metafile.yml 4.8 KB

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  1. Models:
  2. - Name: faster-rcnn_res2net-101_fpn_2x_coco
  3. In Collection: Faster R-CNN
  4. Config: configs/res2net/faster-rcnn_res2net-101_fpn_2x_coco.py
  5. Metadata:
  6. Training Memory (GB): 7.4
  7. Epochs: 24
  8. Training Data: COCO
  9. Training Techniques:
  10. - SGD with Momentum
  11. - Weight Decay
  12. Training Resources: 8x V100 GPUs
  13. Architecture:
  14. - Res2Net
  15. Results:
  16. - Task: Object Detection
  17. Dataset: COCO
  18. Metrics:
  19. box AP: 43.0
  20. Weights: https://download.openmmlab.com/mmdetection/v2.0/res2net/faster_rcnn_r2_101_fpn_2x_coco/faster_rcnn_r2_101_fpn_2x_coco-175f1da6.pth
  21. Paper:
  22. URL: https://arxiv.org/abs/1904.01169
  23. Title: 'Res2Net for object detection and instance segmentation'
  24. README: configs/res2net/README.md
  25. Code:
  26. URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/res2net.py#L239
  27. Version: v2.1.0
  28. - Name: mask-rcnn_res2net-101_fpn_2x_coco
  29. In Collection: Mask R-CNN
  30. Config: configs/res2net/mask-rcnn_res2net-101_fpn_2x_coco.py
  31. Metadata:
  32. Training Memory (GB): 7.9
  33. Epochs: 24
  34. Training Data: COCO
  35. Training Techniques:
  36. - SGD with Momentum
  37. - Weight Decay
  38. Training Resources: 8x V100 GPUs
  39. Architecture:
  40. - Res2Net
  41. Results:
  42. - Task: Object Detection
  43. Dataset: COCO
  44. Metrics:
  45. box AP: 43.6
  46. - Task: Instance Segmentation
  47. Dataset: COCO
  48. Metrics:
  49. mask AP: 38.7
  50. Weights: https://download.openmmlab.com/mmdetection/v2.0/res2net/mask_rcnn_r2_101_fpn_2x_coco/mask_rcnn_r2_101_fpn_2x_coco-17f061e8.pth
  51. Paper:
  52. URL: https://arxiv.org/abs/1904.01169
  53. Title: 'Res2Net for object detection and instance segmentation'
  54. README: configs/res2net/README.md
  55. Code:
  56. URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/res2net.py#L239
  57. Version: v2.1.0
  58. - Name: cascade-rcnn_res2net-101_fpn_20e_coco
  59. In Collection: Cascade R-CNN
  60. Config: configs/res2net/cascade-rcnn_res2net-101_fpn_20e_coco.py
  61. Metadata:
  62. Training Memory (GB): 7.8
  63. Epochs: 20
  64. Training Data: COCO
  65. Training Techniques:
  66. - SGD with Momentum
  67. - Weight Decay
  68. Training Resources: 8x V100 GPUs
  69. Architecture:
  70. - Res2Net
  71. Results:
  72. - Task: Object Detection
  73. Dataset: COCO
  74. Metrics:
  75. box AP: 45.7
  76. Weights: https://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_rcnn_r2_101_fpn_20e_coco/cascade_rcnn_r2_101_fpn_20e_coco-f4b7b7db.pth
  77. Paper:
  78. URL: https://arxiv.org/abs/1904.01169
  79. Title: 'Res2Net for object detection and instance segmentation'
  80. README: configs/res2net/README.md
  81. Code:
  82. URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/res2net.py#L239
  83. Version: v2.1.0
  84. - Name: cascade-mask-rcnn_res2net-101_fpn_20e_coco
  85. In Collection: Cascade R-CNN
  86. Config: configs/res2net/cascade-mask-rcnn_res2net-101_fpn_20e_coco.py
  87. Metadata:
  88. Training Memory (GB): 9.5
  89. Epochs: 20
  90. Training Data: COCO
  91. Training Techniques:
  92. - SGD with Momentum
  93. - Weight Decay
  94. Training Resources: 8x V100 GPUs
  95. Architecture:
  96. - Res2Net
  97. Results:
  98. - Task: Object Detection
  99. Dataset: COCO
  100. Metrics:
  101. box AP: 46.4
  102. - Task: Instance Segmentation
  103. Dataset: COCO
  104. Metrics:
  105. mask AP: 40.0
  106. Weights: https://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco/cascade_mask_rcnn_r2_101_fpn_20e_coco-8a7b41e1.pth
  107. Paper:
  108. URL: https://arxiv.org/abs/1904.01169
  109. Title: 'Res2Net for object detection and instance segmentation'
  110. README: configs/res2net/README.md
  111. Code:
  112. URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/res2net.py#L239
  113. Version: v2.1.0
  114. - Name: htc_res2net-101_fpn_20e_coco
  115. In Collection: HTC
  116. Config: configs/res2net/htc_res2net-101_fpn_20e_coco.py
  117. Metadata:
  118. Epochs: 20
  119. Training Data: COCO
  120. Training Techniques:
  121. - SGD with Momentum
  122. - Weight Decay
  123. Training Resources: 8x V100 GPUs
  124. Architecture:
  125. - Res2Net
  126. Results:
  127. - Task: Object Detection
  128. Dataset: COCO
  129. Metrics:
  130. box AP: 47.5
  131. - Task: Instance Segmentation
  132. Dataset: COCO
  133. Metrics:
  134. mask AP: 41.6
  135. Weights: https://download.openmmlab.com/mmdetection/v2.0/res2net/htc_r2_101_fpn_20e_coco/htc_r2_101_fpn_20e_coco-3a8d2112.pth
  136. Paper:
  137. URL: https://arxiv.org/abs/1904.01169
  138. Title: 'Res2Net for object detection and instance segmentation'
  139. README: configs/res2net/README.md
  140. Code:
  141. URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/res2net.py#L239
  142. Version: v2.1.0