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MSCOCO Detection Benchmark

We recommend using these caffe models with py-RFCN-priv

1. Results training on MSCOCO2017-trainval and testing on test-dev2017.

Network mAP mAP@50 mAP@75 mAP@S mAP@M mAP@L
RFCN-se-inception-v2
with ms-train & ohem & multigrid
32.6 53.6 34.5 12.5 35.1 48.4
RFCN-se-inception-v2
with ms-train & ohem & multigrid & bbox-voting & soft-nms & flipping & ms-test
36.8 59.8 38.7 19.7 39.8 49.1
RFCN-se-resnet50
with ms-train & ohem & multigrid
32.9 54.4 34.8 13.0 35.3 48.1
FPN-Faster-inception-v4
with ms-train
36.5 58.5 38.8 16.5 38.8 52.1
FPN-Faster-inception-v4
with ms-train & bbox-voting & soft-nms
38.3 61.0 40.8 20.0 41.5 51.4
FPN-Faster-inception-v4
with ms-train & bbox-voting & soft-nms & flipping & ms-test
39.5 62.5 42.3 23.3 43.2 51.0
RFCN-air101
with ms-train & ohem & multigrid
38.2 60.1 41.2 18.2 41.9 53.0
RFCN-air101
with extra-7-epochs & ms-train & ohem & multigrid
38.5 60.2 41.4 18.3 42.1 53.4
RFCN-air101
with ms-train & ohem & multigrid & bbox-voting & soft-nms & flipping
40.4 63.5 43.5 22.6 44.4 52.0
RFCN-air101
with ms-train & ohem & multigrid & bbox-voting & soft-nms & flipping & ms-test
41.8 65.3 45.3 26.1 45.6 52.4
RFCN-air101
with ms-train & ohem & multigrid & bbox-voting & soft-nms & flipping & assign-ms-test
42.1 64.6 45.6 25.6 44.5 54.1
RFCN-air101
with ms-train & ohem & multigrid & deformpsroi & bbox-voting & soft-nms & flipping & assign-ms-test
43.2 66.0 46.7 25.6 46.3 55.9
Faster-2fc-air101
with ms-train & ohem & multigrid
36.5 60.4 38.1 15.5 39.5 53.5
  • All the models are test on a single scale (600*1000) without any bells and whistles;

2. Context Pyramid Attention Network (CPANet) results training on MSCOCO2017-trainval and testing on test-dev2017.

Network mAP mAP@50 mAP@75 mAP@S mAP@M mAP@L
CPANet-air101
with ms-train & ohem & multigrid & 600-scale-test
40.1 62.2 43.4 19.4 44.4 55.9
CPANet-air101
with ms-train & ohem & multigrid & 800-scale-test
41.9 64.8 45.5 24.0 45.9 54.6
CPANet-air101
with ms-train & ohem & multigrid & 800-scale-test & snms
42.7 65.4 46.7 24.6 46.8 55.6
CPANet-air101
with ms-train & ohem & multigrid & 800-scale-test & snms & flipping
43.5 65.9 47.5 25.1 47.7 56.6

3. COCOPerson results training on MSCOCO2017-trainval and testing on test-dev2017.

Network mAP mAP@50 mAP@75 mAP@S mAP@M mAP@L mAR@10
RFCN-se-air14-thin-specific
with ms-train & ohem & multigrid
21.5 48.9 16.5 12.3 27.3 30.8 28.6
RFCN-resnet18-specific
with ms-train & ohem & multigrid
38.5 66.1 39.8 16.8 47.1 63.0 41.9
RFCN-se-resnet50-specific
with 800-scale-train & ohem & multigrid
39.0 64.1 41.1 13.5 48.4 66.4 43.9
RFCN-se-resnet50-specific
with ms-train & ohem & multigrid
41.9 67.7 44.3 18.6 51.0 67.9 46.0
RFCN-se-resnet50-specific
with ms-train & ohem & multigrid & snms & flip & ms-test
44.6 72.8 47.3 25.3 54.4 63.3 49.8
RFCN-se-resnet50
with ms-train & ohem & multigrid
42.7 72.0 44.5 21.0 51.1 66.4 45.4
RFCN-se-inception-v2-specific
with ms-train & ohem & multigrid
41.2 66.7 43.2 17.6 50.0 68.3 45.1
RFCN-se-inception-v2
with ms-train & ohem & multigrid
42.3 71.4 44.2 19.5 50.7 67.2 44.9
RFCN-se-inception-v2
with ms-train & ohem & multigrid & bbox-voting & soft-nms & flipping & ms-test
48.0 79.5 50.0 28.3 55.8 67.5 50.8
RFCN-air101
with ms-train & ohem & multigrid & deformpsroi & bbox-voting & soft-nms & flipping & assign-ms-test
54.0 83.9 58.2 35.2 61.6 73.0 55.1
CPANet-air101
with ms-train & ohem & multigrid & 600-scale-test
47.7 76.4 51.1 25.3 56.8 70.6 50.2
CPANet-air101
with ms-train & ohem & multigrid & 800-scale-test & snms & flipping
53.4 82.7 58.0 33.1 61.8 73.3 55.0