9.  
  10. layer {
  11. name: "conv1"
  12. type: "Convolution"
  13. bottom: "data"
  14. top: "conv1"
  15. convolution_param {
  16. num_output: 64
  17. kernel_size: 7
  18. pad: 3
  19. stride: 2
  20. }
  21. }
  22. layer {
  23. name: "relu1"
  24. type: "ReLU"
  25. bottom: "conv1"
  26. top: "conv1"
  27. relu_param{
  28. negative_slope: 0.1
  29. }
  30. }
  31. layer {
  32. name: "pool1"
  33. type: "Pooling"
  34. bottom: "conv1"
  35. top: "pool1"
  36. pooling_param {
  37. pool: MAX
  38. kernel_size: 2
  39. stride: 2
  40. }
  41. }
  42.  
  43. layer{
  44. name: "conv2"
  45. type: "Convolution"
  46. bottom: "pool1"
  47. top: "conv2"
  48. convolution_param {
  49. num_output: 192
  50. kernel_size: 3
  51. pad: 1
  52. stride: 1
  53. }
  54. }
  55. layer {
  56. name: "relu2"
  57. type: "ReLU"
  58. bottom: "conv2"
  59. top: "conv2"
  60. relu_param{
  61. negative_slope: 0.1
  62. }
  63. }
  64. layer {
  65. name: "pool2"
  66. type: "Pooling"
  67. bottom: "conv2"
  68. top: "pool2"
  69. pooling_param {
  70. pool: MAX
  71. kernel_size: 2
  72. stride: 2
  73. }
  74. }
  75.  
  76. layer{
  77. name: "conv3"
  78. type: "Convolution"
  79. bottom: "pool2"
  80. top: "conv3"
  81. convolution_param {
  82. num_output: 128
  83. kernel_size: 1
  84. pad: 0
  85. stride: 1
  86. }
  87. }
  88. layer {
  89. name: "relu3"
  90. type: "ReLU"
  91. bottom: "conv3"
  92. top: "conv3"
  93. relu_param{
  94. negative_slope: 0.1
  95. }
  96. }
  97.  
  98.  
  99. layer{
  100. name: "conv4"
  101. type: "Convolution"
  102. bottom: "conv3"
  103. top: "conv4"
  104. convolution_param {
  105. num_output: 256
  106. kernel_size: 3
  107. pad: 1
  108. stride: 1
  109. }
  110. }
  111. layer {
  112. name: "relu4"
  113. type: "ReLU"
  114. bottom: "conv4"
  115. top: "conv4"
  116. relu_param{
  117. negative_slope: 0.1
  118. }
  119. }
  120.  
  121. layer{
  122. name: "conv5"
  123. type: "Convolution"
  124. bottom: "conv4"
  125. top: "conv5"
  126. convolution_param {
  127. num_output: 256
  128. kernel_size: 1
  129. pad: 0
  130. stride: 1
  131. }
  132. }
  133. layer {
  134. name: "relu5"
  135. type: "ReLU"
  136. bottom: "conv5"
  137. top: "conv5"
  138. relu_param{
  139. negative_slope: 0.1
  140. }
  141. }
  142.  
  143. layer{
  144. name: "conv6"
  145. type: "Convolution"
  146. bottom: "conv5"
  147. top: "conv6"
  148. convolution_param {
  149. num_output: 512
  150. kernel_size: 3
  151. pad: 1
  152. stride: 1
  153. }
  154. }
  155. layer {
  156. name: "relu6"
  157. type: "ReLU"
  158. bottom: "conv6"
  159. top: "conv6"
  160. relu_param{
  161. negative_slope: 0.1
  162. }
  163. }
  164. layer {
  165. name: "pool6"
  166. type: "Pooling"
  167. bottom: "conv6"
  168. top: "pool6"
  169. pooling_param {
  170. pool: MAX
  171. kernel_size: 2
  172. stride: 2
  173. }
  174. }
  175.  
  176. layer{
  177. name: "conv7"
  178. type: "Convolution"
  179. bottom: "pool6"
  180. top: "conv7"
  181. convolution_param {
  182. num_output: 256
  183. kernel_size: 1
  184. pad: 0
  185. stride: 1
  186. }
  187. }
  188. layer {
  189. name: "relu7"
  190. type: "ReLU"
  191. bottom: "conv7"
  192. top: "conv7"
  193. relu_param{
  194. negative_slope: 0.1
  195. }
  196. }
  197.  
  198. layer{
  199. name: "conv8"
  200. type: "Convolution"
  201. bottom: "conv7"
  202. top: "conv8"
  203. convolution_param {
  204. num_output: 512
  205. kernel_size: 3
  206. pad: 1
  207. stride: 1
  208. }
  209. }
  210. layer {
  211. name: "relu8"
  212. type: "ReLU"
  213. bottom: "conv8"
  214. top: "conv8"
  215. relu_param{
  216. negative_slope: 0.1
  217. }
  218. }
  219.  
  220. layer{
  221. name: "conv9"
  222. type: "Convolution"
  223. bottom: "conv8"
  224. top: "conv9"
  225. convolution_param {
  226. num_output: 256
  227. kernel_size: 1
  228. pad: 0
  229. stride: 1
  230. }
  231. }
  232. layer {
  233. name: "relu9"
  234. type: "ReLU"
  235. bottom: "conv9"
  236. top: "conv9"
  237. relu_param{
  238. negative_slope: 0.1
  239. }
  240. }
  241.  
  242. layer{
  243. name: "conv10"
  244. type: "Convolution"
  245. bottom: "conv9"
  246. top: "conv10"
  247. convolution_param {
  248. num_output: 512
  249. kernel_size: 3
  250. pad: 1
  251. stride: 1
  252. }
  253. }
  254. layer {
  255. name: "relu10"
  256. type: "ReLU"
  257. bottom: "conv10"
  258. top: "conv10"
  259. relu_param{
  260. negative_slope: 0.1
  261. }
  262. }
  263.  
  264. layer{
  265. name: "conv11"
  266. type: "Convolution"
  267. bottom: "conv10"
  268. top: "conv11"
  269. convolution_param {
  270. num_output: 256
  271. kernel_size: 1
  272. pad: 0
  273. stride: 1
  274. }
  275. }
  276. layer {
  277. name: "relu11"
  278. type: "ReLU"
  279. bottom: "conv11"
  280. top: "conv11"
  281. relu_param{
  282. negative_slope: 0.1
  283. }
  284. }
  285.  
  286.  
  287. layer{
  288. name: "conv12"
  289. type: "Convolution"
  290. bottom: "conv11"
  291. top: "conv12"
  292. convolution_param {
  293. num_output: 512
  294. kernel_size: 3
  295. pad: 1
  296. stride: 1
  297. }
  298. }
  299. layer {
  300. name: "relu12"
  301. type: "ReLU"
  302. bottom: "conv12"
  303. top: "conv12"
  304. relu_param{
  305. negative_slope: 0.1
  306. }
  307. }
  308.  
  309.  
  310. layer{
  311. name: "conv13"
  312. type: "Convolution"
  313. bottom: "conv12"
  314. top: "conv13"
  315. convolution_param {
  316. num_output: 256
  317. kernel_size: 1
  318. pad: 0
  319. stride: 1
  320. }
  321. }
  322. layer {
  323. name: "relu13"
  324. type: "ReLU"
  325. bottom: "conv13"
  326. top: "conv13"
  327. relu_param{
  328. negative_slope: 0.1
  329. }
  330. }
  331.  
  332. layer{
  333. name: "conv14"
  334. type: "Convolution"
  335. bottom: "conv13"
  336. top: "conv14"
  337. convolution_param {
  338. num_output: 512
  339. kernel_size: 3
  340. pad: 1
  341. stride: 1
  342. }
  343. }
  344. layer {
  345. name: "relu14"
  346. type: "ReLU"
  347. bottom: "conv14"
  348. top: "conv14"
  349. relu_param{
  350. negative_slope: 0.1
  351. }
  352. }
  353.  
  354. layer{
  355. name: "conv15"
  356. type: "Convolution"
  357. bottom: "conv14"
  358. top: "conv15"
  359. convolution_param {
  360. num_output: 512
  361. kernel_size: 1
  362. pad: 0
  363. stride: 1
  364. }
  365. }
  366. layer {
  367. name: "relu15"
  368. type: "ReLU"
  369. bottom: "conv15"
  370. top: "conv15"
  371. relu_param{
  372. negative_slope: 0.1
  373. }
  374. }
  375.  
  376.  
  377. layer{
  378. name: "conv16"
  379. type: "Convolution"
  380. bottom: "conv15"
  381. top: "conv16"
  382. convolution_param {
  383. num_output: 1024
  384. kernel_size: 3
  385. pad: 1
  386. stride: 1
  387. }
  388. }
  389. layer {
  390. name: "relu16"
  391. type: "ReLU"
  392. bottom: "conv16"
  393. top: "conv16"
  394. relu_param{
  395. negative_slope: 0.1
  396. }
  397. }
  398.  
  399. layer {
  400. name: "pool16"
  401. type: "Pooling"
  402. bottom: "conv16"
  403. top: "pool16"
  404. pooling_param {
  405. pool: MAX
  406. kernel_size: 2
  407. stride: 2
  408. }
  409. }
  410.  
  411.  
  412. layer{
  413. name: "conv17"
  414. type: "Convolution"
  415. bottom: "pool16"
  416. top: "conv17"
  417. convolution_param {
  418. num_output: 512
  419. kernel_size: 1
  420. pad: 0
  421. stride: 1
  422. }
  423. }
  424. layer {
  425. name: "relu17"
  426. type: "ReLU"
  427. bottom: "conv17"
  428. top: "conv17"
  429. relu_param{
  430. negative_slope: 0.1
  431. }
  432. }
  433.  
  434.  
  435. layer{
  436. name: "conv18"
  437. type: "Convolution"
  438. bottom: "conv17"
  439. top: "conv18"
  440. convolution_param {
  441. num_output: 1024
  442. kernel_size: 3
  443. pad: 1
  444. stride: 1
  445. }
  446. }
  447. layer {
  448. name: "relu18"
  449. type: "ReLU"
  450. bottom: "conv18"
  451. top: "conv18"
  452. relu_param{
  453. negative_slope: 0.1
  454. }
  455. }
  456.  
  457.  
  458.  
  459. layer{
  460. name: "conv19"
  461. type: "Convolution"
  462. bottom: "conv18"
  463. top: "conv19"
  464. convolution_param {
  465. num_output: 512
  466. kernel_size: 1
  467. pad: 0
  468. stride: 1
  469. }
  470. }
  471. layer {
  472. name: "relu19"
  473. type: "ReLU"
  474. bottom: "conv19"
  475. top: "conv19"
  476. relu_param{
  477. negative_slope: 0.1
  478. }
  479. }
  480.  
  481.  
  482.  
  483. layer{
  484. name: "conv20"
  485. type: "Convolution"
  486. bottom: "conv19"
  487. top: "conv20"
  488. convolution_param {
  489. num_output: 1024
  490. kernel_size: 3
  491. pad: 1
  492. stride: 1
  493. }
  494. }
  495. layer {
  496. name: "relu20"
  497. type: "ReLU"
  498. bottom: "conv20"
  499. top: "conv20"
  500. relu_param{
  501. negative_slope: 0.1
  502. }
  503. }
  504.  
  505.  
  506.  
  507. layer{
  508. name: "conv21"
  509. type: "Convolution"
  510. bottom: "conv20"
  511. top: "conv21"
  512. convolution_param {
  513. num_output: 1024
  514. kernel_size: 3
  515. pad: 1
  516. stride: 1
  517. }
  518. }
  519. layer {
  520. name: "relu21"
  521. type: "ReLU"
  522. bottom: "conv21"
  523. top: "conv21"
  524. relu_param{
  525. negative_slope: 0.1
  526. }
  527. }
  528.  
  529.  
  530. layer{
  531. name: "conv22"
  532. type: "Convolution"
  533. bottom: "conv21"
  534. top: "conv22"
  535. convolution_param {
  536. num_output: 1024
  537. kernel_size: 3
  538. pad: 1
  539. stride: 2
  540. }
  541. }
  542. layer {
  543. name: "relu22"
  544. type: "ReLU"
  545. bottom: "conv22"
  546. top: "conv22"
  547. relu_param{
  548. negative_slope: 0.1
  549. }
  550. }
  551.  
  552.  
  553.  
  554. layer{
  555. name: "conv23"
  556. type: "Convolution"
  557. bottom: "conv22"
  558. top: "conv23"
  559. convolution_param {
  560. num_output: 1024
  561. kernel_size: 3
  562. pad: 1
  563. stride: 1
  564. }
  565. }
  566. layer {
  567. name: "relu23"
  568. type: "ReLU"
  569. bottom: "conv23"
  570. top: "conv23"
  571. relu_param{
  572. negative_slope: 0.1
  573. }
  574. }
  575.  
  576.  
  577. layer{
  578. name: "conv24"
  579. type: "Convolution"
  580. bottom: "conv23"
  581. top: "conv24"
  582. convolution_param {
  583. num_output: 1024
  584. kernel_size: 3
  585. pad: 1
  586. stride: 1
  587. }
  588. }
  589. layer {
  590. name: "relu24"
  591. type: "ReLU"
  592. bottom: "conv24"
  593. top: "conv24"
  594. relu_param{
  595. negative_slope: 0.1
  596. }
  597. }
  598.  
  599.  
  600.  
  601.  
  602. layer{
  603. name: "fc25"
  604. type: "InnerProduct"
  605. bottom: "conv24"
  606. top: "fc25"
  607. inner_product_param {
  608. num_output: 4096
  609. }
  610. }
  611. layer {
  612. name: "relu25"
  613. type: "ReLU"
  614. bottom: "fc25"
  615. top: "fc25"
  616. relu_param{
  617. negative_slope: 0.1
  618. }
  619. }
  620.  
  621.  
  622. layer{
  623. name: "fc26"
  624. type: "InnerProduct"
  625. bottom: "fc25"
  626. top: "result"
  627. inner_product_param {
  628. num_output: 1470
  629. }
  630. }
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