metafile.yml 27 KB

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  1. Models:
  2. - Name: mask-rcnn_regnetx-3.2GF_fpn_1x_coco
  3. In Collection: Mask R-CNN
  4. Config: configs/regnet/mask-rcnn_regnetx-3.2GF_fpn_1x_coco.py
  5. Metadata:
  6. Training Memory (GB): 5.0
  7. Epochs: 12
  8. Training Data: COCO
  9. Training Techniques:
  10. - SGD with Momentum
  11. - Weight Decay
  12. Training Resources: 8x V100 GPUs
  13. Architecture:
  14. - RegNet
  15. Results:
  16. - Task: Object Detection
  17. Dataset: COCO
  18. Metrics:
  19. box AP: 40.3
  20. - Task: Instance Segmentation
  21. Dataset: COCO
  22. Metrics:
  23. mask AP: 36.6
  24. Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_1x_coco_20200520_163141-2a9d1814.pth
  25. Paper:
  26. URL: https://arxiv.org/abs/2003.13678
  27. Title: 'Designing Network Design Spaces'
  28. README: configs/regnet/README.md
  29. Code:
  30. URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
  31. Version: v2.1.0
  32. - Name: mask-rcnn_regnetx-4GF_fpn_1x_coco
  33. In Collection: Mask R-CNN
  34. Config: configs/regnet/mask-rcnn_regnetx-4GF_fpn_1x_coco.py
  35. Metadata:
  36. Training Memory (GB): 5.5
  37. Epochs: 12
  38. Training Data: COCO
  39. Training Techniques:
  40. - SGD with Momentum
  41. - Weight Decay
  42. Training Resources: 8x V100 GPUs
  43. Architecture:
  44. - RegNet
  45. Results:
  46. - Task: Object Detection
  47. Dataset: COCO
  48. Metrics:
  49. box AP: 41.5
  50. - Task: Instance Segmentation
  51. Dataset: COCO
  52. Metrics:
  53. mask AP: 37.4
  54. Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco/mask_rcnn_regnetx-4GF_fpn_1x_coco_20200517_180217-32e9c92d.pth
  55. Paper:
  56. URL: https://arxiv.org/abs/2003.13678
  57. Title: 'Designing Network Design Spaces'
  58. README: configs/regnet/README.md
  59. Code:
  60. URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
  61. Version: v2.1.0
  62. - Name: mask-rcnn_regnetx-6.4GF_fpn_1x_coco
  63. In Collection: Mask R-CNN
  64. Config: configs/regnet/mask-rcnn_regnetx-6.4GF_fpn_1x_coco.py
  65. Metadata:
  66. Training Memory (GB): 6.1
  67. Epochs: 12
  68. Training Data: COCO
  69. Training Techniques:
  70. - SGD with Momentum
  71. - Weight Decay
  72. Training Resources: 8x V100 GPUs
  73. Architecture:
  74. - RegNet
  75. Results:
  76. - Task: Object Detection
  77. Dataset: COCO
  78. Metrics:
  79. box AP: 41.0
  80. - Task: Instance Segmentation
  81. Dataset: COCO
  82. Metrics:
  83. mask AP: 37.1
  84. Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco/mask_rcnn_regnetx-6.4GF_fpn_1x_coco_20200517_180439-3a7aae83.pth
  85. Paper:
  86. URL: https://arxiv.org/abs/2003.13678
  87. Title: 'Designing Network Design Spaces'
  88. README: configs/regnet/README.md
  89. Code:
  90. URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
  91. Version: v2.1.0
  92. - Name: mask-rcnn_regnetx-8GF_fpn_1x_coco
  93. In Collection: Mask R-CNN
  94. Config: configs/regnet/mask-rcnn_regnetx-8GF_fpn_1x_coco.py
  95. Metadata:
  96. Training Memory (GB): 6.4
  97. Epochs: 12
  98. Training Data: COCO
  99. Training Techniques:
  100. - SGD with Momentum
  101. - Weight Decay
  102. Training Resources: 8x V100 GPUs
  103. Architecture:
  104. - RegNet
  105. Results:
  106. - Task: Object Detection
  107. Dataset: COCO
  108. Metrics:
  109. box AP: 41.7
  110. - Task: Instance Segmentation
  111. Dataset: COCO
  112. Metrics:
  113. mask AP: 37.5
  114. Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco/mask_rcnn_regnetx-8GF_fpn_1x_coco_20200517_180515-09daa87e.pth
  115. Paper:
  116. URL: https://arxiv.org/abs/2003.13678
  117. Title: 'Designing Network Design Spaces'
  118. README: configs/regnet/README.md
  119. Code:
  120. URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
  121. Version: v2.1.0
  122. - Name: mask-rcnn_regnetx-12GF_fpn_1x_coco
  123. In Collection: Mask R-CNN
  124. Config: configs/regnet/mask-rcnn_regnetx-12GF_fpn_1x_coco.py
  125. Metadata:
  126. Training Memory (GB): 7.4
  127. Epochs: 12
  128. Training Data: COCO
  129. Training Techniques:
  130. - SGD with Momentum
  131. - Weight Decay
  132. Training Resources: 8x V100 GPUs
  133. Architecture:
  134. - RegNet
  135. Results:
  136. - Task: Object Detection
  137. Dataset: COCO
  138. Metrics:
  139. box AP: 42.2
  140. - Task: Instance Segmentation
  141. Dataset: COCO
  142. Metrics:
  143. mask AP: 38
  144. Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco/mask_rcnn_regnetx-12GF_fpn_1x_coco_20200517_180552-b538bd8b.pth
  145. Paper:
  146. URL: https://arxiv.org/abs/2003.13678
  147. Title: 'Designing Network Design Spaces'
  148. README: configs/regnet/README.md
  149. Code:
  150. URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
  151. Version: v2.1.0
  152. - Name: mask-rcnn_regnetx-3.2GF-mdconv-c3-c5_fpn_1x_coco
  153. In Collection: Mask R-CNN
  154. Config: configs/regnet/mask-rcnn_regnetx-3.2GF-mdconv-c3-c5_fpn_1x_coco.py
  155. Metadata:
  156. Training Memory (GB): 5.0
  157. Epochs: 12
  158. Training Data: COCO
  159. Training Techniques:
  160. - SGD with Momentum
  161. - Weight Decay
  162. Training Resources: 8x V100 GPUs
  163. Architecture:
  164. - RegNet
  165. Results:
  166. - Task: Object Detection
  167. Dataset: COCO
  168. Metrics:
  169. box AP: 40.3
  170. - Task: Instance Segmentation
  171. Dataset: COCO
  172. Metrics:
  173. mask AP: 36.6
  174. Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco_20200520_172726-75f40794.pth
  175. Paper:
  176. URL: https://arxiv.org/abs/2003.13678
  177. Title: 'Designing Network Design Spaces'
  178. README: configs/regnet/README.md
  179. Code:
  180. URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
  181. Version: v2.1.0
  182. - Name: faster-rcnn_regnetx-3.2GF_fpn_1x_coco
  183. In Collection: Faster R-CNN
  184. Config: configs/regnet/faster-rcnn_regnetx-3.2GF_fpn_1x_coco.py
  185. Metadata:
  186. Training Memory (GB): 4.5
  187. Epochs: 12
  188. Training Data: COCO
  189. Training Techniques:
  190. - SGD with Momentum
  191. - Weight Decay
  192. Training Resources: 8x V100 GPUs
  193. Architecture:
  194. - RegNet
  195. Results:
  196. - Task: Object Detection
  197. Dataset: COCO
  198. Metrics:
  199. box AP: 39.9
  200. Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco/faster_rcnn_regnetx-3.2GF_fpn_1x_coco_20200517_175927-126fd9bf.pth
  201. Paper:
  202. URL: https://arxiv.org/abs/2003.13678
  203. Title: 'Designing Network Design Spaces'
  204. README: configs/regnet/README.md
  205. Code:
  206. URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
  207. Version: v2.1.0
  208. - Name: faster-rcnn_regnetx-3.2GF_fpn_2x_coco
  209. In Collection: Faster R-CNN
  210. Config: configs/regnet/faster-rcnn_regnetx-3.2GF_fpn_2x_coco.py
  211. Metadata:
  212. Training Memory (GB): 4.5
  213. Epochs: 24
  214. Training Data: COCO
  215. Training Techniques:
  216. - SGD with Momentum
  217. - Weight Decay
  218. Training Resources: 8x V100 GPUs
  219. Architecture:
  220. - RegNet
  221. Results:
  222. - Task: Object Detection
  223. Dataset: COCO
  224. Metrics:
  225. box AP: 41.1
  226. Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco/faster_rcnn_regnetx-3.2GF_fpn_2x_coco_20200520_223955-e2081918.pth
  227. Paper:
  228. URL: https://arxiv.org/abs/2003.13678
  229. Title: 'Designing Network Design Spaces'
  230. README: configs/regnet/README.md
  231. Code:
  232. URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
  233. Version: v2.1.0
  234. - Name: retinanet_regnetx-800MF_fpn_1x_coco
  235. In Collection: RetinaNet
  236. Config: configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py
  237. Metadata:
  238. Training Memory (GB): 2.5
  239. Epochs: 12
  240. Training Data: COCO
  241. Training Techniques:
  242. - SGD with Momentum
  243. - Weight Decay
  244. Training Resources: 8x V100 GPUs
  245. Architecture:
  246. - RegNet
  247. Results:
  248. - Task: Object Detection
  249. Dataset: COCO
  250. Metrics:
  251. box AP: 35.6
  252. Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-800MF_fpn_1x_coco/retinanet_regnetx-800MF_fpn_1x_coco_20200517_191403-f6f91d10.pth
  253. Paper:
  254. URL: https://arxiv.org/abs/2003.13678
  255. Title: 'Designing Network Design Spaces'
  256. README: configs/regnet/README.md
  257. Code:
  258. URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
  259. Version: v2.1.0
  260. - Name: retinanet_regnetx-1.6GF_fpn_1x_coco
  261. In Collection: RetinaNet
  262. Config: configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py
  263. Metadata:
  264. Training Memory (GB): 3.3
  265. Epochs: 12
  266. Training Data: COCO
  267. Training Techniques:
  268. - SGD with Momentum
  269. - Weight Decay
  270. Training Resources: 8x V100 GPUs
  271. Architecture:
  272. - RegNet
  273. Results:
  274. - Task: Object Detection
  275. Dataset: COCO
  276. Metrics:
  277. box AP: 37.3
  278. Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco/retinanet_regnetx-1.6GF_fpn_1x_coco_20200517_191403-37009a9d.pth
  279. Paper:
  280. URL: https://arxiv.org/abs/2003.13678
  281. Title: 'Designing Network Design Spaces'
  282. README: configs/regnet/README.md
  283. Code:
  284. URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
  285. Version: v2.1.0
  286. - Name: retinanet_regnetx-3.2GF_fpn_1x_coco
  287. In Collection: RetinaNet
  288. Config: configs/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco.py
  289. Metadata:
  290. Training Memory (GB): 4.2
  291. Epochs: 12
  292. Training Data: COCO
  293. Training Techniques:
  294. - SGD with Momentum
  295. - Weight Decay
  296. Training Resources: 8x V100 GPUs
  297. Architecture:
  298. - RegNet
  299. Results:
  300. - Task: Object Detection
  301. Dataset: COCO
  302. Metrics:
  303. box AP: 39.1
  304. Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco/retinanet_regnetx-3.2GF_fpn_1x_coco_20200520_163141-cb1509e8.pth
  305. Paper:
  306. URL: https://arxiv.org/abs/2003.13678
  307. Title: 'Designing Network Design Spaces'
  308. README: configs/regnet/README.md
  309. Code:
  310. URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
  311. Version: v2.1.0
  312. - Name: faster-rcnn_regnetx-400MF_fpn_ms-3x_coco
  313. In Collection: Faster R-CNN
  314. Config: configs/regnet/faster-rcnn_regnetx-400MF_fpn_ms-3x_coco.py
  315. Metadata:
  316. Training Memory (GB): 2.3
  317. Epochs: 36
  318. Training Data: COCO
  319. Training Techniques:
  320. - SGD with Momentum
  321. - Weight Decay
  322. Training Resources: 8x V100 GPUs
  323. Architecture:
  324. - RegNet
  325. Results:
  326. - Task: Object Detection
  327. Dataset: COCO
  328. Metrics:
  329. box AP: 37.1
  330. Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco_20210526_095112-e1967c37.pth
  331. Paper:
  332. URL: https://arxiv.org/abs/2003.13678
  333. Title: 'Designing Network Design Spaces'
  334. README: configs/regnet/README.md
  335. Code:
  336. URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
  337. Version: v2.1.0
  338. - Name: faster-rcnn_regnetx-800MF_fpn_ms-3x_coco
  339. In Collection: Faster R-CNN
  340. Config: configs/regnet/faster-rcnn_regnetx-800MF_fpn_ms-3x_coco.py
  341. Metadata:
  342. Training Memory (GB): 2.8
  343. Epochs: 36
  344. Training Data: COCO
  345. Training Techniques:
  346. - SGD with Momentum
  347. - Weight Decay
  348. Training Resources: 8x V100 GPUs
  349. Architecture:
  350. - RegNet
  351. Results:
  352. - Task: Object Detection
  353. Dataset: COCO
  354. Metrics:
  355. box AP: 38.8
  356. Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco_20210526_095118-a2c70b20.pth
  357. Paper:
  358. URL: https://arxiv.org/abs/2003.13678
  359. Title: 'Designing Network Design Spaces'
  360. README: configs/regnet/README.md
  361. Code:
  362. URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
  363. Version: v2.1.0
  364. - Name: faster-rcnn_regnetx-1.6GF_fpn_ms-3x_coco
  365. In Collection: Faster R-CNN
  366. Config: configs/regnet/faster-rcnn_regnetx-1.6GF_fpn_ms-3x_coco.py
  367. Metadata:
  368. Training Memory (GB): 3.4
  369. Epochs: 36
  370. Training Data: COCO
  371. Training Techniques:
  372. - SGD with Momentum
  373. - Weight Decay
  374. Training Resources: 8x V100 GPUs
  375. Architecture:
  376. - RegNet
  377. Results:
  378. - Task: Object Detection
  379. Dataset: COCO
  380. Metrics:
  381. box AP: 40.5
  382. Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-1_20210526_095325-94aa46cc.pth
  383. Paper:
  384. URL: https://arxiv.org/abs/2003.13678
  385. Title: 'Designing Network Design Spaces'
  386. README: configs/regnet/README.md
  387. Code:
  388. URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
  389. Version: v2.1.0
  390. - Name: faster-rcnn_regnetx-3.2GF_fpn_ms-3x_coco
  391. In Collection: Faster R-CNN
  392. Config: configs/regnet/faster-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py
  393. Metadata:
  394. Training Memory (GB): 4.4
  395. Epochs: 36
  396. Training Data: COCO
  397. Training Techniques:
  398. - SGD with Momentum
  399. - Weight Decay
  400. Training Resources: 8x V100 GPUs
  401. Architecture:
  402. - RegNet
  403. Results:
  404. - Task: Object Detection
  405. Dataset: COCO
  406. Metrics:
  407. box AP: 42.3
  408. Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-3_20210526_095152-e16a5227.pth
  409. Paper:
  410. URL: https://arxiv.org/abs/2003.13678
  411. Title: 'Designing Network Design Spaces'
  412. README: configs/regnet/README.md
  413. Code:
  414. URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
  415. Version: v2.1.0
  416. - Name: faster-rcnn_regnetx-4GF_fpn_ms-3x_coco
  417. In Collection: Faster R-CNN
  418. Config: configs/regnet/faster-rcnn_regnetx-4GF_fpn_ms-3x_coco.py
  419. Metadata:
  420. Training Memory (GB): 4.9
  421. Epochs: 36
  422. Training Data: COCO
  423. Training Techniques:
  424. - SGD with Momentum
  425. - Weight Decay
  426. Training Resources: 8x V100 GPUs
  427. Architecture:
  428. - RegNet
  429. Results:
  430. - Task: Object Detection
  431. Dataset: COCO
  432. Metrics:
  433. box AP: 42.8
  434. Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco_20210526_095201-65eaf841.pth
  435. Paper:
  436. URL: https://arxiv.org/abs/2003.13678
  437. Title: 'Designing Network Design Spaces'
  438. README: configs/regnet/README.md
  439. Code:
  440. URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
  441. Version: v2.1.0
  442. - Name: mask-rcnn_regnetx-3.2GF_fpn_ms-3x_coco
  443. In Collection: Mask R-CNN
  444. Config: configs/regnet/mask-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py
  445. Metadata:
  446. Training Memory (GB): 5.0
  447. Epochs: 36
  448. Training Data: COCO
  449. Training Techniques:
  450. - SGD with Momentum
  451. - Weight Decay
  452. Training Resources: 8x V100 GPUs
  453. Architecture:
  454. - RegNet
  455. Results:
  456. - Task: Object Detection
  457. Dataset: COCO
  458. Metrics:
  459. box AP: 43.1
  460. - Task: Instance Segmentation
  461. Dataset: COCO
  462. Metrics:
  463. mask AP: 38.7
  464. Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200521_202221-99879813.pth
  465. Paper:
  466. URL: https://arxiv.org/abs/2003.13678
  467. Title: 'Designing Network Design Spaces'
  468. README: configs/regnet/README.md
  469. Code:
  470. URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
  471. Version: v2.1.0
  472. - Name: mask-rcnn_regnetx-400MF_fpn_ms-poly-3x_coco
  473. In Collection: Mask R-CNN
  474. Config: configs/regnet/mask-rcnn_regnetx-400MF_fpn_ms-poly-3x_coco.py
  475. Metadata:
  476. Training Memory (GB): 2.5
  477. Epochs: 36
  478. Training Data: COCO
  479. Training Techniques:
  480. - SGD with Momentum
  481. - Weight Decay
  482. Training Resources: 8x V100 GPUs
  483. Architecture:
  484. - RegNet
  485. Results:
  486. - Task: Object Detection
  487. Dataset: COCO
  488. Metrics:
  489. box AP: 37.6
  490. - Task: Instance Segmentation
  491. Dataset: COCO
  492. Metrics:
  493. mask AP: 34.4
  494. Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco_20210601_235443-8aac57a4.pth
  495. Paper:
  496. URL: https://arxiv.org/abs/2003.13678
  497. Title: 'Designing Network Design Spaces'
  498. README: configs/regnet/README.md
  499. Code:
  500. URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
  501. Version: v2.1.0
  502. - Name: mask-rcnn_regnetx-800MF_fpn_ms-poly-3x_coco
  503. In Collection: Mask R-CNN
  504. Config: configs/regnet/mask-rcnn_regnetx-800MF_fpn_ms-poly-3x_coco.py
  505. Metadata:
  506. Training Memory (GB): 2.9
  507. Epochs: 36
  508. Training Data: COCO
  509. Training Techniques:
  510. - SGD with Momentum
  511. - Weight Decay
  512. Training Resources: 8x V100 GPUs
  513. Architecture:
  514. - RegNet
  515. Results:
  516. - Task: Object Detection
  517. Dataset: COCO
  518. Metrics:
  519. box AP: 39.5
  520. - Task: Instance Segmentation
  521. Dataset: COCO
  522. Metrics:
  523. mask AP: 36.1
  524. Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco_20210602_210641-715d51f5.pth
  525. Paper:
  526. URL: https://arxiv.org/abs/2003.13678
  527. Title: 'Designing Network Design Spaces'
  528. README: configs/regnet/README.md
  529. Code:
  530. URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
  531. Version: v2.1.0
  532. - Name: mask-rcnn_regnetx-1.6GF_fpn_ms-poly-3x_coco
  533. In Collection: Mask R-CNN
  534. Config: configs/regnet/mask-rcnn_regnetx-1.6GF_fpn_ms-poly-3x_coco.py
  535. Metadata:
  536. Training Memory (GB): 3.6
  537. Epochs: 36
  538. Training Data: COCO
  539. Training Techniques:
  540. - SGD with Momentum
  541. - Weight Decay
  542. Training Resources: 8x V100 GPUs
  543. Architecture:
  544. - RegNet
  545. Results:
  546. - Task: Object Detection
  547. Dataset: COCO
  548. Metrics:
  549. box AP: 40.9
  550. - Task: Instance Segmentation
  551. Dataset: COCO
  552. Metrics:
  553. mask AP: 37.5
  554. Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-1_20210602_210641-6764cff5.pth
  555. Paper:
  556. URL: https://arxiv.org/abs/2003.13678
  557. Title: 'Designing Network Design Spaces'
  558. README: configs/regnet/README.md
  559. Code:
  560. URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
  561. Version: v2.1.0
  562. - Name: mask-rcnn_regnetx-3.2GF_fpn_ms-3x_coco
  563. In Collection: Mask R-CNN
  564. Config: configs/regnet/mask-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py
  565. Metadata:
  566. Training Memory (GB): 5.0
  567. Epochs: 36
  568. Training Data: COCO
  569. Training Techniques:
  570. - SGD with Momentum
  571. - Weight Decay
  572. Training Resources: 8x V100 GPUs
  573. Architecture:
  574. - RegNet
  575. Results:
  576. - Task: Object Detection
  577. Dataset: COCO
  578. Metrics:
  579. box AP: 43.1
  580. - Task: Instance Segmentation
  581. Dataset: COCO
  582. Metrics:
  583. mask AP: 38.7
  584. Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200521_202221-99879813.pth
  585. Paper:
  586. URL: https://arxiv.org/abs/2003.13678
  587. Title: 'Designing Network Design Spaces'
  588. README: configs/regnet/README.md
  589. Code:
  590. URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
  591. Version: v2.1.0
  592. - Name: mask-rcnn_regnetx-4GF_fpn_ms-poly-3x_coco
  593. In Collection: Mask R-CNN
  594. Config: configs/regnet/mask-rcnn_regnetx-4GF_fpn_ms-poly-3x_coco.py
  595. Metadata:
  596. Training Memory (GB): 5.1
  597. Epochs: 36
  598. Training Data: COCO
  599. Training Techniques:
  600. - SGD with Momentum
  601. - Weight Decay
  602. Training Resources: 8x V100 GPUs
  603. Architecture:
  604. - RegNet
  605. Results:
  606. - Task: Object Detection
  607. Dataset: COCO
  608. Metrics:
  609. box AP: 43.4
  610. - Task: Instance Segmentation
  611. Dataset: COCO
  612. Metrics:
  613. mask AP: 39.2
  614. Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco_20210602_032621-00f0331c.pth
  615. Paper:
  616. URL: https://arxiv.org/abs/2003.13678
  617. Title: 'Designing Network Design Spaces'
  618. README: configs/regnet/README.md
  619. Code:
  620. URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
  621. Version: v2.1.0
  622. - Name: cascade-mask-rcnn_regnetx-400MF_fpn_ms-3x_coco
  623. In Collection: Cascade R-CNN
  624. Config: configs/regnet/cascade-mask-rcnn_regnetx-400MF_fpn_ms-3x_coco.py
  625. Metadata:
  626. Training Memory (GB): 4.3
  627. Epochs: 36
  628. Training Data: COCO
  629. Training Techniques:
  630. - SGD with Momentum
  631. - Weight Decay
  632. Training Resources: 8x V100 GPUs
  633. Architecture:
  634. - RegNet
  635. Results:
  636. - Task: Object Detection
  637. Dataset: COCO
  638. Metrics:
  639. box AP: 41.6
  640. - Task: Instance Segmentation
  641. Dataset: COCO
  642. Metrics:
  643. mask AP: 36.4
  644. Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco_20210715_211619-5142f449.pth
  645. Paper:
  646. URL: https://arxiv.org/abs/2003.13678
  647. Title: 'Designing Network Design Spaces'
  648. README: configs/regnet/README.md
  649. Code:
  650. URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
  651. Version: v2.1.0
  652. - Name: cascade-mask-rcnn_regnetx-800MF_fpn_ms-3x_coco
  653. In Collection: Cascade R-CNN
  654. Config: configs/regnet/cascade-mask-rcnn_regnetx-800MF_fpn_ms-3x_coco.py
  655. Metadata:
  656. Training Memory (GB): 4.8
  657. Epochs: 36
  658. Training Data: COCO
  659. Training Techniques:
  660. - SGD with Momentum
  661. - Weight Decay
  662. Training Resources: 8x V100 GPUs
  663. Architecture:
  664. - RegNet
  665. Results:
  666. - Task: Object Detection
  667. Dataset: COCO
  668. Metrics:
  669. box AP: 42.8
  670. - Task: Instance Segmentation
  671. Dataset: COCO
  672. Metrics:
  673. mask AP: 37.6
  674. Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco_20210715_211616-dcbd13f4.pth
  675. Paper:
  676. URL: https://arxiv.org/abs/2003.13678
  677. Title: 'Designing Network Design Spaces'
  678. README: configs/regnet/README.md
  679. Code:
  680. URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
  681. Version: v2.1.0
  682. - Name: cascade-mask-rcnn_regnetx-1.6GF_fpn_ms-3x_coco
  683. In Collection: Cascade R-CNN
  684. Config: configs/regnet/cascade-mask-rcnn_regnetx-1.6GF_fpn_ms-3x_coco.py
  685. Metadata:
  686. Training Memory (GB): 5.4
  687. Epochs: 36
  688. Training Data: COCO
  689. Training Techniques:
  690. - SGD with Momentum
  691. - Weight Decay
  692. Training Resources: 8x V100 GPUs
  693. Architecture:
  694. - RegNet
  695. Results:
  696. - Task: Object Detection
  697. Dataset: COCO
  698. Metrics:
  699. box AP: 44.5
  700. - Task: Instance Segmentation
  701. Dataset: COCO
  702. Metrics:
  703. mask AP: 39.0
  704. Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-1_20210715_211616-75f29a61.pth
  705. Paper:
  706. URL: https://arxiv.org/abs/2003.13678
  707. Title: 'Designing Network Design Spaces'
  708. README: configs/regnet/README.md
  709. Code:
  710. URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
  711. Version: v2.1.0
  712. - Name: cascade-mask-rcnn_regnetx-3.2GF_fpn_ms-3x_coco
  713. In Collection: Cascade R-CNN
  714. Config: configs/regnet/cascade-mask-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py
  715. Metadata:
  716. Training Memory (GB): 6.4
  717. Epochs: 36
  718. Training Data: COCO
  719. Training Techniques:
  720. - SGD with Momentum
  721. - Weight Decay
  722. Training Resources: 8x V100 GPUs
  723. Architecture:
  724. - RegNet
  725. Results:
  726. - Task: Object Detection
  727. Dataset: COCO
  728. Metrics:
  729. box AP: 45.8
  730. - Task: Instance Segmentation
  731. Dataset: COCO
  732. Metrics:
  733. mask AP: 40.0
  734. Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-3_20210715_211616-b9c2c58b.pth
  735. Paper:
  736. URL: https://arxiv.org/abs/2003.13678
  737. Title: 'Designing Network Design Spaces'
  738. README: configs/regnet/README.md
  739. Code:
  740. URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
  741. Version: v2.1.0
  742. - Name: cascade-mask-rcnn_regnetx-4GF_fpn_ms-3x_coco
  743. In Collection: Cascade R-CNN
  744. Config: configs/regnet/cascade-mask-rcnn_regnetx-4GF_fpn_ms-3x_coco.py
  745. Metadata:
  746. Training Memory (GB): 6.9
  747. Epochs: 36
  748. Training Data: COCO
  749. Training Techniques:
  750. - SGD with Momentum
  751. - Weight Decay
  752. Training Resources: 8x V100 GPUs
  753. Architecture:
  754. - RegNet
  755. Results:
  756. - Task: Object Detection
  757. Dataset: COCO
  758. Metrics:
  759. box AP: 45.8
  760. - Task: Instance Segmentation
  761. Dataset: COCO
  762. Metrics:
  763. mask AP: 40.0
  764. Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco_20210715_212034-cbb1be4c.pth
  765. Paper:
  766. URL: https://arxiv.org/abs/2003.13678
  767. Title: 'Designing Network Design Spaces'
  768. README: configs/regnet/README.md
  769. Code:
  770. URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
  771. Version: v2.1.0