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  1. Collections:
  2. - Name: Mask 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. - Softmax
  11. - RPN
  12. - Convolution
  13. - Dense Connections
  14. - FPN
  15. - ResNet
  16. - RoIAlign
  17. Paper:
  18. URL: https://arxiv.org/abs/1703.06870v3
  19. Title: 'Mask R-CNN'
  20. README: configs/mask_rcnn/README.md
  21. Code:
  22. URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/mask_rcnn.py#L6
  23. Version: v2.0.0
  24. Models:
  25. - Name: mask_rcnn_r50_caffe_fpn_1x_coco
  26. In Collection: Mask R-CNN
  27. Config: configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py
  28. Metadata:
  29. Training Memory (GB): 4.3
  30. Epochs: 12
  31. Results:
  32. - Task: Object Detection
  33. Dataset: COCO
  34. Metrics:
  35. box AP: 38.0
  36. - Task: Instance Segmentation
  37. Dataset: COCO
  38. Metrics:
  39. mask AP: 34.4
  40. Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco/mask_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.38__segm_mAP-0.344_20200504_231812-0ebd1859.pth
  41. - Name: mask_rcnn_r50_fpn_1x_coco
  42. In Collection: Mask R-CNN
  43. Config: configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py
  44. Metadata:
  45. Training Memory (GB): 4.4
  46. inference time (ms/im):
  47. - value: 62.11
  48. hardware: V100
  49. backend: PyTorch
  50. batch size: 1
  51. mode: FP32
  52. resolution: (800, 1333)
  53. Epochs: 12
  54. Results:
  55. - Task: Object Detection
  56. Dataset: COCO
  57. Metrics:
  58. box AP: 38.2
  59. - Task: Instance Segmentation
  60. Dataset: COCO
  61. Metrics:
  62. mask AP: 34.7
  63. Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth
  64. - Name: mask_rcnn_r50_fpn_2x_coco
  65. In Collection: Mask R-CNN
  66. Config: configs/mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py
  67. Metadata:
  68. Training Memory (GB): 4.4
  69. inference time (ms/im):
  70. - value: 62.11
  71. hardware: V100
  72. backend: PyTorch
  73. batch size: 1
  74. mode: FP32
  75. resolution: (800, 1333)
  76. Epochs: 24
  77. Results:
  78. - Task: Object Detection
  79. Dataset: COCO
  80. Metrics:
  81. box AP: 39.2
  82. - Task: Instance Segmentation
  83. Dataset: COCO
  84. Metrics:
  85. mask AP: 35.4
  86. Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_2x_coco/mask_rcnn_r50_fpn_2x_coco_bbox_mAP-0.392__segm_mAP-0.354_20200505_003907-3e542a40.pth
  87. - Name: mask_rcnn_r101_caffe_fpn_1x_coco
  88. In Collection: Mask R-CNN
  89. Config: configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco.py
  90. Metadata:
  91. Epochs: 12
  92. Results:
  93. - Task: Object Detection
  94. Dataset: COCO
  95. Metrics:
  96. box AP: 40.4
  97. - Task: Instance Segmentation
  98. Dataset: COCO
  99. Metrics:
  100. mask AP: 36.4
  101. Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco/mask_rcnn_r101_caffe_fpn_1x_coco_20200601_095758-805e06c1.pth
  102. - Name: mask_rcnn_r101_fpn_1x_coco
  103. In Collection: Mask R-CNN
  104. Config: configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py
  105. Metadata:
  106. Training Memory (GB): 6.4
  107. inference time (ms/im):
  108. - value: 74.07
  109. hardware: V100
  110. backend: PyTorch
  111. batch size: 1
  112. mode: FP32
  113. resolution: (800, 1333)
  114. Epochs: 12
  115. Results:
  116. - Task: Object Detection
  117. Dataset: COCO
  118. Metrics:
  119. box AP: 40.0
  120. - Task: Instance Segmentation
  121. Dataset: COCO
  122. Metrics:
  123. mask AP: 36.1
  124. Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204-1efe0ed5.pth
  125. - Name: mask_rcnn_r101_fpn_2x_coco
  126. In Collection: Mask R-CNN
  127. Config: configs/mask_rcnn/mask_rcnn_r101_fpn_2x_coco.py
  128. Metadata:
  129. Training Memory (GB): 6.4
  130. inference time (ms/im):
  131. - value: 74.07
  132. hardware: V100
  133. backend: PyTorch
  134. batch size: 1
  135. mode: FP32
  136. resolution: (800, 1333)
  137. Epochs: 24
  138. Results:
  139. - Task: Object Detection
  140. Dataset: COCO
  141. Metrics:
  142. box AP: 40.8
  143. - Task: Instance Segmentation
  144. Dataset: COCO
  145. Metrics:
  146. mask AP: 36.6
  147. Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_2x_coco/mask_rcnn_r101_fpn_2x_coco_bbox_mAP-0.408__segm_mAP-0.366_20200505_071027-14b391c7.pth
  148. - Name: mask_rcnn_x101_32x4d_fpn_1x_coco
  149. In Collection: Mask R-CNN
  150. Config: configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py
  151. Metadata:
  152. Training Memory (GB): 7.6
  153. inference time (ms/im):
  154. - value: 88.5
  155. hardware: V100
  156. backend: PyTorch
  157. batch size: 1
  158. mode: FP32
  159. resolution: (800, 1333)
  160. Epochs: 12
  161. Results:
  162. - Task: Object Detection
  163. Dataset: COCO
  164. Metrics:
  165. box AP: 41.9
  166. - Task: Instance Segmentation
  167. Dataset: COCO
  168. Metrics:
  169. mask AP: 37.5
  170. Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco/mask_rcnn_x101_32x4d_fpn_1x_coco_20200205-478d0b67.pth
  171. - Name: mask_rcnn_x101_32x4d_fpn_2x_coco
  172. In Collection: Mask R-CNN
  173. Config: configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py
  174. Metadata:
  175. Training Memory (GB): 7.6
  176. inference time (ms/im):
  177. - value: 88.5
  178. hardware: V100
  179. backend: PyTorch
  180. batch size: 1
  181. mode: FP32
  182. resolution: (800, 1333)
  183. Epochs: 24
  184. Results:
  185. - Task: Object Detection
  186. Dataset: COCO
  187. Metrics:
  188. box AP: 42.2
  189. - Task: Instance Segmentation
  190. Dataset: COCO
  191. Metrics:
  192. mask AP: 37.8
  193. Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco/mask_rcnn_x101_32x4d_fpn_2x_coco_bbox_mAP-0.422__segm_mAP-0.378_20200506_004702-faef898c.pth
  194. - Name: mask_rcnn_x101_64x4d_fpn_1x_coco
  195. In Collection: Mask R-CNN
  196. Config: configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco.py
  197. Metadata:
  198. Training Memory (GB): 10.7
  199. inference time (ms/im):
  200. - value: 125
  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: 42.8
  212. - Task: Instance Segmentation
  213. Dataset: COCO
  214. Metrics:
  215. mask AP: 38.4
  216. Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco/mask_rcnn_x101_64x4d_fpn_1x_coco_20200201-9352eb0d.pth
  217. - Name: mask_rcnn_x101_64x4d_fpn_2x_coco
  218. In Collection: Mask R-CNN
  219. Config: configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco.py
  220. Metadata:
  221. Training Memory (GB): 10.7
  222. inference time (ms/im):
  223. - value: 125
  224. hardware: V100
  225. backend: PyTorch
  226. batch size: 1
  227. mode: FP32
  228. resolution: (800, 1333)
  229. Epochs: 24
  230. Results:
  231. - Task: Object Detection
  232. Dataset: COCO
  233. Metrics:
  234. box AP: 42.7
  235. - Task: Instance Segmentation
  236. Dataset: COCO
  237. Metrics:
  238. mask AP: 38.1
  239. Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco/mask_rcnn_x101_64x4d_fpn_2x_coco_20200509_224208-39d6f70c.pth
  240. - Name: mask_rcnn_x101_32x8d_fpn_1x_coco
  241. In Collection: Mask R-CNN
  242. Config: configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_1x_coco.py
  243. Metadata:
  244. Training Memory (GB): 10.7
  245. inference time (ms/im):
  246. - value: 125
  247. hardware: V100
  248. backend: PyTorch
  249. batch size: 1
  250. mode: FP32
  251. resolution: (800, 1333)
  252. Epochs: 12
  253. Results:
  254. - Task: Object Detection
  255. Dataset: COCO
  256. Metrics:
  257. box AP: 42.8
  258. - Task: Instance Segmentation
  259. Dataset: COCO
  260. Metrics:
  261. mask AP: 38.3
  262. - Name: mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco
  263. In Collection: Mask R-CNN
  264. Config: configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py
  265. Metadata:
  266. Training Memory (GB): 4.3
  267. Epochs: 24
  268. Results:
  269. - Task: Object Detection
  270. Dataset: COCO
  271. Metrics:
  272. box AP: 40.3
  273. - Task: Instance Segmentation
  274. Dataset: COCO
  275. Metrics:
  276. mask AP: 36.5
  277. Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco_bbox_mAP-0.403__segm_mAP-0.365_20200504_231822-a75c98ce.pth
  278. - Name: mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco
  279. In Collection: Mask R-CNN
  280. Config: configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py
  281. Metadata:
  282. Training Memory (GB): 4.3
  283. Epochs: 36
  284. Results:
  285. - Task: Object Detection
  286. Dataset: COCO
  287. Metrics:
  288. box AP: 40.8
  289. - Task: Instance Segmentation
  290. Dataset: COCO
  291. Metrics:
  292. mask AP: 37.0
  293. Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth
  294. - Name: mask_rcnn_r50_fpn_mstrain-poly_3x_coco
  295. In Collection: Mask R-CNN
  296. Config: configs/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco.py
  297. Metadata:
  298. Training Memory (GB): 4.1
  299. Epochs: 36
  300. Results:
  301. - Task: Object Detection
  302. Dataset: COCO
  303. Metrics:
  304. box AP: 40.9
  305. - Task: Instance Segmentation
  306. Dataset: COCO
  307. Metrics:
  308. mask AP: 37.1
  309. Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_fpn_mstrain-poly_3x_coco_20210524_201154-21b550bb.pth
  310. - Name: mask_rcnn_r101_fpn_mstrain-poly_3x_coco
  311. In Collection: Mask R-CNN
  312. Config: configs/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco.py
  313. Metadata:
  314. Training Memory (GB): 6.1
  315. Epochs: 36
  316. Results:
  317. - Task: Object Detection
  318. Dataset: COCO
  319. Metrics:
  320. box AP: 42.7
  321. - Task: Instance Segmentation
  322. Dataset: COCO
  323. Metrics:
  324. mask AP: 38.5
  325. Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco/mask_rcnn_r101_fpn_mstrain-poly_3x_coco_20210524_200244-5675c317.pth
  326. - Name: mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco
  327. In Collection: Mask R-CNN
  328. Config: configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco.py
  329. Metadata:
  330. Training Memory (GB): 5.9
  331. Epochs: 36
  332. Results:
  333. - Task: Object Detection
  334. Dataset: COCO
  335. Metrics:
  336. box AP: 42.9
  337. - Task: Instance Segmentation
  338. Dataset: COCO
  339. Metrics:
  340. mask AP: 38.5
  341. Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco_20210526_132339-3c33ce02.pth
  342. - Name: mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco
  343. In Collection: Mask R-CNN
  344. Config: configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco.py
  345. Metadata:
  346. Training Memory (GB): 7.3
  347. Epochs: 36
  348. Results:
  349. - Task: Object Detection
  350. Dataset: COCO
  351. Metrics:
  352. box AP: 43.6
  353. - Task: Instance Segmentation
  354. Dataset: COCO
  355. Metrics:
  356. mask AP: 39.0
  357. Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco_20210524_201410-abcd7859.pth
  358. - Name: mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco
  359. In Collection: Mask R-CNN
  360. Config: configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco.py
  361. Metadata:
  362. Epochs: 12
  363. Results:
  364. - Task: Object Detection
  365. Dataset: COCO
  366. Metrics:
  367. box AP: 43.6
  368. - Task: Instance Segmentation
  369. Dataset: COCO
  370. Metrics:
  371. mask AP: 39.0
  372. - Name: mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco
  373. In Collection: Mask R-CNN
  374. Config: configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco
  375. Metadata:
  376. Training Memory (GB): 10.3
  377. Epochs: 36
  378. Results:
  379. - Task: Object Detection
  380. Dataset: COCO
  381. Metrics:
  382. box AP: 44.3
  383. Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco_20210607_161042-8bd2c639.pth
  384. - Name: mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco
  385. In Collection: Mask R-CNN
  386. Config: configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco.py
  387. Metadata:
  388. Epochs: 36
  389. Training Memory (GB): 10.4
  390. Results:
  391. - Task: Object Detection
  392. Dataset: COCO
  393. Metrics:
  394. box AP: 44.5
  395. - Task: Instance Segmentation
  396. Dataset: COCO
  397. Metrics:
  398. mask AP: 39.7
  399. Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco_20210526_120447-c376f129.pth

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Contributors (3)