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MMDetection 基准测试 和 Model Zoo | 三

本文是全系列中第3 / 4篇:MMDetection

作者|open-mmlab
编译|Flin
来源|Github

基准测试 和 Model Zoo

环境

硬件
  • 8 个 NVIDIA Tesla V100 GPUs
  • Intel Xeon 4114 CPU @ 2.20GHz
软件环境
  • Python 3.6 / 3.7
  • PyTorch 1.1
  • CUDA 9.0.176
  • CUDNN 7.0.4
  • NCCL 2.1.15

镜像站点

我们使用AWS作为托管model zoo的主要站点,并在阿里云上维护镜像。
你可以在模型网址中把https://s3.ap-northeast-2.amazonaws.com/open-mmlab替换为https://open-mmlab.oss-cn-beijing.aliyuncs.com。

常用设置

  • 所有FPN基准和RPN-C4基准均使用8个GPU进行训练,批处理大小为16(每个GPU 2张图像)。其他C4基线使用8个批处理大小为8的GP​​U进行了训练(每个GPU 1张图像)。
  • 所有模型都在coco_2017_train上训练以及在coco_2017_val测试。
  • 我们使用分布式训练,并且BN层统计信息是固定的。
  • 我们采用与Detectron相同的训练时间表。1x表示12个epoch,而2x表示24个epoch,这比Detectron的迭代次数略少,并且可以忽略不计。
  • ImageNet上所有pytorch样式的预训练主干都来自PyTorchmodel zoo。
  • 为了与其他代码库进行公平比较,我们将GPU内存报告 torch.cuda.max_memory_allocated()为所有8个GPU 的最大值。请注意,此值通常小于nvidia-smi显示的值。
  • 我们将推理时间报告为总体时间,包括数据加载,网络转发和后处理。

基线

具有不同主干的更多模型将添加到model zoo。

RPN
Backbone Style Lr schd 内存 (GB) 训练时间 (s/iter) 最短时间 (fps) AR1000 Download
R-50-C4 caffe 1x 20.5 51.1 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_r50_caffe_c4_1x-ea7d3428.pth)
R-50-C4 caffe 2x 2.2 0.17 20.3 52.2 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_r50_caffe_c4_2x-c6d5b958.pth)
R-50-C4 pytorch 1x 20.1 50.2 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_r50_c4_1x-eb38972b.pth)
R-50-C4 pytorch 2x 20.0 51.1 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_r50_c4_2x-3d4c1e14.pth)
R-50-FPN caffe 1x 3.3 0.253 16.9 58.2
R-50-FPN pytorch 1x 3.5 0.276 17.7 57.1 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_r50_fpn_1x_20181010-4a9c0712.pth)
R-50-FPN pytorch 2x 57.6 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_r50_fpn_2x_20181010-88a4a471.pth)
R-101-FPN caffe 1x 5.2 0.379 13.9 59.4
R-101-FPN pytorch 1x 5.4 0.396 14.4 58.6 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_r101_fpn_1x_20181129-f50da4bd.pth)
R-101-FPN pytorch 2x 59.1 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_r101_fpn_2x_20181129-e42c6c9a.pth)
X-101-32x4d-FPN pytorch 1x 6.6 0.589 11.8 59.4 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_x101_32x4d_fpn_1x_20181218-7e379d26.pth)
X-101-32x4d-FPN pytorch 2x 59.9 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_x101_32x4d_fpn_2x_20181218-0510af40.pth)
X-101-64x4d-FPN pytorch 1x 9.5 0.955 8.3 59.8 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_x101_64x4d_fpn_1x_20181218-c1a24f1f.pth)
X-101-64x4d-FPN pytorch 2x 60.0 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_x101_64x4d_fpn_2x_20181218-c22bdd70.pth)
Faster R-CNN
Backbone Style Lr schd 内存 (GB) 训练时间 (s/iter) 最短时间 (fps) box AP Download
R-50-C4 caffe 1x 9.5 34.9 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r50_caffe_c4_1x-75ecfdfa.pth)
R-50-C4 caffe 2x 4.0 0.39 9.3 36.5 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r50_caffe_c4_2x-71c67f27.pth)
R-50-C4 pytorch 1x 9.3 33.9 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r50_c4_1x-642cf91f.pth)
R-50-C4 pytorch 2x 9.4 35.9 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r50_c4_2x-6e4fdf4f.pth)
R-50-FPN caffe 1x 3.6 0.333 13.5 36.6
R-50-FPN pytorch 1x 3.8 0.353 13.6 36.4 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r50_fpn_1x_20181010-3d1b3351.pth)
R-50-FPN pytorch 2x 37.7 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r50_fpn_2x_20181010-443129e1.pth)
R-101-FPN caffe 1x 5.5 0.465 11.5 38.8
R-101-FPN pytorch 1x 5.7 0.474 11.9 38.5 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r101_fpn_1x_20181129-d1468807.pth)
R-101-FPN pytorch 2x 39.4 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r101_fpn_2x_20181129-73e7ade7.pth)
X-101-32x4d-FPN pytorch 1x 6.9 0.672 10.3 40.1 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_x101_32x4d_fpn_1x_20181218-ad81c133.pth)
X-101-32x4d-FPN pytorch 2x 40.4 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_x101_32x4d_fpn_2x_20181218-0ed58946.pth)
X-101-64x4d-FPN pytorch 1x 9.8 1.040 7.3 41.3 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_x101_64x4d_fpn_1x_20181218-c9c69c8f.pth)
X-101-64x4d-FPN pytorch 2x 40.7 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_x101_64x4d_fpn_2x_20181218-fe94f9b8.pth)
HRNetV2p-W18 pytorch 1x 36.1 model(https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/faster_rcnn_hrnetv2p_w18_1x_20190522-e368c387.pth)
HRNetV2p-W18 pytorch 2x 38.3 model(https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/faster_rcnn_hrnetv2p_w18_2x_20190810-9c8615d5.pth)
HRNetV2p-W32 pytorch 1x 39.5 model(https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/faster_rcnn_hrnetv2p_w32_1x_20190522-d22f1fef.pth)
HRNetV2p-W32 pytorch 2x 40.6 model(https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/faster_rcnn_hrnetv2p_w32_2x_20190810-24e8912a.pth)
HRNetV2p-W48 pytorch 1x 40.9 model(https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/faster_rcnn_hrnetv2p_w48_1x_20190820-5c6d0903.pth)
HRNetV2p-W48 pytorch 2x 41.5 model(https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/faster_rcnn_hrnetv2p_w48_2x_20190820-79fb8bfc.pth)
Mask R-CNN
Backbone Style Lr schd 内存 (GB) 训练时间 (s/iter) 最短时间 (fps) box AP mask AP Download
R-50-C4 caffe 1x 8.1 35.9 31.5 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r50_caffe_c4_1x-02a4ad3b.pth)
R-50-C4 caffe 2x 4.2 0.43 8.1 37.9 32.9 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r50_caffe_c4_2x-d150973a.pth)
R-50-C4 pytorch 1x 7.9 35.1 31.2 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r50_c4_1x-a83bdd40.pth)
R-50-C4 pytorch 2x 8.0 37.2 32.5 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r50_c4_2x-3cf169a9.pth)
R-50-FPN caffe 1x 3.8 0.430 10.2 37.4 34.3
R-50-FPN pytorch 1x 3.9 0.453 10.6 37.3 34.2 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r50_fpn_1x_20181010-069fa190.pth)
R-50-FPN pytorch 2x 38.5 35.1 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r50_fpn_2x_20181010-41d35c05.pth)
R-101-FPN caffe 1x 5.7 0.534 9.4 39.9 36.1
R-101-FPN pytorch 1x 5.8 0.571 9.5 39.4 35.9 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r101_fpn_1x_20181129-34ad1961.pth)
R-101-FPN pytorch 2x 40.3 36.5 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r101_fpn_2x_20181129-a254bdfc.pth)
X-101-32x4d-FPN pytorch 1x 7.1 0.759 8.3 41.1 37.1 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_x101_32x4d_fpn_1x_20181218-44e635cc.pth)
X-101-32x4d-FPN pytorch 2x 41.4 37.1 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_x101_32x4d_fpn_2x_20181218-f023dffa.pth)
X-101-64x4d-FPN pytorch 1x 10.0 1.102 6.5 42.1 38.0 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_x101_64x4d_fpn_1x_20181218-cb159987.pth)
X-101-64x4d-FPN pytorch 2x 42.0 37.7 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_x101_64x4d_fpn_2x_20181218-ea936e44.pth)
HRNetV2p-W18 pytorch 1x 37.3 34.2 model(https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/mask_rcnn_hrnetv2p_w18_1x_20190522-c8ad459f.pth)
HRNetV2p-W18 pytorch 2x 39.2 35.7 model(https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/mask_rcnn_hrnetv2p_w18_2x_20190810-1e4747eb.pth)
HRNetV2p-W32 pytorch 1x 40.7 36.8 model(https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/mask_rcnn_hrnetv2p_w32_1x_20190522-374aaa00.pth)
HRNetV2p-W32 pytorch 2x 41.7 37.5 model(https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/mask_rcnn_hrnetv2p_w32_2x_20190810-773eca75.pth)
HRNetV2p-W48 pytorch 1x 42.4 38.1 model(https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/mask_rcnn_hrnetv2p_w48_1x_20190820-0923d1ad.pth)
HRNetV2p-W48 pytorch 2x 42.9 38.3 model(https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/mask_rcnn_hrnetv2p_w48_2x_20190820-70df51b2.pth)
Fast R-CNN (有预先计算的proposals)
Backbone Style 类型 Lr schd 内存 (GB) 训练时间 (s/iter) 最短时间 (fps) box AP mask AP Download
R-50-C4 caffe Faster 1x 6.7 35.0 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_rcnn_r50_caffe_c4_1x-0ef9a60b.pth)
R-50-C4 caffe Faster 2x 3.8 0.34 6.6 36.4 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_rcnn_r50_c4_2x-657a9fc6.pth)
R-50-C4 pytorch Faster 1x 6.3 34.2 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_rcnn_r50_c4_1x-2bc00ca9.pth)
R-50-C4 pytorch Faster 2x 6.1 35.8 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_rcnn_r50_caffe_c4_2x-9171d0fc.pth)
R-50-FPN caffe Faster 1x 3.3 0.242 18.4 36.6
R-50-FPN pytorch Faster 1x 3.5 0.250 16.5 35.8 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_rcnn_r50_fpn_1x_20181010-08160859.pth)
R-50-C4 caffe Mask 1x 8.1 35.9 31.5 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r50_caffe_c4_1x-b43f7f3c.pth)
R-50-C4 caffe Mask 2x 4.2 0.43 8.1 37.9 32.9 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r50_caffe_c4_2x-e3580184.pth)
R-50-C4 pytorch Mask 1x 7.9 35.1 31.2 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r50_c4_1x-bc7fa8c8.pth)
R-50-C4 pytorch Mask 2x 8.0 37.2 32.5 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r50_fpn_2x_20181010-5048cb03.pth)
R-50-FPN pytorch Faster 2x 37.1 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_rcnn_r50_fpn_2x_20181010-d263ada5.pth)
R-101-FPN caffe Faster 1x 5.2 0.355 14.4 38.6
R-101-FPN pytorch Faster 1x 5.4 0.388 13.2 38.1 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_rcnn_r101_fpn_1x_20181129-ffaa2eb0.pth)
R-101-FPN pytorch Faster 2x 38.8 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_rcnn_r101_fpn_2x_20181129-9dba92ce.pth)
R-50-FPN caffe Mask 1x 3.4 0.328 12.8 37.3 34.5
R-50-FPN pytorch Mask 1x 3.5 0.346 12.7 36.8 34.1 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r50_fpn_1x_20181010-e030a38f.pth)
R-50-FPN pytorch Mask 2x 37.9 34.8 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r50_fpn_2x_20181010-5048cb03.pth)
R-101-FPN caffe Mask 1x 5.2 0.429 11.2 39.4 36.1
R-101-FPN pytorch Mask 1x 5.4 0.462 10.9 38.9 35.8 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r101_fpn_1x_20181129-2273fa9b.pth)
R-101-FPN pytorch Mask 2x 39.9 36.4 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r101_fpn_2x_20181129-bf63ec5e.pth)
RetinaNet
Backbone Style Lr schd 内存 (GB) 训练时间 (s/iter) 最短时间 (fps) box AP Download
R-50-FPN caffe 1x 3.4 0.285 12.5 35.8
R-50-FPN pytorch 1x 3.6 0.308 12.1 35.6 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_r50_fpn_1x_20181125-7b0c2548.pth)
R-50-FPN pytorch 2x 36.4 model(https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/retinanet_r50_fpn_2x_20190616-75574209.pth)
R-101-FPN caffe 1x 5.3 0.410 10.4 37.8
R-101-FPN pytorch 1x 5.5 0.429 10.9 37.7 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_r101_fpn_1x_20181129-f016f384.pth)
R-101-FPN pytorch 2x 38.1 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_r101_fpn_2x_20181129-72c14526.pth)
X-101-32x4d-FPN pytorch 1x 6.7 0.632 9.3 39.0 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_x101_32x4d_fpn_1x_20190501-967812ba.pth)
X-101-32x4d-FPN pytorch 2x 39.3 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_x101_32x4d_fpn_2x_20181218-8596452d.pth)
X-101-64x4d-FPN pytorch 1x 9.6 0.993 7.0 40.0 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_x101_64x4d_fpn_1x_20181218-a0a22662.pth)
X-101-64x4d-FPN pytorch 2x 39.6 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_x101_64x4d_fpn_2x_20181218-5e88d045.pth)
Cascade R-CNN
Backbone Style Lr schd 内存 (GB) 训练时间 (s/iter) 最短时间 (fps) box AP Download
R-50-C4 caffe 1x 8.7 0.92 5.0 38.7 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_r50_caffe_c4_1x-7c85c62b.pth)
R-50-FPN caffe 1x 3.9 0.464 10.9 40.5
R-50-FPN pytorch 1x 4.1 0.455 11.9 40.4 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_r50_fpn_1x_20190501-3b6211ab.pth)
R-50-FPN pytorch 20e 41.1 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_r50_fpn_20e_20181123-db483a09.pth)
R-101-FPN caffe 1x 5.8 0.569 9.6 42.4
R-101-FPN pytorch 1x 6.0 0.584 10.3 42.0 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_r101_fpn_1x_20181129-d64ebac7.pth)
R-101-FPN pytorch 20e 42.5 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_r101_fpn_20e_20181129-b46dcede.pth)
X-101-32x4d-FPN pytorch 1x 7.2 0.770 8.9 43.6 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_x101_32x4d_fpn_1x_20190501-af628be5.pth)
X-101-32x4d-FPN pytorch 20e 44.0 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_x101_32x4d_fpn_2x_20181218-28f73c4c.pth)
X-101-64x4d-FPN pytorch 1x 10.0 1.133 6.7 44.5 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_x101_64x4d_fpn_1x_20181218-e2dc376a.pth)
X-101-64x4d-FPN pytorch 20e 44.7 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_x101_64x4d_fpn_2x_20181218-5add321e.pth)
HRNetV2p-W18 pytorch 20e 41.2 model(https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/cascade_rcnn_hrnetv2p_w18_20e_20190810-132012d0.pth)
HRNetV2p-W32 pytorch 20e 43.7 model(https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/cascade_rcnn_hrnetv2p_w32_20e_20190522-55bec4ee.pth)
HRNetV2p-W48 pytorch 20e 44.6 model(https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/cascade_rcnn_hrnetv2p_w48_20e_20190810-f40ed8e1.pth)
Cascade Mask R-CNN
Backbone Style Lr schd 内存 (GB) 训练时间 (s/iter) 最短时间 (fps) box AP mask AP Download
R-50-C4 caffe 1x 9.1 0.99 4.5 39.3 32.8 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_r50_caffe_c4_1x-f72cc254.pth)
R-50-FPN caffe 1x 5.1 0.692 7.6 40.9 35.5
R-50-FPN pytorch 1x 5.3 0.683 7.4 41.2 35.7 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_r50_fpn_1x_20181123-88b170c9.pth)
R-50-FPN pytorch 20e 42.3 36.6 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_r50_fpn_20e_20181123-6e0c9713.pth)
R-101-FPN caffe 1x 7.0 0.803 7.2 43.1 37.2
R-101-FPN pytorch 1x 7.2 0.807 6.8 42.6 37.0 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_r101_fpn_1x_20181129-64f00602.pth)
R-101-FPN pytorch 20e 43.3 37.6 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_r101_fpn_20e_20181129-cb85151d.pth)
X-101-32x4d-FPN pytorch 1x 8.4 0.976 6.6 44.4 38.2 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_x101_32x4d_fpn_1x_20181218-1d944c89.pth)
X-101-32x4d-FPN pytorch 20e 44.7 38.6 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_x101_32x4d_fpn_20e_20181218-761a3473.pth)
X-101-64x4d-FPN pytorch 1x 11.4 1.33 5.3 45.4 39.1 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_x101_64x4d_fpn_1x_20190501-827e0a70.pth)
X-101-64x4d-FPN pytorch 20e 45.7 39.4 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_x101_64x4d_fpn_20e_20181218-630773a7.pth)
HRNetV2p-W18 pytorch 20e 41.9 36.4 model(https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_20190810-054fb7bf.pth)
HRNetV2p-W32 pytorch 20e 44.5 38.5 model(https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_20190810-76f61cd0.pth)
HRNetV2p-W48 pytorch 20e 46.0 39.5 model(https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/cascade_mask_rcnn_hrnetv2p_w48_20e_20190810-d04a1415.pth)

注意s:

  • 20e级联(掩码)R-CNN中的时间表指示在第16和19个epoch减少lr,总共减少20个epoch。
混合任务级联(HTC)
Backbone Style Lr schd 内存 (GB) 训练时间 (s/iter) 最短时间 (fps) box AP mask AP Download
R-50-FPN pytorch 1x 7.4 0.936 4.1 42.1 37.3 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/htc/htc_r50_fpn_1x_20190408-878c1712.pth)
R-50-FPN pytorch 20e 43.2 38.1 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/htc/htc_r50_fpn_20e_20190408-c03b7015.pth)
R-101-FPN pytorch 20e 9.3 1.051 4.0 44.9 39.4 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/htc/htc_r101_fpn_20e_20190408-a2e586db.pth)
X-101-32x4d-FPN pytorch 20e 5.8 0.769 3.8 46.1 40.3 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/htc/htc_x101_32x4d_fpn_20e_20190408-9eae4d0b.pth)
X-101-64x4d-FPN pytorch 20e 7.5 1.120 3.5 46.9 40.8 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/htc/htc_x101_64x4d_fpn_20e_20190408-497f2561.pth)
HRNetV2p-W18 pytorch 20e 43.1 37.9 model(https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/htc_hrnetv2p_w18_20e_20190810-d70072af.pth)
HRNetV2p-W32 pytorch 20e 45.3 39.6 model(https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/htc_hrnetv2p_w32_20e_20190810-82f9ef5a.pth)
HRNetV2p-W48 pytorch 20e 46.8 40.7 model(https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/htc_hrnetv2p_w48_20e_20190810-f6d2c3fd.pth)
HRNetV2p-W48 pytorch 28e 47.0 41.0 model(https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/htc_hrnetv2p_w48_28e_20190810-a4274b38.pth)

注意:

  • 有关详细信息和更强大的模型(50.7 / 43.9),请参阅混合任务级联(https://github.com/open-mmlab/mmdetection/blob/master/configs/htc).
SSD
Backbone Size Style Lr schd 内存 (GB) 训练时间 (s/iter) 最短时间 (fps) box AP Download
VGG16 300 caffe 120e 3.5 0.256 25.9 / 34.6 25.7 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/ssd300_coco_vgg16_caffe_120e_20181221-84d7110b.pth)
VGG16 512 caffe 120e 7.6 0.412 20.7 / 25.4 29.3 model(https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/ssd512_coco_vgg16_caffe_120e_20181221-d48b0be8.pth)

注意:

  • cudnn.benchmark设置为True用于SSD训练和测试。
  • 对于batch size= 1和batch size= 8,报告推理时间。
  • 由于模型参数和nms,COCO和VOC的速度有所不同。
组规范化(GN)

有关详细信息,请参考组规范化(https://github.com/open-mmlab/mmdetection/blob/master/configs/gn)。

权重标准化

有关详细信息,请参考权重标准化(https://github.com/open-mmlab/mmdetection/blob/master/configs/gn+ws)。

可变形卷积v2

有关详细信息,请参阅可变形卷积网络(https://github.com/open-mmlab/mmdetection/blob/master/configs/dcn)。

CARAFE:功能的内容感知重组

有关详细信息,请参考CARAFE(https://github.com/open-mmlab/mmdetection/blob/master/configs/carafe)。

Instaboost

有关详细信息,请参考Instaboost(https://github.com/open-mmlab/mmdetection/blob/master/configs/instaboost)。

Libra R-CNN

有关详细信息,请参考Libra R-CNN(https://github.com/open-mmlab/mmdetection/blob/master/configs/libra_rcnn)。

Guided Anchoring

有关详细信息,请参阅Guided Anchoring(https://github.com/open-mmlab/mmdetection/blob/master/configs/guided_anchoring)。

FCOS

有关详细信息,请参阅FCOS(https://github.com/open-mmlab/mmdetection/blob/master/configs/fcos)。

FoveaBox

有关详细信息,请参考FoveaBox(https://github.com/open-mmlab/mmdetection/blob/master/configs/foveabox)。

RepPoints

有关详细信息,请参考RepPoints(https://github.com/open-mmlab/mmdetection/blob/master/configs/reppoints)。

FreeAnchor

有关详细信息,请参考FreeAnchor(https://github.com/open-mmlab/mmdetection/blob/master/configs/free_anchor)。

Grid R-CNN (plus)

有关详细信息,请参考Grid R-CNN(https://github.com/open-mmlab/mmdetection/blob/master/configs/grid_rcnn)。

GHM

有关详细信息,请参阅GHM(https://github.com/open-mmlab/mmdetection/blob/master/configs/ghm)。

GCNet

有关详细信息,请参考GCNet(https://github.com/open-mmlab/mmdetection/blob/master/configs/gcnet)。

HRNet

有关详细信息,请参考HRNet(https://github.com/open-mmlab/mmdetection/blob/master/configs/hrnet)。

Mask Scoring R-CNN

有关详细信息,请参考Mask Scoring R-CNN(https://github.com/open-mmlab/mmdetection/blob/master/configs/ms_rcnn)。

Train from Scratch

有关详细信息,请参考 重新思考ImageNet预训练(https://github.com/open-mmlab/mmdetection/blob/master/configs/scratch)。

NAS-FPN

有关详细信息,请参阅NAS-FPN(https://github.com/open-mmlab/mmdetection/blob/master/configs/nas_fpn)。

ATSS

有关详细信息,请参考ATSS(https://github.com/open-mmlab/mmdetection/blob/master/configs/atss)。

其他数据集

我们还对PASCAL VOC(https://github.com/open-mmlab/mmdetection/blob/master/configs/pascal_voc),Cityscapes(https://github.com/open-mmlab/ mmdetection / blob / master / configs / cityscapes)和WIDER FACE(https://github.com/open-mmlab/mmdetection/blob/master/configs/wider_face)的一些方法进行了基准测试。

与 Detectron 和 maskrcnn-benchmark 的比较

我们将mmdetection与Detectron(https://github.com/facebookresearch/Detectron) 和maskrcnn-benchmark(https://github.com/facebookresearch/maskrcnn-benchmark)进行比较。使用的主干是R-50-FPN。

通常来说,mmdetection与Detectron相比具有3个优势。

  • 更高的性能(尤其是在mask AP方面)
  • 更快的训练速度
  • 高效记忆
性能

Detectron和maskrcnn-benchmark使用Caffe风格的ResNet作为主干。我们使用caffe样式(权重从(https://github.com/facebookresearch/Detectron/blob/master/MODEL_ZOO.md#imagenet-pretrained-models) 和pytorch样式(权重来自官方model zoo)ResNet主干报告结果,表示为pytorch样式结果 / caffe样式结果。

我们发现,pytorch风格的ResNet通常比caffe风格的ResNet收敛慢,因此在1倍进度中导致结果略低,但2倍进度的最终结果则较高。

类型 Lr schd Detectron maskrcnn-benchmark mmdetection
RPN 1x 57.2 57.1 / 58.2
2x 57.6 / –
Faster R-CNN 1x 36.7 36.8 36.4 / 36.6
2x 37.9 37.7 / –
Mask R-CNN 1x 37.7 & 33.9 37.8 & 34.2 37.3 & 34.2 / 37.4 & 34.3
2x 38.6 & 34.5 38.5 & 35.1 / –
Fast R-CNN 1x 36.4 35.8 / 36.6
2x 36.8 37.1 / –
Fast R-CNN (w/mask) 1x 37.3 & 33.7 36.8 & 34.1 / 37.3 & 34.5
2x 37.7 & 34.0 37.9 & 34.8 / –
训练速度

训练速度以s/iter为单位。越低越好。

类型 Detectron (P1001) maskrcnn-benchmark (V100) mmdetection (V1002)
RPN 0.416 0.253
Faster R-CNN 0.544 0.353 0.333
Mask R-CNN 0.889 0.454 0.430
Fast R-CNN 0.285 0.242
Fast R-CNN (w/mask) 0.377 0.328
  • 1。Facebook的Big Basin服务器(P100 / V100)比我们使用的服务器稍快。mmdetection在FB的服务器上也可以稍快一些地运行。

  • 2。为了公平比较,我们在此处列出了caffe的结果。

推理速度

推理速度在单个GPU上以fps(img / s)进行测量。越高越好。

类型 Detectron (P100) maskrcnn-benchmark (V100) mmdetection (V100)
RPN 12.5 16.9
Faster R-CNN 10.3 7.9 13.5
Mask R-CNN 8.5 7.7 10.2
Fast R-CNN 12.5 18.4
Fast R-CNN (w/mask) 9.9 12.8
训练内存
类型 Detectron maskrcnn-benchmark mmdetection
RPN 6.4 3.3
Faster R-CNN 7.2 4.4 3.6
Mask R-CNN 8.6 5.2 3.8
Fast R-CNN 6.0 3.3
Fast R-CNN (w/mask) 7.9 3.4

毫无疑问,maskrcnn基准测试和mmdetection比Detectron的存储效率更高,而主要优点是PyTorch本身。我们还执行一些内存优化来推动它向前发展。

请注意,Caffe2和PyTorch具有不同的API,以通过不同的实现获取内存使用情况。对于所有代码库,nvidia-smi显示的内存使用量均大于上表中报告的数字。

原文链接:https://mmdetection.readthedocs.io/en/latest/MODEL_ZOO.html

原创文章,作者:磐石,如若转载,请注明出处:https://panchuang.net/2020/05/05/mmdetection-%e5%9f%ba%e5%87%86%e6%b5%8b%e8%af%95-%e5%92%8c-model-zoo-%e4%b8%89/

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