darknet53_448.cfg 5.7 KB

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266267268269270271272273274275276277278279280281282283284285286287288289290291292293294295296297298299300301302303304305306307308309310311312313314315316317318319320321322323324325326327328329330331332333334335336337338339340341342343344345346347348349350351352353354355356357358359360361362363364365366367368369370371372373374375376377378379380381382383384385386387388389390391392393394395396397398399400401402403404405406407408409410411412413414415416417418419420421422423424425426427428429430431432433434435436437438439440441442443444445446447448449450451452453454455456457458459460461462463464465466467468469470471472473474475476477478479480481482483484485486487488489490491492493494495496497498499500501502503504505506507508509510511512513514515516517518519520521522523524525526527528529530531532533534535536537538539540541542543544545546547548549550551552553554555556557558559
  1. [net]
  2. # Training - start training with darknet53.weights
  3. # batch=128
  4. # subdivisions=8
  5. # Testing
  6. batch=1
  7. subdivisions=1
  8. height=448
  9. width=448
  10. channels=3
  11. min_crop=448
  12. max_crop=512
  13. learning_rate=0.001
  14. policy=poly
  15. power=4
  16. max_batches=100000
  17. momentum=0.9
  18. decay=0.0005
  19. [convolutional]
  20. batch_normalize=1
  21. filters=32
  22. size=3
  23. stride=1
  24. pad=1
  25. activation=leaky
  26. # Downsample
  27. [convolutional]
  28. batch_normalize=1
  29. filters=64
  30. size=3
  31. stride=2
  32. pad=1
  33. activation=leaky
  34. [convolutional]
  35. batch_normalize=1
  36. filters=32
  37. size=1
  38. stride=1
  39. pad=1
  40. activation=leaky
  41. [convolutional]
  42. batch_normalize=1
  43. filters=64
  44. size=3
  45. stride=1
  46. pad=1
  47. activation=leaky
  48. [shortcut]
  49. from=-3
  50. activation=linear
  51. # Downsample
  52. [convolutional]
  53. batch_normalize=1
  54. filters=128
  55. size=3
  56. stride=2
  57. pad=1
  58. activation=leaky
  59. [convolutional]
  60. batch_normalize=1
  61. filters=64
  62. size=1
  63. stride=1
  64. pad=1
  65. activation=leaky
  66. [convolutional]
  67. batch_normalize=1
  68. filters=128
  69. size=3
  70. stride=1
  71. pad=1
  72. activation=leaky
  73. [shortcut]
  74. from=-3
  75. activation=linear
  76. [convolutional]
  77. batch_normalize=1
  78. filters=64
  79. size=1
  80. stride=1
  81. pad=1
  82. activation=leaky
  83. [convolutional]
  84. batch_normalize=1
  85. filters=128
  86. size=3
  87. stride=1
  88. pad=1
  89. activation=leaky
  90. [shortcut]
  91. from=-3
  92. activation=linear
  93. # Downsample
  94. [convolutional]
  95. batch_normalize=1
  96. filters=256
  97. size=3
  98. stride=2
  99. pad=1
  100. activation=leaky
  101. [convolutional]
  102. batch_normalize=1
  103. filters=128
  104. size=1
  105. stride=1
  106. pad=1
  107. activation=leaky
  108. [convolutional]
  109. batch_normalize=1
  110. filters=256
  111. size=3
  112. stride=1
  113. pad=1
  114. activation=leaky
  115. [shortcut]
  116. from=-3
  117. activation=linear
  118. [convolutional]
  119. batch_normalize=1
  120. filters=128
  121. size=1
  122. stride=1
  123. pad=1
  124. activation=leaky
  125. [convolutional]
  126. batch_normalize=1
  127. filters=256
  128. size=3
  129. stride=1
  130. pad=1
  131. activation=leaky
  132. [shortcut]
  133. from=-3
  134. activation=linear
  135. [convolutional]
  136. batch_normalize=1
  137. filters=128
  138. size=1
  139. stride=1
  140. pad=1
  141. activation=leaky
  142. [convolutional]
  143. batch_normalize=1
  144. filters=256
  145. size=3
  146. stride=1
  147. pad=1
  148. activation=leaky
  149. [shortcut]
  150. from=-3
  151. activation=linear
  152. [convolutional]
  153. batch_normalize=1
  154. filters=128
  155. size=1
  156. stride=1
  157. pad=1
  158. activation=leaky
  159. [convolutional]
  160. batch_normalize=1
  161. filters=256
  162. size=3
  163. stride=1
  164. pad=1
  165. activation=leaky
  166. [shortcut]
  167. from=-3
  168. activation=linear
  169. [convolutional]
  170. batch_normalize=1
  171. filters=128
  172. size=1
  173. stride=1
  174. pad=1
  175. activation=leaky
  176. [convolutional]
  177. batch_normalize=1
  178. filters=256
  179. size=3
  180. stride=1
  181. pad=1
  182. activation=leaky
  183. [shortcut]
  184. from=-3
  185. activation=linear
  186. [convolutional]
  187. batch_normalize=1
  188. filters=128
  189. size=1
  190. stride=1
  191. pad=1
  192. activation=leaky
  193. [convolutional]
  194. batch_normalize=1
  195. filters=256
  196. size=3
  197. stride=1
  198. pad=1
  199. activation=leaky
  200. [shortcut]
  201. from=-3
  202. activation=linear
  203. [convolutional]
  204. batch_normalize=1
  205. filters=128
  206. size=1
  207. stride=1
  208. pad=1
  209. activation=leaky
  210. [convolutional]
  211. batch_normalize=1
  212. filters=256
  213. size=3
  214. stride=1
  215. pad=1
  216. activation=leaky
  217. [shortcut]
  218. from=-3
  219. activation=linear
  220. [convolutional]
  221. batch_normalize=1
  222. filters=128
  223. size=1
  224. stride=1
  225. pad=1
  226. activation=leaky
  227. [convolutional]
  228. batch_normalize=1
  229. filters=256
  230. size=3
  231. stride=1
  232. pad=1
  233. activation=leaky
  234. [shortcut]
  235. from=-3
  236. activation=linear
  237. # Downsample
  238. [convolutional]
  239. batch_normalize=1
  240. filters=512
  241. size=3
  242. stride=2
  243. pad=1
  244. activation=leaky
  245. [convolutional]
  246. batch_normalize=1
  247. filters=256
  248. size=1
  249. stride=1
  250. pad=1
  251. activation=leaky
  252. [convolutional]
  253. batch_normalize=1
  254. filters=512
  255. size=3
  256. stride=1
  257. pad=1
  258. activation=leaky
  259. [shortcut]
  260. from=-3
  261. activation=linear
  262. [convolutional]
  263. batch_normalize=1
  264. filters=256
  265. size=1
  266. stride=1
  267. pad=1
  268. activation=leaky
  269. [convolutional]
  270. batch_normalize=1
  271. filters=512
  272. size=3
  273. stride=1
  274. pad=1
  275. activation=leaky
  276. [shortcut]
  277. from=-3
  278. activation=linear
  279. [convolutional]
  280. batch_normalize=1
  281. filters=256
  282. size=1
  283. stride=1
  284. pad=1
  285. activation=leaky
  286. [convolutional]
  287. batch_normalize=1
  288. filters=512
  289. size=3
  290. stride=1
  291. pad=1
  292. activation=leaky
  293. [shortcut]
  294. from=-3
  295. activation=linear
  296. [convolutional]
  297. batch_normalize=1
  298. filters=256
  299. size=1
  300. stride=1
  301. pad=1
  302. activation=leaky
  303. [convolutional]
  304. batch_normalize=1
  305. filters=512
  306. size=3
  307. stride=1
  308. pad=1
  309. activation=leaky
  310. [shortcut]
  311. from=-3
  312. activation=linear
  313. [convolutional]
  314. batch_normalize=1
  315. filters=256
  316. size=1
  317. stride=1
  318. pad=1
  319. activation=leaky
  320. [convolutional]
  321. batch_normalize=1
  322. filters=512
  323. size=3
  324. stride=1
  325. pad=1
  326. activation=leaky
  327. [shortcut]
  328. from=-3
  329. activation=linear
  330. [convolutional]
  331. batch_normalize=1
  332. filters=256
  333. size=1
  334. stride=1
  335. pad=1
  336. activation=leaky
  337. [convolutional]
  338. batch_normalize=1
  339. filters=512
  340. size=3
  341. stride=1
  342. pad=1
  343. activation=leaky
  344. [shortcut]
  345. from=-3
  346. activation=linear
  347. [convolutional]
  348. batch_normalize=1
  349. filters=256
  350. size=1
  351. stride=1
  352. pad=1
  353. activation=leaky
  354. [convolutional]
  355. batch_normalize=1
  356. filters=512
  357. size=3
  358. stride=1
  359. pad=1
  360. activation=leaky
  361. [shortcut]
  362. from=-3
  363. activation=linear
  364. [convolutional]
  365. batch_normalize=1
  366. filters=256
  367. size=1
  368. stride=1
  369. pad=1
  370. activation=leaky
  371. [convolutional]
  372. batch_normalize=1
  373. filters=512
  374. size=3
  375. stride=1
  376. pad=1
  377. activation=leaky
  378. [shortcut]
  379. from=-3
  380. activation=linear
  381. # Downsample
  382. [convolutional]
  383. batch_normalize=1
  384. filters=1024
  385. size=3
  386. stride=2
  387. pad=1
  388. activation=leaky
  389. [convolutional]
  390. batch_normalize=1
  391. filters=512
  392. size=1
  393. stride=1
  394. pad=1
  395. activation=leaky
  396. [convolutional]
  397. batch_normalize=1
  398. filters=1024
  399. size=3
  400. stride=1
  401. pad=1
  402. activation=leaky
  403. [shortcut]
  404. from=-3
  405. activation=linear
  406. [convolutional]
  407. batch_normalize=1
  408. filters=512
  409. size=1
  410. stride=1
  411. pad=1
  412. activation=leaky
  413. [convolutional]
  414. batch_normalize=1
  415. filters=1024
  416. size=3
  417. stride=1
  418. pad=1
  419. activation=leaky
  420. [shortcut]
  421. from=-3
  422. activation=linear
  423. [convolutional]
  424. batch_normalize=1
  425. filters=512
  426. size=1
  427. stride=1
  428. pad=1
  429. activation=leaky
  430. [convolutional]
  431. batch_normalize=1
  432. filters=1024
  433. size=3
  434. stride=1
  435. pad=1
  436. activation=leaky
  437. [shortcut]
  438. from=-3
  439. activation=linear
  440. [convolutional]
  441. batch_normalize=1
  442. filters=512
  443. size=1
  444. stride=1
  445. pad=1
  446. activation=leaky
  447. [convolutional]
  448. batch_normalize=1
  449. filters=1024
  450. size=3
  451. stride=1
  452. pad=1
  453. activation=leaky
  454. [shortcut]
  455. from=-3
  456. activation=linear
  457. [avgpool]
  458. [convolutional]
  459. filters=1000
  460. size=1
  461. stride=1
  462. pad=1
  463. activation=linear
  464. [softmax]
  465. groups=1