metafile.yml 14 KB

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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_fp16_1x_coco
  65. In Collection: Mask R-CNN
  66. Config: configs/mask_rcnn/mask-rcnn_r50_fpn_amp-1x_coco.py
  67. Metadata:
  68. Training Memory (GB): 3.6
  69. Training Techniques:
  70. - SGD with Momentum
  71. - Weight Decay
  72. - Mixed Precision Training
  73. inference time (ms/im):
  74. - value: 41.49
  75. hardware: V100
  76. backend: PyTorch
  77. batch size: 1
  78. mode: FP16
  79. resolution: (800, 1333)
  80. Epochs: 12
  81. Results:
  82. - Task: Object Detection
  83. Dataset: COCO
  84. Metrics:
  85. box AP: 38.1
  86. - Task: Instance Segmentation
  87. Dataset: COCO
  88. Metrics:
  89. mask AP: 34.7
  90. Weights: https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_1x_coco/mask_rcnn_r50_fpn_fp16_1x_coco_20200205-59faf7e4.pth
  91. - Name: mask-rcnn_r50_fpn_2x_coco
  92. In Collection: Mask R-CNN
  93. Config: configs/mask_rcnn/mask-rcnn_r50_fpn_2x_coco.py
  94. Metadata:
  95. Training Memory (GB): 4.4
  96. inference time (ms/im):
  97. - value: 62.11
  98. hardware: V100
  99. backend: PyTorch
  100. batch size: 1
  101. mode: FP32
  102. resolution: (800, 1333)
  103. Epochs: 24
  104. Results:
  105. - Task: Object Detection
  106. Dataset: COCO
  107. Metrics:
  108. box AP: 39.2
  109. - Task: Instance Segmentation
  110. Dataset: COCO
  111. Metrics:
  112. mask AP: 35.4
  113. 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
  114. - Name: mask-rcnn_r101-caffe_fpn_1x_coco
  115. In Collection: Mask R-CNN
  116. Config: configs/mask_rcnn/mask-rcnn_r101-caffe_fpn_1x_coco.py
  117. Metadata:
  118. Epochs: 12
  119. Results:
  120. - Task: Object Detection
  121. Dataset: COCO
  122. Metrics:
  123. box AP: 40.4
  124. - Task: Instance Segmentation
  125. Dataset: COCO
  126. Metrics:
  127. mask AP: 36.4
  128. 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
  129. - Name: mask-rcnn_r101_fpn_1x_coco
  130. In Collection: Mask R-CNN
  131. Config: configs/mask_rcnn/mask-rcnn_r101_fpn_1x_coco.py
  132. Metadata:
  133. Training Memory (GB): 6.4
  134. inference time (ms/im):
  135. - value: 74.07
  136. hardware: V100
  137. backend: PyTorch
  138. batch size: 1
  139. mode: FP32
  140. resolution: (800, 1333)
  141. Epochs: 12
  142. Results:
  143. - Task: Object Detection
  144. Dataset: COCO
  145. Metrics:
  146. box AP: 40.0
  147. - Task: Instance Segmentation
  148. Dataset: COCO
  149. Metrics:
  150. mask AP: 36.1
  151. 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
  152. - Name: mask-rcnn_r101_fpn_2x_coco
  153. In Collection: Mask R-CNN
  154. Config: configs/mask_rcnn/mask-rcnn_r101_fpn_2x_coco.py
  155. Metadata:
  156. Training Memory (GB): 6.4
  157. inference time (ms/im):
  158. - value: 74.07
  159. hardware: V100
  160. backend: PyTorch
  161. batch size: 1
  162. mode: FP32
  163. resolution: (800, 1333)
  164. Epochs: 24
  165. Results:
  166. - Task: Object Detection
  167. Dataset: COCO
  168. Metrics:
  169. box AP: 40.8
  170. - Task: Instance Segmentation
  171. Dataset: COCO
  172. Metrics:
  173. mask AP: 36.6
  174. 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
  175. - Name: mask-rcnn_x101-32x4d_fpn_1x_coco
  176. In Collection: Mask R-CNN
  177. Config: configs/mask_rcnn/mask-rcnn_x101-32x4d_fpn_1x_coco.py
  178. Metadata:
  179. Training Memory (GB): 7.6
  180. inference time (ms/im):
  181. - value: 88.5
  182. hardware: V100
  183. backend: PyTorch
  184. batch size: 1
  185. mode: FP32
  186. resolution: (800, 1333)
  187. Epochs: 12
  188. Results:
  189. - Task: Object Detection
  190. Dataset: COCO
  191. Metrics:
  192. box AP: 41.9
  193. - Task: Instance Segmentation
  194. Dataset: COCO
  195. Metrics:
  196. mask AP: 37.5
  197. 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
  198. - Name: mask-rcnn_x101-32x4d_fpn_2x_coco
  199. In Collection: Mask R-CNN
  200. Config: configs/mask_rcnn/mask-rcnn_x101-32x4d_fpn_2x_coco.py
  201. Metadata:
  202. Training Memory (GB): 7.6
  203. inference time (ms/im):
  204. - value: 88.5
  205. hardware: V100
  206. backend: PyTorch
  207. batch size: 1
  208. mode: FP32
  209. resolution: (800, 1333)
  210. Epochs: 24
  211. Results:
  212. - Task: Object Detection
  213. Dataset: COCO
  214. Metrics:
  215. box AP: 42.2
  216. - Task: Instance Segmentation
  217. Dataset: COCO
  218. Metrics:
  219. mask AP: 37.8
  220. 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
  221. - Name: mask-rcnn_x101-64x4d_fpn_1x_coco
  222. In Collection: Mask R-CNN
  223. Config: configs/mask_rcnn/mask-rcnn_x101-64x4d_fpn_1x_coco.py
  224. Metadata:
  225. Training Memory (GB): 10.7
  226. inference time (ms/im):
  227. - value: 125
  228. hardware: V100
  229. backend: PyTorch
  230. batch size: 1
  231. mode: FP32
  232. resolution: (800, 1333)
  233. Epochs: 12
  234. Results:
  235. - Task: Object Detection
  236. Dataset: COCO
  237. Metrics:
  238. box AP: 42.8
  239. - Task: Instance Segmentation
  240. Dataset: COCO
  241. Metrics:
  242. mask AP: 38.4
  243. 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
  244. - Name: mask-rcnn_x101-64x4d_fpn_2x_coco
  245. In Collection: Mask R-CNN
  246. Config: configs/mask_rcnn/mask-rcnn_x101-64x4d_fpn_2x_coco.py
  247. Metadata:
  248. Training Memory (GB): 10.7
  249. inference time (ms/im):
  250. - value: 125
  251. hardware: V100
  252. backend: PyTorch
  253. batch size: 1
  254. mode: FP32
  255. resolution: (800, 1333)
  256. Epochs: 24
  257. Results:
  258. - Task: Object Detection
  259. Dataset: COCO
  260. Metrics:
  261. box AP: 42.7
  262. - Task: Instance Segmentation
  263. Dataset: COCO
  264. Metrics:
  265. mask AP: 38.1
  266. 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
  267. - Name: mask-rcnn_x101-32x8d_fpn_1x_coco
  268. In Collection: Mask R-CNN
  269. Config: configs/mask_rcnn/mask-rcnn_x101-32x8d_fpn_1x_coco.py
  270. Metadata:
  271. Training Memory (GB): 10.6
  272. Epochs: 12
  273. Results:
  274. - Task: Object Detection
  275. Dataset: COCO
  276. Metrics:
  277. box AP: 42.8
  278. - Task: Instance Segmentation
  279. Dataset: COCO
  280. Metrics:
  281. mask AP: 38.3
  282. Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x8d_fpn_1x_coco/mask_rcnn_x101_32x8d_fpn_1x_coco_20220630_173841-0aaf329e.pth
  283. - Name: mask-rcnn_r50-caffe_fpn_ms-poly-2x_coco
  284. In Collection: Mask R-CNN
  285. Config: configs/mask_rcnn/mask-rcnn_r50-caffe_fpn_ms-poly-2x_coco.py
  286. Metadata:
  287. Training Memory (GB): 4.3
  288. Epochs: 24
  289. Results:
  290. - Task: Object Detection
  291. Dataset: COCO
  292. Metrics:
  293. box AP: 40.3
  294. - Task: Instance Segmentation
  295. Dataset: COCO
  296. Metrics:
  297. mask AP: 36.5
  298. 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
  299. - Name: mask-rcnn_r50-caffe_fpn_ms-poly-3x_coco
  300. In Collection: Mask R-CNN
  301. Config: configs/mask_rcnn/mask-rcnn_r50-caffe_fpn_ms-poly-3x_coco.py
  302. Metadata:
  303. Training Memory (GB): 4.3
  304. Epochs: 36
  305. Results:
  306. - Task: Object Detection
  307. Dataset: COCO
  308. Metrics:
  309. box AP: 40.8
  310. - Task: Instance Segmentation
  311. Dataset: COCO
  312. Metrics:
  313. mask AP: 37.0
  314. 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
  315. - Name: mask-rcnn_r50_fpn_mstrain-poly_3x_coco
  316. In Collection: Mask R-CNN
  317. Config: configs/mask_rcnn/mask-rcnn_r50_fpn_ms-poly-3x_coco.py
  318. Metadata:
  319. Training Memory (GB): 4.1
  320. Epochs: 36
  321. Results:
  322. - Task: Object Detection
  323. Dataset: COCO
  324. Metrics:
  325. box AP: 40.9
  326. - Task: Instance Segmentation
  327. Dataset: COCO
  328. Metrics:
  329. mask AP: 37.1
  330. 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
  331. - Name: mask-rcnn_r101_fpn_ms-poly-3x_coco
  332. In Collection: Mask R-CNN
  333. Config: configs/mask_rcnn/mask-rcnn_r101_fpn_ms-poly-3x_coco.py
  334. Metadata:
  335. Training Memory (GB): 6.1
  336. Epochs: 36
  337. Results:
  338. - Task: Object Detection
  339. Dataset: COCO
  340. Metrics:
  341. box AP: 42.7
  342. - Task: Instance Segmentation
  343. Dataset: COCO
  344. Metrics:
  345. mask AP: 38.5
  346. 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
  347. - Name: mask-rcnn_r101-caffe_fpn_ms-poly-3x_coco
  348. In Collection: Mask R-CNN
  349. Config: configs/mask_rcnn/mask-rcnn_r101-caffe_fpn_ms-poly-3x_coco.py
  350. Metadata:
  351. Training Memory (GB): 5.9
  352. Epochs: 36
  353. Results:
  354. - Task: Object Detection
  355. Dataset: COCO
  356. Metrics:
  357. box AP: 42.9
  358. - Task: Instance Segmentation
  359. Dataset: COCO
  360. Metrics:
  361. mask AP: 38.5
  362. 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
  363. - Name: mask-rcnn_x101-32x4d_fpn_ms-poly-3x_coco
  364. In Collection: Mask R-CNN
  365. Config: configs/mask_rcnn/mask-rcnn_x101-32x4d_fpn_ms-poly-3x_coco.py
  366. Metadata:
  367. Training Memory (GB): 7.3
  368. Epochs: 36
  369. Results:
  370. - Task: Object Detection
  371. Dataset: COCO
  372. Metrics:
  373. box AP: 43.6
  374. - Task: Instance Segmentation
  375. Dataset: COCO
  376. Metrics:
  377. mask AP: 39.0
  378. 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
  379. - Name: mask-rcnn_x101-32x8d_fpn_ms-poly-1x_coco
  380. In Collection: Mask R-CNN
  381. Config: configs/mask_rcnn/mask-rcnn_x101-32x8d_fpn_ms-poly-1x_coco.py
  382. Metadata:
  383. Training Memory (GB): 10.4
  384. Epochs: 12
  385. Results:
  386. - Task: Object Detection
  387. Dataset: COCO
  388. Metrics:
  389. box AP: 43.4
  390. - Task: Instance Segmentation
  391. Dataset: COCO
  392. Metrics:
  393. mask AP: 39.0
  394. Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco/mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco_20220630_170346-b4637974.pth
  395. - Name: mask-rcnn_x101-32x8d_fpn_ms-poly-3x_coco
  396. In Collection: Mask R-CNN
  397. Config: configs/mask_rcnn/mask-rcnn_x101-32x8d_fpn_ms-poly-3x_coco.py
  398. Metadata:
  399. Training Memory (GB): 10.3
  400. Epochs: 36
  401. Results:
  402. - Task: Object Detection
  403. Dataset: COCO
  404. Metrics:
  405. box AP: 44.3
  406. 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
  407. - Name: mask-rcnn_x101-64x4d_fpn_ms-poly_3x_coco
  408. In Collection: Mask R-CNN
  409. Config: configs/mask_rcnn/mask-rcnn_x101-64x4d_fpn_ms-poly_3x_coco.py
  410. Metadata:
  411. Epochs: 36
  412. Training Memory (GB): 10.4
  413. Results:
  414. - Task: Object Detection
  415. Dataset: COCO
  416. Metrics:
  417. box AP: 44.5
  418. - Task: Instance Segmentation
  419. Dataset: COCO
  420. Metrics:
  421. mask AP: 39.7
  422. 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