metafile.yaml 16 KB

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  1. Collections:
  2. - Name: UPerNet
  3. License: Apache License 2.0
  4. Metadata:
  5. Training Data:
  6. - Cityscapes
  7. - ADE20K
  8. - Pascal VOC 2012 + Aug
  9. Paper:
  10. Title: Unified Perceptual Parsing for Scene Understanding
  11. URL: https://arxiv.org/pdf/1807.10221.pdf
  12. README: configs/upernet/README.md
  13. Frameworks:
  14. - PyTorch
  15. Models:
  16. - Name: upernet_r50_4xb2-40k_cityscapes-512x1024
  17. In Collection: UPerNet
  18. Results:
  19. Task: Semantic Segmentation
  20. Dataset: Cityscapes
  21. Metrics:
  22. mIoU: 77.1
  23. mIoU(ms+flip): 78.37
  24. Config: configs/upernet/upernet_r50_4xb2-40k_cityscapes-512x1024.py
  25. Metadata:
  26. Training Data: Cityscapes
  27. Batch Size: 8
  28. Architecture:
  29. - R-50
  30. - UPerNet
  31. Training Resources: 4x V100 GPUS
  32. Memory (GB): 6.4
  33. Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_40k_cityscapes/upernet_r50_512x1024_40k_cityscapes_20200605_094827-aa54cb54.pth
  34. Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_40k_cityscapes/upernet_r50_512x1024_40k_cityscapes_20200605_094827.log.json
  35. Paper:
  36. Title: Unified Perceptual Parsing for Scene Understanding
  37. URL: https://arxiv.org/pdf/1807.10221.pdf
  38. Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
  39. Framework: PyTorch
  40. - Name: upernet_r101_4xb2-40k_cityscapes-512x1024
  41. In Collection: UPerNet
  42. Results:
  43. Task: Semantic Segmentation
  44. Dataset: Cityscapes
  45. Metrics:
  46. mIoU: 78.69
  47. mIoU(ms+flip): 80.11
  48. Config: configs/upernet/upernet_r101_4xb2-40k_cityscapes-512x1024.py
  49. Metadata:
  50. Training Data: Cityscapes
  51. Batch Size: 8
  52. Architecture:
  53. - R-101
  54. - UPerNet
  55. Training Resources: 4x V100 GPUS
  56. Memory (GB): 7.4
  57. Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_40k_cityscapes/upernet_r101_512x1024_40k_cityscapes_20200605_094933-ebce3b10.pth
  58. Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_40k_cityscapes/upernet_r101_512x1024_40k_cityscapes_20200605_094933.log.json
  59. Paper:
  60. Title: Unified Perceptual Parsing for Scene Understanding
  61. URL: https://arxiv.org/pdf/1807.10221.pdf
  62. Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
  63. Framework: PyTorch
  64. - Name: upernet_r50_4xb2-40k_cityscapes-769x769
  65. In Collection: UPerNet
  66. Results:
  67. Task: Semantic Segmentation
  68. Dataset: Cityscapes
  69. Metrics:
  70. mIoU: 77.98
  71. mIoU(ms+flip): 79.7
  72. Config: configs/upernet/upernet_r50_4xb2-40k_cityscapes-769x769.py
  73. Metadata:
  74. Training Data: Cityscapes
  75. Batch Size: 8
  76. Architecture:
  77. - R-50
  78. - UPerNet
  79. Training Resources: 4x V100 GPUS
  80. Memory (GB): 7.2
  81. Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_40k_cityscapes/upernet_r50_769x769_40k_cityscapes_20200530_033048-92d21539.pth
  82. Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_40k_cityscapes/upernet_r50_769x769_40k_cityscapes_20200530_033048.log.json
  83. Paper:
  84. Title: Unified Perceptual Parsing for Scene Understanding
  85. URL: https://arxiv.org/pdf/1807.10221.pdf
  86. Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
  87. Framework: PyTorch
  88. - Name: upernet_r101_4xb2-40k_cityscapes-769x769
  89. In Collection: UPerNet
  90. Results:
  91. Task: Semantic Segmentation
  92. Dataset: Cityscapes
  93. Metrics:
  94. mIoU: 79.03
  95. mIoU(ms+flip): 80.77
  96. Config: configs/upernet/upernet_r101_4xb2-40k_cityscapes-769x769.py
  97. Metadata:
  98. Training Data: Cityscapes
  99. Batch Size: 8
  100. Architecture:
  101. - R-101
  102. - UPerNet
  103. Training Resources: 4x V100 GPUS
  104. Memory (GB): 8.4
  105. Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_40k_cityscapes/upernet_r101_769x769_40k_cityscapes_20200530_040819-83c95d01.pth
  106. Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_40k_cityscapes/upernet_r101_769x769_40k_cityscapes_20200530_040819.log.json
  107. Paper:
  108. Title: Unified Perceptual Parsing for Scene Understanding
  109. URL: https://arxiv.org/pdf/1807.10221.pdf
  110. Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
  111. Framework: PyTorch
  112. - Name: upernet_r50_4xb2-80k_cityscapes-512x1024
  113. In Collection: UPerNet
  114. Results:
  115. Task: Semantic Segmentation
  116. Dataset: Cityscapes
  117. Metrics:
  118. mIoU: 78.19
  119. mIoU(ms+flip): 79.19
  120. Config: configs/upernet/upernet_r50_4xb2-80k_cityscapes-512x1024.py
  121. Metadata:
  122. Training Data: Cityscapes
  123. Batch Size: 8
  124. Architecture:
  125. - R-50
  126. - UPerNet
  127. Training Resources: 4x V100 GPUS
  128. Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_80k_cityscapes/upernet_r50_512x1024_80k_cityscapes_20200607_052207-848beca8.pth
  129. Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_80k_cityscapes/upernet_r50_512x1024_80k_cityscapes_20200607_052207.log.json
  130. Paper:
  131. Title: Unified Perceptual Parsing for Scene Understanding
  132. URL: https://arxiv.org/pdf/1807.10221.pdf
  133. Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
  134. Framework: PyTorch
  135. - Name: upernet_r101_4xb2-80k_cityscapes-512x1024
  136. In Collection: UPerNet
  137. Results:
  138. Task: Semantic Segmentation
  139. Dataset: Cityscapes
  140. Metrics:
  141. mIoU: 79.4
  142. mIoU(ms+flip): 80.46
  143. Config: configs/upernet/upernet_r101_4xb2-80k_cityscapes-512x1024.py
  144. Metadata:
  145. Training Data: Cityscapes
  146. Batch Size: 8
  147. Architecture:
  148. - R-101
  149. - UPerNet
  150. Training Resources: 4x V100 GPUS
  151. Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_80k_cityscapes/upernet_r101_512x1024_80k_cityscapes_20200607_002403-f05f2345.pth
  152. Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_80k_cityscapes/upernet_r101_512x1024_80k_cityscapes_20200607_002403.log.json
  153. Paper:
  154. Title: Unified Perceptual Parsing for Scene Understanding
  155. URL: https://arxiv.org/pdf/1807.10221.pdf
  156. Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
  157. Framework: PyTorch
  158. - Name: upernet_r50_4xb2-80k_cityscapes-769x769
  159. In Collection: UPerNet
  160. Results:
  161. Task: Semantic Segmentation
  162. Dataset: Cityscapes
  163. Metrics:
  164. mIoU: 79.39
  165. mIoU(ms+flip): 80.92
  166. Config: configs/upernet/upernet_r50_4xb2-80k_cityscapes-769x769.py
  167. Metadata:
  168. Training Data: Cityscapes
  169. Batch Size: 8
  170. Architecture:
  171. - R-50
  172. - UPerNet
  173. Training Resources: 4x V100 GPUS
  174. Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_80k_cityscapes/upernet_r50_769x769_80k_cityscapes_20200607_005107-82ae7d15.pth
  175. Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_80k_cityscapes/upernet_r50_769x769_80k_cityscapes_20200607_005107.log.json
  176. Paper:
  177. Title: Unified Perceptual Parsing for Scene Understanding
  178. URL: https://arxiv.org/pdf/1807.10221.pdf
  179. Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
  180. Framework: PyTorch
  181. - Name: upernet_r101_4xb2-80k_cityscapes-769x769
  182. In Collection: UPerNet
  183. Results:
  184. Task: Semantic Segmentation
  185. Dataset: Cityscapes
  186. Metrics:
  187. mIoU: 80.1
  188. mIoU(ms+flip): 81.49
  189. Config: configs/upernet/upernet_r101_4xb2-80k_cityscapes-769x769.py
  190. Metadata:
  191. Training Data: Cityscapes
  192. Batch Size: 8
  193. Architecture:
  194. - R-101
  195. - UPerNet
  196. Training Resources: 4x V100 GPUS
  197. Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_80k_cityscapes/upernet_r101_769x769_80k_cityscapes_20200607_001014-082fc334.pth
  198. Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_80k_cityscapes/upernet_r101_769x769_80k_cityscapes_20200607_001014.log.json
  199. Paper:
  200. Title: Unified Perceptual Parsing for Scene Understanding
  201. URL: https://arxiv.org/pdf/1807.10221.pdf
  202. Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
  203. Framework: PyTorch
  204. - Name: upernet_r50_4xb4-80k_ade20k-512x512
  205. In Collection: UPerNet
  206. Results:
  207. Task: Semantic Segmentation
  208. Dataset: ADE20K
  209. Metrics:
  210. mIoU: 40.7
  211. mIoU(ms+flip): 41.81
  212. Config: configs/upernet/upernet_r50_4xb4-80k_ade20k-512x512.py
  213. Metadata:
  214. Training Data: ADE20K
  215. Batch Size: 16
  216. Architecture:
  217. - R-50
  218. - UPerNet
  219. Training Resources: 4x V100 GPUS
  220. Memory (GB): 8.1
  221. Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_80k_ade20k/upernet_r50_512x512_80k_ade20k_20200614_144127-ecc8377b.pth
  222. Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_80k_ade20k/upernet_r50_512x512_80k_ade20k_20200614_144127.log.json
  223. Paper:
  224. Title: Unified Perceptual Parsing for Scene Understanding
  225. URL: https://arxiv.org/pdf/1807.10221.pdf
  226. Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
  227. Framework: PyTorch
  228. - Name: upernet_r101_4xb4-80k_ade20k-512x512
  229. In Collection: UPerNet
  230. Results:
  231. Task: Semantic Segmentation
  232. Dataset: ADE20K
  233. Metrics:
  234. mIoU: 42.91
  235. mIoU(ms+flip): 43.96
  236. Config: configs/upernet/upernet_r101_4xb4-80k_ade20k-512x512.py
  237. Metadata:
  238. Training Data: ADE20K
  239. Batch Size: 16
  240. Architecture:
  241. - R-101
  242. - UPerNet
  243. Training Resources: 4x V100 GPUS
  244. Memory (GB): 9.1
  245. Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_80k_ade20k/upernet_r101_512x512_80k_ade20k_20200614_185117-32e4db94.pth
  246. Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_80k_ade20k/upernet_r101_512x512_80k_ade20k_20200614_185117.log.json
  247. Paper:
  248. Title: Unified Perceptual Parsing for Scene Understanding
  249. URL: https://arxiv.org/pdf/1807.10221.pdf
  250. Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
  251. Framework: PyTorch
  252. - Name: upernet_r50_4xb4-160k_ade20k-512x512
  253. In Collection: UPerNet
  254. Results:
  255. Task: Semantic Segmentation
  256. Dataset: ADE20K
  257. Metrics:
  258. mIoU: 42.05
  259. mIoU(ms+flip): 42.78
  260. Config: configs/upernet/upernet_r50_4xb4-160k_ade20k-512x512.py
  261. Metadata:
  262. Training Data: ADE20K
  263. Batch Size: 16
  264. Architecture:
  265. - R-50
  266. - UPerNet
  267. Training Resources: 4x V100 GPUS
  268. Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_160k_ade20k/upernet_r50_512x512_160k_ade20k_20200615_184328-8534de8d.pth
  269. Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_160k_ade20k/upernet_r50_512x512_160k_ade20k_20200615_184328.log.json
  270. Paper:
  271. Title: Unified Perceptual Parsing for Scene Understanding
  272. URL: https://arxiv.org/pdf/1807.10221.pdf
  273. Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
  274. Framework: PyTorch
  275. - Name: upernet_r101_4xb4-160k_ade20k-512x512
  276. In Collection: UPerNet
  277. Results:
  278. Task: Semantic Segmentation
  279. Dataset: ADE20K
  280. Metrics:
  281. mIoU: 43.82
  282. mIoU(ms+flip): 44.85
  283. Config: configs/upernet/upernet_r101_4xb4-160k_ade20k-512x512.py
  284. Metadata:
  285. Training Data: ADE20K
  286. Batch Size: 16
  287. Architecture:
  288. - R-101
  289. - UPerNet
  290. Training Resources: 4x V100 GPUS
  291. Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_160k_ade20k/upernet_r101_512x512_160k_ade20k_20200615_161951-91b32684.pth
  292. Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_160k_ade20k/upernet_r101_512x512_160k_ade20k_20200615_161951.log.json
  293. Paper:
  294. Title: Unified Perceptual Parsing for Scene Understanding
  295. URL: https://arxiv.org/pdf/1807.10221.pdf
  296. Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
  297. Framework: PyTorch
  298. - Name: upernet_r50_4xb4-20k_voc12aug-512x512
  299. In Collection: UPerNet
  300. Results:
  301. Task: Semantic Segmentation
  302. Dataset: Pascal VOC 2012 + Aug
  303. Metrics:
  304. mIoU: 74.82
  305. mIoU(ms+flip): 76.35
  306. Config: configs/upernet/upernet_r50_4xb4-20k_voc12aug-512x512.py
  307. Metadata:
  308. Training Data: Pascal VOC 2012 + Aug
  309. Batch Size: 16
  310. Architecture:
  311. - R-50
  312. - UPerNet
  313. Training Resources: 4x V100 GPUS
  314. Memory (GB): 6.4
  315. Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_20k_voc12aug/upernet_r50_512x512_20k_voc12aug_20200617_165330-5b5890a7.pth
  316. Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_20k_voc12aug/upernet_r50_512x512_20k_voc12aug_20200617_165330.log.json
  317. Paper:
  318. Title: Unified Perceptual Parsing for Scene Understanding
  319. URL: https://arxiv.org/pdf/1807.10221.pdf
  320. Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
  321. Framework: PyTorch
  322. - Name: upernet_r101_4xb4-20k_voc12aug-512x512
  323. In Collection: UPerNet
  324. Results:
  325. Task: Semantic Segmentation
  326. Dataset: Pascal VOC 2012 + Aug
  327. Metrics:
  328. mIoU: 77.1
  329. mIoU(ms+flip): 78.29
  330. Config: configs/upernet/upernet_r101_4xb4-20k_voc12aug-512x512.py
  331. Metadata:
  332. Training Data: Pascal VOC 2012 + Aug
  333. Batch Size: 16
  334. Architecture:
  335. - R-101
  336. - UPerNet
  337. Training Resources: 4x V100 GPUS
  338. Memory (GB): 7.5
  339. Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_20k_voc12aug/upernet_r101_512x512_20k_voc12aug_20200617_165629-f14e7f27.pth
  340. Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_20k_voc12aug/upernet_r101_512x512_20k_voc12aug_20200617_165629.log.json
  341. Paper:
  342. Title: Unified Perceptual Parsing for Scene Understanding
  343. URL: https://arxiv.org/pdf/1807.10221.pdf
  344. Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
  345. Framework: PyTorch
  346. - Name: upernet_r50_4xb4-40k_voc12aug-512x512
  347. In Collection: UPerNet
  348. Results:
  349. Task: Semantic Segmentation
  350. Dataset: Pascal VOC 2012 + Aug
  351. Metrics:
  352. mIoU: 75.92
  353. mIoU(ms+flip): 77.44
  354. Config: configs/upernet/upernet_r50_4xb4-40k_voc12aug-512x512.py
  355. Metadata:
  356. Training Data: Pascal VOC 2012 + Aug
  357. Batch Size: 16
  358. Architecture:
  359. - R-50
  360. - UPerNet
  361. Training Resources: 4x V100 GPUS
  362. Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_40k_voc12aug/upernet_r50_512x512_40k_voc12aug_20200613_162257-ca9bcc6b.pth
  363. Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_40k_voc12aug/upernet_r50_512x512_40k_voc12aug_20200613_162257.log.json
  364. Paper:
  365. Title: Unified Perceptual Parsing for Scene Understanding
  366. URL: https://arxiv.org/pdf/1807.10221.pdf
  367. Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
  368. Framework: PyTorch
  369. - Name: upernet_r101_4xb4-40k_voc12aug-512x512
  370. In Collection: UPerNet
  371. Results:
  372. Task: Semantic Segmentation
  373. Dataset: Pascal VOC 2012 + Aug
  374. Metrics:
  375. mIoU: 77.43
  376. mIoU(ms+flip): 78.56
  377. Config: configs/upernet/upernet_r101_4xb4-40k_voc12aug-512x512.py
  378. Metadata:
  379. Training Data: Pascal VOC 2012 + Aug
  380. Batch Size: 16
  381. Architecture:
  382. - R-101
  383. - UPerNet
  384. Training Resources: 4x V100 GPUS
  385. Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_40k_voc12aug/upernet_r101_512x512_40k_voc12aug_20200613_163549-e26476ac.pth
  386. Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_40k_voc12aug/upernet_r101_512x512_40k_voc12aug_20200613_163549.log.json
  387. Paper:
  388. Title: Unified Perceptual Parsing for Scene Understanding
  389. URL: https://arxiv.org/pdf/1807.10221.pdf
  390. Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
  391. Framework: PyTorch