You can not select more than 25 topics Topics must start with a chinese character,a letter or number, can include dashes ('-') and can be up to 35 characters long.

metafile.yml 18 kB

2 years ago
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266267268269270271272273274275276277278279280281282283284285286287288289290291292293294295296297298299300301302303304305306307308309310311312313314315316317318319320321322323324325326327328329330331332333334335336337338339340341342343344345346347348349350351352353354355356357358359360361362363364365366367368369370371372373374375376377378379380381382383384385386387388389390391392393394395396397398399400401402403404405406407408409410411412413414415416417418419420421422423424425426427428429430431432433434435436437438439440441442443444445446447448449450451452453454455456457458459460461462463464465466467468469470471472473474475476477478479480481482483484485486487488489490491492493494495496497498499500501502503504505506507508509510511512513514515516517518519520521522523524525
  1. Collections:
  2. - Name: Cascade R-CNN
  3. Metadata:
  4. Training Data: COCO
  5. Training Techniques:
  6. - SGD with Momentum
  7. - Weight Decay
  8. Training Resources: 8x V100 GPUs
  9. Architecture:
  10. - Cascade R-CNN
  11. - FPN
  12. - RPN
  13. - ResNet
  14. - RoIAlign
  15. Paper:
  16. URL: http://dx.doi.org/10.1109/tpami.2019.2956516
  17. Title: 'Cascade R-CNN: Delving into High Quality Object Detection'
  18. README: configs/cascade_rcnn/README.md
  19. Code:
  20. URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/cascade_rcnn.py#L6
  21. Version: v2.0.0
  22. Models:
  23. - Name: cascade_rcnn_r50_caffe_fpn_1x_coco
  24. In Collection: Cascade R-CNN
  25. Config: configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py
  26. Metadata:
  27. Training Memory (GB): 4.2
  28. Epochs: 12
  29. Results:
  30. - Task: Object Detection
  31. Dataset: COCO
  32. Metrics:
  33. box AP: 40.4
  34. Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco/cascade_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.404_20200504_174853-b857be87.pth
  35. - Name: cascade_rcnn_r50_fpn_1x_coco
  36. In Collection: Cascade R-CNN
  37. Config: configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py
  38. Metadata:
  39. Training Memory (GB): 4.4
  40. inference time (ms/im):
  41. - value: 62.11
  42. hardware: V100
  43. backend: PyTorch
  44. batch size: 1
  45. mode: FP32
  46. resolution: (800, 1333)
  47. Epochs: 12
  48. Results:
  49. - Task: Object Detection
  50. Dataset: COCO
  51. Metrics:
  52. box AP: 40.3
  53. Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco/cascade_rcnn_r50_fpn_1x_coco_20200316-3dc56deb.pth
  54. - Name: cascade_rcnn_r50_fpn_20e_coco
  55. In Collection: Cascade R-CNN
  56. Config: configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py
  57. Metadata:
  58. Training Memory (GB): 4.4
  59. inference time (ms/im):
  60. - value: 62.11
  61. hardware: V100
  62. backend: PyTorch
  63. batch size: 1
  64. mode: FP32
  65. resolution: (800, 1333)
  66. Epochs: 20
  67. Results:
  68. - Task: Object Detection
  69. Dataset: COCO
  70. Metrics:
  71. box AP: 41.0
  72. Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco/cascade_rcnn_r50_fpn_20e_coco_bbox_mAP-0.41_20200504_175131-e9872a90.pth
  73. - Name: cascade_rcnn_r101_caffe_fpn_1x_coco
  74. In Collection: Cascade R-CNN
  75. Config: configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py
  76. Metadata:
  77. Training Memory (GB): 6.2
  78. Epochs: 12
  79. Results:
  80. - Task: Object Detection
  81. Dataset: COCO
  82. Metrics:
  83. box AP: 42.3
  84. Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco/cascade_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.423_20200504_175649-cab8dbd5.pth
  85. - Name: cascade_rcnn_r101_fpn_1x_coco
  86. In Collection: Cascade R-CNN
  87. Config: configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py
  88. Metadata:
  89. Training Memory (GB): 6.4
  90. inference time (ms/im):
  91. - value: 74.07
  92. hardware: V100
  93. backend: PyTorch
  94. batch size: 1
  95. mode: FP32
  96. resolution: (800, 1333)
  97. Epochs: 12
  98. Results:
  99. - Task: Object Detection
  100. Dataset: COCO
  101. Metrics:
  102. box AP: 42.0
  103. Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco/cascade_rcnn_r101_fpn_1x_coco_20200317-0b6a2fbf.pth
  104. - Name: cascade_rcnn_r101_fpn_20e_coco
  105. In Collection: Cascade R-CNN
  106. Config: configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py
  107. Metadata:
  108. Training Memory (GB): 6.4
  109. inference time (ms/im):
  110. - value: 74.07
  111. hardware: V100
  112. backend: PyTorch
  113. batch size: 1
  114. mode: FP32
  115. resolution: (800, 1333)
  116. Epochs: 20
  117. Results:
  118. - Task: Object Detection
  119. Dataset: COCO
  120. Metrics:
  121. box AP: 42.5
  122. Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco/cascade_rcnn_r101_fpn_20e_coco_bbox_mAP-0.425_20200504_231812-5057dcc5.pth
  123. - Name: cascade_rcnn_x101_32x4d_fpn_1x_coco
  124. In Collection: Cascade R-CNN
  125. Config: configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco.py
  126. Metadata:
  127. Training Memory (GB): 7.6
  128. inference time (ms/im):
  129. - value: 91.74
  130. hardware: V100
  131. backend: PyTorch
  132. batch size: 1
  133. mode: FP32
  134. resolution: (800, 1333)
  135. Epochs: 12
  136. Results:
  137. - Task: Object Detection
  138. Dataset: COCO
  139. Metrics:
  140. box AP: 43.7
  141. Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco/cascade_rcnn_x101_32x4d_fpn_1x_coco_20200316-95c2deb6.pth
  142. - Name: cascade_rcnn_x101_32x4d_fpn_20e_coco
  143. In Collection: Cascade R-CNN
  144. Config: configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco.py
  145. Metadata:
  146. Training Memory (GB): 7.6
  147. Epochs: 20
  148. Results:
  149. - Task: Object Detection
  150. Dataset: COCO
  151. Metrics:
  152. box AP: 43.7
  153. Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco/cascade_rcnn_x101_32x4d_fpn_20e_coco_20200906_134608-9ae0a720.pth
  154. - Name: cascade_rcnn_x101_64x4d_fpn_1x_coco
  155. In Collection: Cascade R-CNN
  156. Config: configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco.py
  157. Metadata:
  158. Training Memory (GB): 10.7
  159. Epochs: 12
  160. Results:
  161. - Task: Object Detection
  162. Dataset: COCO
  163. Metrics:
  164. box AP: 44.7
  165. Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco/cascade_rcnn_x101_64x4d_fpn_1x_coco_20200515_075702-43ce6a30.pth
  166. - Name: cascade_rcnn_x101_64x4d_fpn_20e_coco
  167. In Collection: Cascade R-CNN
  168. Config: configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco.py
  169. Metadata:
  170. Training Memory (GB): 10.7
  171. Epochs: 20
  172. Results:
  173. - Task: Object Detection
  174. Dataset: COCO
  175. Metrics:
  176. box AP: 44.5
  177. Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco/cascade_rcnn_x101_64x4d_fpn_20e_coco_20200509_224357-051557b1.pth
  178. - Name: cascade_mask_rcnn_r50_caffe_fpn_1x_coco
  179. In Collection: Cascade R-CNN
  180. Config: configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py
  181. Metadata:
  182. Training Memory (GB): 5.9
  183. Epochs: 12
  184. Results:
  185. - Task: Object Detection
  186. Dataset: COCO
  187. Metrics:
  188. box AP: 41.2
  189. - Task: Instance Segmentation
  190. Dataset: COCO
  191. Metrics:
  192. mask AP: 36.0
  193. Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco/cascade_mask_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.412__segm_mAP-0.36_20200504_174659-5004b251.pth
  194. - Name: cascade_mask_rcnn_r50_fpn_1x_coco
  195. In Collection: Cascade R-CNN
  196. Config: configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py
  197. Metadata:
  198. Training Memory (GB): 6.0
  199. inference time (ms/im):
  200. - value: 89.29
  201. hardware: V100
  202. backend: PyTorch
  203. batch size: 1
  204. mode: FP32
  205. resolution: (800, 1333)
  206. Epochs: 12
  207. Results:
  208. - Task: Object Detection
  209. Dataset: COCO
  210. Metrics:
  211. box AP: 41.2
  212. - Task: Instance Segmentation
  213. Dataset: COCO
  214. Metrics:
  215. mask AP: 35.9
  216. Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco/cascade_mask_rcnn_r50_fpn_1x_coco_20200203-9d4dcb24.pth
  217. - Name: cascade_mask_rcnn_r50_fpn_20e_coco
  218. In Collection: Cascade R-CNN
  219. Config: configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py
  220. Metadata:
  221. Training Memory (GB): 6.0
  222. inference time (ms/im):
  223. - value: 89.29
  224. hardware: V100
  225. backend: PyTorch
  226. batch size: 1
  227. mode: FP32
  228. resolution: (800, 1333)
  229. Epochs: 20
  230. Results:
  231. - Task: Object Detection
  232. Dataset: COCO
  233. Metrics:
  234. box AP: 41.9
  235. - Task: Instance Segmentation
  236. Dataset: COCO
  237. Metrics:
  238. mask AP: 36.5
  239. Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco/cascade_mask_rcnn_r50_fpn_20e_coco_bbox_mAP-0.419__segm_mAP-0.365_20200504_174711-4af8e66e.pth
  240. - Name: cascade_mask_rcnn_r101_caffe_fpn_1x_coco
  241. In Collection: Cascade R-CNN
  242. Config: configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py
  243. Metadata:
  244. Training Memory (GB): 7.8
  245. Epochs: 12
  246. Results:
  247. - Task: Object Detection
  248. Dataset: COCO
  249. Metrics:
  250. box AP: 43.2
  251. - Task: Instance Segmentation
  252. Dataset: COCO
  253. Metrics:
  254. mask AP: 37.6
  255. Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco/cascade_mask_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.432__segm_mAP-0.376_20200504_174813-5c1e9599.pth
  256. - Name: cascade_mask_rcnn_r101_fpn_1x_coco
  257. In Collection: Cascade R-CNN
  258. Config: configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py
  259. Metadata:
  260. Training Memory (GB): 7.9
  261. inference time (ms/im):
  262. - value: 102.04
  263. hardware: V100
  264. backend: PyTorch
  265. batch size: 1
  266. mode: FP32
  267. resolution: (800, 1333)
  268. Epochs: 12
  269. Results:
  270. - Task: Object Detection
  271. Dataset: COCO
  272. Metrics:
  273. box AP: 42.9
  274. - Task: Instance Segmentation
  275. Dataset: COCO
  276. Metrics:
  277. mask AP: 37.3
  278. Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco/cascade_mask_rcnn_r101_fpn_1x_coco_20200203-befdf6ee.pth
  279. - Name: cascade_mask_rcnn_r101_fpn_20e_coco
  280. In Collection: Cascade R-CNN
  281. Config: configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py
  282. Metadata:
  283. Training Memory (GB): 7.9
  284. inference time (ms/im):
  285. - value: 102.04
  286. hardware: V100
  287. backend: PyTorch
  288. batch size: 1
  289. mode: FP32
  290. resolution: (800, 1333)
  291. Epochs: 20
  292. Results:
  293. - Task: Object Detection
  294. Dataset: COCO
  295. Metrics:
  296. box AP: 43.4
  297. - Task: Instance Segmentation
  298. Dataset: COCO
  299. Metrics:
  300. mask AP: 37.8
  301. Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco/cascade_mask_rcnn_r101_fpn_20e_coco_bbox_mAP-0.434__segm_mAP-0.378_20200504_174836-005947da.pth
  302. - Name: cascade_mask_rcnn_x101_32x4d_fpn_1x_coco
  303. In Collection: Cascade R-CNN
  304. Config: configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py
  305. Metadata:
  306. Training Memory (GB): 9.2
  307. inference time (ms/im):
  308. - value: 116.28
  309. hardware: V100
  310. backend: PyTorch
  311. batch size: 1
  312. mode: FP32
  313. resolution: (800, 1333)
  314. Epochs: 12
  315. Results:
  316. - Task: Object Detection
  317. Dataset: COCO
  318. Metrics:
  319. box AP: 44.3
  320. - Task: Instance Segmentation
  321. Dataset: COCO
  322. Metrics:
  323. mask AP: 38.3
  324. Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco_20200201-0f411b1f.pth
  325. - Name: cascade_mask_rcnn_x101_32x4d_fpn_20e_coco
  326. In Collection: Cascade R-CNN
  327. Config: configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py
  328. Metadata:
  329. Training Memory (GB): 9.2
  330. inference time (ms/im):
  331. - value: 116.28
  332. hardware: V100
  333. backend: PyTorch
  334. batch size: 1
  335. mode: FP32
  336. resolution: (800, 1333)
  337. Epochs: 20
  338. Results:
  339. - Task: Object Detection
  340. Dataset: COCO
  341. Metrics:
  342. box AP: 45.0
  343. - Task: Instance Segmentation
  344. Dataset: COCO
  345. Metrics:
  346. mask AP: 39.0
  347. Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco_20200528_083917-ed1f4751.pth
  348. - Name: cascade_mask_rcnn_x101_64x4d_fpn_1x_coco
  349. In Collection: Cascade R-CNN
  350. Config: configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py
  351. Metadata:
  352. Training Memory (GB): 12.2
  353. inference time (ms/im):
  354. - value: 149.25
  355. hardware: V100
  356. backend: PyTorch
  357. batch size: 1
  358. mode: FP32
  359. resolution: (800, 1333)
  360. Epochs: 12
  361. Results:
  362. - Task: Object Detection
  363. Dataset: COCO
  364. Metrics:
  365. box AP: 45.3
  366. - Task: Instance Segmentation
  367. Dataset: COCO
  368. Metrics:
  369. mask AP: 39.2
  370. Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco_20200203-9a2db89d.pth
  371. - Name: cascade_mask_rcnn_x101_64x4d_fpn_20e_coco
  372. In Collection: Cascade R-CNN
  373. Config: configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py
  374. Metadata:
  375. Training Memory (GB): 12.2
  376. Epochs: 20
  377. Results:
  378. - Task: Object Detection
  379. Dataset: COCO
  380. Metrics:
  381. box AP: 45.6
  382. - Task: Instance Segmentation
  383. Dataset: COCO
  384. Metrics:
  385. mask AP: 39.5
  386. Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco_20200512_161033-bdb5126a.pth
  387. - Name: cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco
  388. In Collection: Cascade R-CNN
  389. Config: configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco.py
  390. Metadata:
  391. Training Memory (GB): 5.7
  392. Epochs: 36
  393. Results:
  394. - Task: Object Detection
  395. Dataset: COCO
  396. Metrics:
  397. box AP: 44.0
  398. - Task: Instance Segmentation
  399. Dataset: COCO
  400. Metrics:
  401. mask AP: 38.1
  402. Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210707_002651-6e29b3a6.pth
  403. - Name: cascade_mask_rcnn_r50_fpn_mstrain_3x_coco
  404. In Collection: Cascade R-CNN
  405. Config: configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py
  406. Metadata:
  407. Training Memory (GB): 5.9
  408. Epochs: 36
  409. Results:
  410. - Task: Object Detection
  411. Dataset: COCO
  412. Metrics:
  413. box AP: 44.3
  414. - Task: Instance Segmentation
  415. Dataset: COCO
  416. Metrics:
  417. mask AP: 38.5
  418. Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco_20210628_164719-5bdc3824.pth
  419. - Name: cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco
  420. In Collection: Cascade R-CNN
  421. Config: configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco.py
  422. Metadata:
  423. Training Memory (GB): 7.7
  424. Epochs: 36
  425. Results:
  426. - Task: Object Detection
  427. Dataset: COCO
  428. Metrics:
  429. box AP: 45.4
  430. - Task: Instance Segmentation
  431. Dataset: COCO
  432. Metrics:
  433. mask AP: 39.5
  434. Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco_20210707_002620-a5bd2389.pth
  435. - Name: cascade_mask_rcnn_r101_fpn_mstrain_3x_coco
  436. In Collection: Cascade R-CNN
  437. Config: configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco.py
  438. Metadata:
  439. Training Memory (GB): 7.8
  440. Epochs: 36
  441. Results:
  442. - Task: Object Detection
  443. Dataset: COCO
  444. Metrics:
  445. box AP: 45.5
  446. - Task: Instance Segmentation
  447. Dataset: COCO
  448. Metrics:
  449. mask AP: 39.6
  450. Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco_20210628_165236-51a2d363.pth
  451. - Name: cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco
  452. In Collection: Cascade R-CNN
  453. Config: configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py
  454. Metadata:
  455. Training Memory (GB): 9.0
  456. Epochs: 36
  457. Results:
  458. - Task: Object Detection
  459. Dataset: COCO
  460. Metrics:
  461. box AP: 46.3
  462. - Task: Instance Segmentation
  463. Dataset: COCO
  464. Metrics:
  465. mask AP: 40.1
  466. Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco_20210706_225234-40773067.pth
  467. - Name: cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco
  468. In Collection: Cascade R-CNN
  469. Config: configs/cascade_rcnn/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py
  470. Metadata:
  471. Training Memory (GB): 12.1
  472. Epochs: 36
  473. Results:
  474. - Task: Object Detection
  475. Dataset: COCO
  476. Metrics:
  477. box AP: 46.1
  478. - Task: Instance Segmentation
  479. Dataset: COCO
  480. Metrics:
  481. mask AP: 39.9
  482. Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco_20210719_180640-9ff7e76f.pth
  483. - Name: cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco
  484. In Collection: Cascade R-CNN
  485. Config: configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py
  486. Metadata:
  487. Training Memory (GB): 12.0
  488. Epochs: 36
  489. Results:
  490. - Task: Object Detection
  491. Dataset: COCO
  492. Metrics:
  493. box AP: 46.6
  494. - Task: Instance Segmentation
  495. Dataset: COCO
  496. Metrics:
  497. mask AP: 40.3
  498. Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco_20210719_210311-d3e64ba0.pth

No Description

Contributors (2)