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SimpleDet Model Zoo

Introduction

This file documents a large collection of baselines trained with SimpleDet.

Common Settings

  • All models were trained on train2014+valminusminival2014, and tested on minival2014.
  • We adopt the same training schedules as Detectron. 1x indicates 6 epochs and 2x indicates 12 epochs since we append flipped images into training data.
  • We report the training GPU memory as what nvidia-smi shows.

ImageNet Pretrained Models

We provide the ImageNet pretrained models used by SimpleDet. Unless otherwise noted, these models are trained on the standard ImageNet-1k dataset.

ResNetV1b Baselines

All config files can be found in config/resnet_v1b. Pretrains are converted from GluonCV. All AP results are reported on minival2014 of the COCO dataset.

Model Backbone Head Train Schedule AP AP50 AP75 APs APm APl
Faster R50v1b-C4 C5-512ROI 1X 35.7 56.7 37.9 18.6 40.4 48.1
Faster R50v1b-C4 C5-512ROI 2X 36.9 57.9 39.3 19.9 41.4 50.2
Faster R101v1b-C4 C5-512ROI 1X 40.0 61.3 43.1 21.5 44.8 54.3
Faster R101v1b-C4 C5-512ROI 2X 40.5 61.2 43.8 22.5 44.8 55.4
Faster R152v1b-C4 C5-512ROI 1X 41.3 62.6 44.6 23.4 46.2 55.6
Faster R152v1b-C4 C5-512ROI 2X 41.8 62.4 45.2 23.2 46.0 56.9
Faster R50v1b-FPN 2MLP 1X 37.2 59.4 40.4 22.3 41.3 47.6
Faster R50v1b-FPN 2MLP 2X 38.0 59.7 41.5 22.2 41.6 48.8
Faster R101v1b-FPN 2MLP 1X 39.9 62.1 43.5 23.1 44.4 51.1
Faster R101v1b-FPN 2MLP 2X 40.4 62.1 44.0 23.2 44.4 52.7
Faster R152v1b-FPN 2MLP 1X 41.5 63.5 45.7 24.7 46.0 53.3
Faster R152v1b-FPN 2MLP 2X 42.0 63.6 45.9 24.8 45.9 55.0
Mask(BBox) R50v1b-FPN 2MLP 1X 37.8 59.9 40.9 22.9 41.5 48.0
Mask(BBox) R50v1b-FPN 2MLP 2X 38.6 60.3 41.8 22.6 42.4 49.8
Mask(BBox) R101v1b-FPN 2MLP 1X 40.4 62.2 44.1 24.0 44.4 52.1
Mask(BBox) R101v1b-FPN 2MLP 2X 41.3 62.8 45.0 23.9 45.4 53.7
Mask(BBox) R152v1b-FPN 2MLP 1X 41.8 63.7 46.1 25.3 46.3 53.6
Mask(BBox) R152v1b-FPN 2MLP 2X 42.8 63.8 46.8 24.6 47.1 55.9
Mask(Inst) R50v1b-FPN 2MLP 1X 34.4 56.5 36.2 18.7 37.9 46.4
Mask(Inst) R50v1b-FPN 2MLP 2X 34.9 56.9 37.1 18.3 38.4 47.8
Mask(Inst) R101v1b-FPN 2MLP 1X 36.3 58.8 38.6 19.4 39.7 49.7
Mask(Inst) R101v1b-FPN 2MLP 2X 36.9 59.3 39.4 19.1 40.7 51.0
Mask(Inst) R152v1b-FPN 2MLP 1X 37.4 60.1 39.8 20.0 41.6 50.7
Mask(Inst) R152v1b-FPN 2MLP 2X 38.0 60.6 40.6 19.8 41.9 52.8
Trident R50v1b-C4 C5-128ROI 1X 38.4 59.7 41.5 21.4 43.6 52.4
Trident R50v1b-C4 C5-128ROI 2X 39.6 60.9 42.9 22.5 44.5 53.9
Trident R101v1b-C4 C5-128ROI 1X 42.2 63.6 45.3 24.5 47.2 57.7
Trident R101v1b-C4 C5-128ROI 2X 43.0 64.3 46.3 25.3 47.9 58.4
Trident R152v1b-C4 C5-128ROI 1X 43.7 64.1 48.0 26.9 47.9 58.9
Trident R152v1b-C4 C5-128ROI 2X 44.4 65.4 48.3 26.4 49.4 59.6
TridentFast R50v1b-C4 C5-128ROI 1X 37.7 58.7 40.3 19.5 42.4 52.7
TridentFast R50v1b-C4 C5-128ROI 2X 39.0 60.2 41.8 20.8 43.6 53.8
TridentFast R101v1b-C4 C5-128ROI 1X 41.1 62.5 43.9 22.1 45.7 57.7
TridentFast R101v1b-C4 C5-128ROI 2X 42.5 63.7 46.0 23.3 46.7 59.3
TridentFast R152v1b-C4 C5-128ROI 1X 42.7 64.0 45.6 23.4 47.5 59.1
TridentFast R152v1b-C4 C5-128ROI 2X 43.9 65.1 47.0 25.1 48.1 60.4
Retina R50v1-FPN 4Conv 1X 36.6 56.9 39.0 20.3 40.7 47.2
Retina R101v1-FPN 4Conv 1X 39.2 59.5 42.2 22.8 44.0 51.1
Retina R152v1-FPN 4Conv 1X 40.4 61.1 43.4 23.6 45.0 52.3
Faster R50v1b-C4-DCNv1 C5-512ROI 1X 38.8 60.0 41.3 20.6 43.3 53.2
Faster R101v1b-C4-DCNv1 C5-512ROI 1X 41.4 63.0 44.7 22.7 46.1 56.8
Faster R50v1b-C4-DCNv2 C5-512ROI 1X 39.6 60.8 42.7 20.8 43.9 54.2
Faster R50v1b-C4-DCNv2 C5-512ROI 2X 41.2 62.2 44.7 21.7 45.3 57.0
Faster R101v1b-C4-DCNv2 C5-512ROI 1X 41.7 63.0 44.7 22.8 46.1 57.3
Faster R101v1b-C4-DCNv2 C5-512ROI 2X 42.7 63.7 46.0 24.9 46.9 57.9

Box, and Mask Detection Baselines

All AP results are reported on minival2014 of the COCO dataset.

Model Backbone Head Train Schedule GPU Image/GPU FP16 Train MEM Train Speed Box AP(Mask AP) Link
Faster R50v1-C4 C5-512ROI 1X 8X 1080Ti 2 no 5.9G(4.5G) 20 img/s 34.2 model
Faster R50v1-C4 C5-512ROI 1X 8X TitanV 2 yes 6.1G 49 img/s 34.4 model
Faster R50v2-C4 C5-256ROI 1X 8X 1080Ti 2 no 5.1G 33 img/s 32.8 model
Cascade R50v2-C5 2MLP 1X 8X 1080Ti 2 no 5.9G 25 img/s 38.8 model
Cascade R50v1-FPN 2MLP 1X 8X 1080Ti 2 no 6.6G 21 img/s 40.3 model
Faster R50v1-FPN 2MLP 1X 8X 1080Ti 2 no 4.2G(2.6G) 43 img/s 36.5 model
Mask R50v1-FPN 2MLP+4CONV 1X 8X 1080Ti 2 no 5.7G(3.6G) 35 img/s 37.1(33.7) model
Retina R50v1-FPN 4Conv 1X 8X 1080Ti 2 no 4.7G(2.2G) 44 img/s 35.6 model
Trident R50v2-C4 C5-128ROI 1X 8X 1080Ti 2 no 7.0G(5.3G) 20 img/s 37.1 model
Faster R101v2-C4 C5-256ROI 1X 8X 1080Ti 2 no 6.7G 25 img/s 37.6 model
Faster-SyncBN R101v2-C4 C5-256ROI 1X 8X 1080Ti 2 no 7.8G 17 img/s 38.6 model
Faster R101v1-C4 C5-512ROI 1X 8X 1080Ti 2 no 10.2G 16 img/s 38.3 model
Faster R101v1-C4 C5-512ROI 1X 8X TitanV 2 yes 7.0G 35 img/s 38.1 model
Faster R101v1-FPN 2MLP 1X 8X 1080Ti 2 no 5.3G(3.4G) 31 img/s 38.7 model
Cascade R101v2-C5 2MLP 1X 8X 1080Ti 2 no 7.6G 22 img/s 41.0 model
Cascade R101v1-FPN 2MLP 1X 8X 1080Ti 2 no 8.7G 19 img/s 42.3 model
Trident R101v2-C4 C5-128ROI 1X 8X 1080Ti 1 no 6.6G 9 img/s 40.6 model
Trident-Fast R101v2-C4 C5-128ROI 1X 8X 1080Ti 1 no 6.6G 9 img/s 39.9 model
Retina R101v1-FPN 4Conv 1X 8X 1080Ti 2 no 5.9G(3.0G) 31 img/s 37.8 model

FP16 Speed Benchmark

Here we provide the FP16 speeed benchmark results of several models.

Model Backbone Head Train Schedule GPU Image/GPU FP16 Train MEM Train Speed
Faster R50v1-C4 C5-512ROI 1X 8X 1080Ti 2 no 8.4G 20 img/s
Faster R50v1-C4 C5-512ROI 1X 8X TitanV 2 yes 6.1G 49 img/s
Faster R50v1-C4 C5-512ROI 1X 8X TitanV 4 yes 11.2G 55 img/s
Faster R50v2-C4 C5-256ROI 1X 8X 1080Ti 2 no 5.1G 33 img/s
Faster R50v2-C4 C5-256ROI 1X 8X TitanV 2 yes 3.8G 61 img/s
Faster R50v2-C4 C5-256ROI 1X 8X TitanV 4 yes 6.6G 73 img/s
Faster R101v1-C4 C5-512ROI 1X 8X 1080Ti 2 no 10.2G 16 img/s
Faster R101v1-C4 C5-512ROI 1X 8X TitanV 2 yes 7.0G 35 img/s
Faster R50v1-FPN 2MLP 1X 8X 1080Ti 2 no 4.2G(2.6G) 43 img/s
Faster R50v1-FPN 2MLP 1X 8X 2080Ti 2 yes 3.7G(3.1G) 65 img/s
Faster R50v1-FPN 2MLP 1X 8X 2080Ti 4 yes 6.2G(6.4G) 77 img/s