metafile.yml 16 KB

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  1. Collections:
  2. - Name: Faster 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. - FPN
  11. - RPN
  12. - ResNet
  13. - RoIPool
  14. Paper:
  15. URL: https://arxiv.org/abs/1506.01497
  16. Title: "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks"
  17. README: configs/faster_rcnn/README.md
  18. Code:
  19. URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/faster_rcnn.py#L6
  20. Version: v2.0.0
  21. Models:
  22. - Name: faster-rcnn_r50-caffe-c4_1x_coco
  23. In Collection: Faster R-CNN
  24. Config: configs/faster_rcnn/faster-rcnn_r50-caffe_c4-1x_coco.py
  25. Metadata:
  26. Epochs: 12
  27. Results:
  28. - Task: Object Detection
  29. Dataset: COCO
  30. Metrics:
  31. box AP: 35.6
  32. Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_c4_1x_coco/faster_rcnn_r50_caffe_c4_1x_coco_20220316_150152-3f885b85.pth
  33. - Name: faster-rcnn_r50-caffe-c4_mstrain_1x_coco
  34. In Collection: Faster R-CNN
  35. Config: configs/faster_rcnn/faster-rcnn_r50-caffe-c4_ms-1x_coco.py
  36. Metadata:
  37. Epochs: 12
  38. Results:
  39. - Task: Object Detection
  40. Dataset: COCO
  41. Metrics:
  42. box AP: 35.9
  43. Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_c4_mstrain_1x_coco/faster_rcnn_r50_caffe_c4_mstrain_1x_coco_20220316_150527-db276fed.pth
  44. - Name: faster-rcnn_r50-caffe-dc5_1x_coco
  45. In Collection: Faster R-CNN
  46. Config: configs/faster_rcnn/faster-rcnn_r50-caffe-dc5_1x_coco.py
  47. Metadata:
  48. Epochs: 12
  49. Results:
  50. - Task: Object Detection
  51. Dataset: COCO
  52. Metrics:
  53. box AP: 37.2
  54. Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco/faster_rcnn_r50_caffe_dc5_1x_coco_20201030_151909-531f0f43.pth
  55. - Name: faster-rcnn_r50-caffe_fpn_1x_coco
  56. In Collection: Faster R-CNN
  57. Config: configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_1x_coco.py
  58. Metadata:
  59. Training Memory (GB): 3.8
  60. Epochs: 12
  61. Results:
  62. - Task: Object Detection
  63. Dataset: COCO
  64. Metrics:
  65. box AP: 37.8
  66. Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco/faster_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.378_20200504_180032-c5925ee5.pth
  67. - Name: faster-rcnn_r50_fpn_1x_coco
  68. In Collection: Faster R-CNN
  69. Config: configs/faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py
  70. Metadata:
  71. Training Memory (GB): 4.0
  72. inference time (ms/im):
  73. - value: 46.73
  74. hardware: V100
  75. backend: PyTorch
  76. batch size: 1
  77. mode: FP32
  78. resolution: (800, 1333)
  79. Epochs: 12
  80. Results:
  81. - Task: Object Detection
  82. Dataset: COCO
  83. Metrics:
  84. box AP: 37.4
  85. Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth
  86. - Name: faster-rcnn_r50_fpn_fp16_1x_coco
  87. In Collection: Faster R-CNN
  88. Config: configs/faster_rcnn/faster-rcnn_r50_fpn_amp-1x_coco.py
  89. Metadata:
  90. Training Memory (GB): 3.4
  91. Training Techniques:
  92. - SGD with Momentum
  93. - Weight Decay
  94. - Mixed Precision Training
  95. inference time (ms/im):
  96. - value: 34.72
  97. hardware: V100
  98. backend: PyTorch
  99. batch size: 1
  100. mode: FP16
  101. resolution: (800, 1333)
  102. Epochs: 12
  103. Results:
  104. - Task: Object Detection
  105. Dataset: COCO
  106. Metrics:
  107. box AP: 37.5
  108. Weights: https://download.openmmlab.com/mmdetection/v2.0/fp16/faster_rcnn_r50_fpn_fp16_1x_coco/faster_rcnn_r50_fpn_fp16_1x_coco_20200204-d4dc1471.pth
  109. - Name: faster-rcnn_r50_fpn_2x_coco
  110. In Collection: Faster R-CNN
  111. Config: configs/faster_rcnn/faster-rcnn_r50_fpn_2x_coco.py
  112. Metadata:
  113. Training Memory (GB): 4.0
  114. inference time (ms/im):
  115. - value: 46.73
  116. hardware: V100
  117. backend: PyTorch
  118. batch size: 1
  119. mode: FP32
  120. resolution: (800, 1333)
  121. Epochs: 24
  122. Results:
  123. - Task: Object Detection
  124. Dataset: COCO
  125. Metrics:
  126. box AP: 38.4
  127. Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth
  128. - Name: faster-rcnn_r101-caffe_fpn_1x_coco
  129. In Collection: Faster R-CNN
  130. Config: configs/faster_rcnn/faster-rcnn_r101-caffe_fpn_1x_coco.py
  131. Metadata:
  132. Training Memory (GB): 5.7
  133. Epochs: 12
  134. Results:
  135. - Task: Object Detection
  136. Dataset: COCO
  137. Metrics:
  138. box AP: 39.8
  139. Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco/faster_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.398_20200504_180057-b269e9dd.pth
  140. - Name: faster-rcnn_r101_fpn_1x_coco
  141. In Collection: Faster R-CNN
  142. Config: configs/faster_rcnn/faster-rcnn_r101_fpn_1x_coco.py
  143. Metadata:
  144. Training Memory (GB): 6.0
  145. inference time (ms/im):
  146. - value: 64.1
  147. hardware: V100
  148. backend: PyTorch
  149. batch size: 1
  150. mode: FP32
  151. resolution: (800, 1333)
  152. Epochs: 12
  153. Results:
  154. - Task: Object Detection
  155. Dataset: COCO
  156. Metrics:
  157. box AP: 39.4
  158. Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_1x_coco/faster_rcnn_r101_fpn_1x_coco_20200130-f513f705.pth
  159. - Name: faster-rcnn_r101_fpn_2x_coco
  160. In Collection: Faster R-CNN
  161. Config: configs/faster_rcnn/faster-rcnn_r101_fpn_2x_coco.py
  162. Metadata:
  163. Training Memory (GB): 6.0
  164. inference time (ms/im):
  165. - value: 64.1
  166. hardware: V100
  167. backend: PyTorch
  168. batch size: 1
  169. mode: FP32
  170. resolution: (800, 1333)
  171. Epochs: 24
  172. Results:
  173. - Task: Object Detection
  174. Dataset: COCO
  175. Metrics:
  176. box AP: 39.8
  177. Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_2x_coco/faster_rcnn_r101_fpn_2x_coco_bbox_mAP-0.398_20200504_210455-1d2dac9c.pth
  178. - Name: faster-rcnn_x101-32x4d_fpn_1x_coco
  179. In Collection: Faster R-CNN
  180. Config: configs/faster_rcnn/faster-rcnn_x101-32x4d_fpn_1x_coco.py
  181. Metadata:
  182. Training Memory (GB): 7.2
  183. inference time (ms/im):
  184. - value: 72.46
  185. hardware: V100
  186. backend: PyTorch
  187. batch size: 1
  188. mode: FP32
  189. resolution: (800, 1333)
  190. Epochs: 12
  191. Results:
  192. - Task: Object Detection
  193. Dataset: COCO
  194. Metrics:
  195. box AP: 41.2
  196. Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco/faster_rcnn_x101_32x4d_fpn_1x_coco_20200203-cff10310.pth
  197. - Name: faster-rcnn_x101-32x4d_fpn_2x_coco
  198. In Collection: Faster R-CNN
  199. Config: configs/faster_rcnn/faster-rcnn_x101-32x4d_fpn_2x_coco.py
  200. Metadata:
  201. Training Memory (GB): 7.2
  202. inference time (ms/im):
  203. - value: 72.46
  204. hardware: V100
  205. backend: PyTorch
  206. batch size: 1
  207. mode: FP32
  208. resolution: (800, 1333)
  209. Epochs: 24
  210. Results:
  211. - Task: Object Detection
  212. Dataset: COCO
  213. Metrics:
  214. box AP: 41.2
  215. Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco/faster_rcnn_x101_32x4d_fpn_2x_coco_bbox_mAP-0.412_20200506_041400-64a12c0b.pth
  216. - Name: faster-rcnn_x101-64x4d_fpn_1x_coco
  217. In Collection: Faster R-CNN
  218. Config: configs/faster_rcnn/faster-rcnn_x101-64x4d_fpn_1x_coco.py
  219. Metadata:
  220. Training Memory (GB): 10.3
  221. inference time (ms/im):
  222. - value: 106.38
  223. hardware: V100
  224. backend: PyTorch
  225. batch size: 1
  226. mode: FP32
  227. resolution: (800, 1333)
  228. Epochs: 12
  229. Results:
  230. - Task: Object Detection
  231. Dataset: COCO
  232. Metrics:
  233. box AP: 42.1
  234. Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco/faster_rcnn_x101_64x4d_fpn_1x_coco_20200204-833ee192.pth
  235. - Name: faster-rcnn_x101-64x4d_fpn_2x_coco
  236. In Collection: Faster R-CNN
  237. Config: configs/faster_rcnn/faster-rcnn_x101-64x4d_fpn_2x_coco.py
  238. Metadata:
  239. Training Memory (GB): 10.3
  240. inference time (ms/im):
  241. - value: 106.38
  242. hardware: V100
  243. backend: PyTorch
  244. batch size: 1
  245. mode: FP32
  246. resolution: (800, 1333)
  247. Epochs: 24
  248. Results:
  249. - Task: Object Detection
  250. Dataset: COCO
  251. Metrics:
  252. box AP: 41.6
  253. Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco/faster_rcnn_x101_64x4d_fpn_2x_coco_20200512_161033-5961fa95.pth
  254. - Name: faster-rcnn_r50_fpn_iou_1x_coco
  255. In Collection: Faster R-CNN
  256. Config: configs/faster_rcnn/faster-rcnn_r50_fpn_iou_1x_coco.py
  257. Metadata:
  258. Epochs: 12
  259. Results:
  260. - Task: Object Detection
  261. Dataset: COCO
  262. Metrics:
  263. box AP: 37.9
  264. Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_iou_1x_coco/faster_rcnn_r50_fpn_iou_1x_coco_20200506_095954-938e81f0.pth
  265. - Name: faster-rcnn_r50_fpn_giou_1x_coco
  266. In Collection: Faster R-CNN
  267. Config: configs/faster_rcnn/faster-rcnn_r50_fpn_giou_1x_coco.py
  268. Metadata:
  269. Epochs: 12
  270. Results:
  271. - Task: Object Detection
  272. Dataset: COCO
  273. Metrics:
  274. box AP: 37.6
  275. Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_giou_1x_coco-0eada910.pth
  276. - Name: faster-rcnn_r50_fpn_bounded_iou_1x_coco
  277. In Collection: Faster R-CNN
  278. Config: configs/faster_rcnn/faster-rcnn_r50_fpn_bounded-iou_1x_coco.py
  279. Metadata:
  280. Epochs: 12
  281. Results:
  282. - Task: Object Detection
  283. Dataset: COCO
  284. Metrics:
  285. box AP: 37.4
  286. Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_bounded_iou_1x_coco-98ad993b.pth
  287. - Name: faster-rcnn_r50-caffe-dc5_mstrain_1x_coco
  288. In Collection: Faster R-CNN
  289. Config: configs/faster_rcnn/faster-rcnn_r50-caffe-dc5_ms-1x_coco.py
  290. Metadata:
  291. Epochs: 12
  292. Results:
  293. - Task: Object Detection
  294. Dataset: COCO
  295. Metrics:
  296. box AP: 37.4
  297. Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco_20201028_233851-b33d21b9.pth
  298. - Name: faster-rcnn_r50-caffe-dc5_mstrain_3x_coco
  299. In Collection: Faster R-CNN
  300. Config: configs/faster_rcnn/faster-rcnn_r50-caffe-dc5_ms-3x_coco.py
  301. Metadata:
  302. Epochs: 36
  303. Results:
  304. - Task: Object Detection
  305. Dataset: COCO
  306. Metrics:
  307. box AP: 38.7
  308. Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco_20201028_002107-34a53b2c.pth
  309. - Name: faster-rcnn_r50-caffe_fpn_ms-2x_coco
  310. In Collection: Faster R-CNN
  311. Config: configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-2x_coco.py
  312. Metadata:
  313. Training Memory (GB): 4.3
  314. Epochs: 24
  315. Results:
  316. - Task: Object Detection
  317. Dataset: COCO
  318. Metrics:
  319. box AP: 39.7
  320. Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco_bbox_mAP-0.397_20200504_231813-10b2de58.pth
  321. - Name: faster-rcnn_r50-caffe_fpn_ms-3x_coco
  322. In Collection: Faster R-CNN
  323. Config: configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-3x_coco.py
  324. Metadata:
  325. Training Memory (GB): 3.7
  326. Epochs: 36
  327. Results:
  328. - Task: Object Detection
  329. Dataset: COCO
  330. Metrics:
  331. box AP: 39.9
  332. Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210526_095054-1f77628b.pth
  333. - Name: faster-rcnn_r50_fpn_mstrain_3x_coco
  334. In Collection: Faster R-CNN
  335. Config: configs/faster_rcnn/faster-rcnn_r50_fpn_ms-3x_coco.py
  336. Metadata:
  337. Training Memory (GB): 3.9
  338. Epochs: 36
  339. Results:
  340. - Task: Object Detection
  341. Dataset: COCO
  342. Metrics:
  343. box AP: 40.3
  344. Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco/faster_rcnn_r50_fpn_mstrain_3x_coco_20210524_110822-e10bd31c.pth
  345. - Name: faster-rcnn_r101-caffe_fpn_ms-3x_coco
  346. In Collection: Faster R-CNN
  347. Config: configs/faster_rcnn/faster-rcnn_r101-caffe_fpn_ms-3x_coco.py
  348. Metadata:
  349. Training Memory (GB): 5.6
  350. Epochs: 36
  351. Results:
  352. - Task: Object Detection
  353. Dataset: COCO
  354. Metrics:
  355. box AP: 42.0
  356. Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco_20210526_095742-a7ae426d.pth
  357. - Name: faster-rcnn_r101_fpn_ms-3x_coco
  358. In Collection: Faster R-CNN
  359. Config: configs/faster_rcnn/faster-rcnn_r101_fpn_ms-3x_coco.py
  360. Metadata:
  361. Training Memory (GB): 5.8
  362. Epochs: 36
  363. Results:
  364. - Task: Object Detection
  365. Dataset: COCO
  366. Metrics:
  367. box AP: 41.8
  368. Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco/faster_rcnn_r101_fpn_mstrain_3x_coco_20210524_110822-4d4d2ca8.pth
  369. - Name: faster-rcnn_x101-32x4d_fpn_ms-3x_coco
  370. In Collection: Faster R-CNN
  371. Config: configs/faster_rcnn/faster-rcnn_x101-32x4d_fpn_ms-3x_coco.py
  372. Metadata:
  373. Training Memory (GB): 7.0
  374. Epochs: 36
  375. Results:
  376. - Task: Object Detection
  377. Dataset: COCO
  378. Metrics:
  379. box AP: 42.5
  380. Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco_20210524_124151-16b9b260.pth
  381. - Name: faster-rcnn_x101-32x8d_fpn_ms-3x_coco
  382. In Collection: Faster R-CNN
  383. Config: configs/faster_rcnn/faster-rcnn_x101-32x8d_fpn_ms-3x_coco.py
  384. Metadata:
  385. Training Memory (GB): 10.1
  386. Epochs: 36
  387. Results:
  388. - Task: Object Detection
  389. Dataset: COCO
  390. Metrics:
  391. box AP: 42.4
  392. Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco_20210604_182954-002e082a.pth
  393. - Name: faster-rcnn_x101-64x4d_fpn_ms-3x_coco
  394. In Collection: Faster R-CNN
  395. Config: configs/faster_rcnn/faster-rcnn_x101-64x4d_fpn_ms-3x_coco.py
  396. Metadata:
  397. Training Memory (GB): 10.0
  398. Epochs: 36
  399. Results:
  400. - Task: Object Detection
  401. Dataset: COCO
  402. Metrics:
  403. box AP: 43.1
  404. Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco_20210524_124528-26c63de6.pth
  405. - Name: faster-rcnn_r50_fpn_tnr-pretrain_1x_coco
  406. In Collection: Faster R-CNN
  407. Config: configs/faster_rcnn/faster-rcnn_r50-tnr-pre_fpn_1x_coco.py
  408. Metadata:
  409. Training Memory (GB): 4.0
  410. inference time (ms/im):
  411. - value: 46.73
  412. hardware: V100
  413. backend: PyTorch
  414. batch size: 1
  415. mode: FP32
  416. resolution: (800, 1333)
  417. Epochs: 12
  418. Results:
  419. - Task: Object Detection
  420. Dataset: COCO
  421. Metrics:
  422. box AP: 40.2
  423. Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_tnr-pretrain_1x_coco/faster_rcnn_r50_fpn_tnr-pretrain_1x_coco_20220320_085147-efedfda4.pth