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Model Zoo and Baselines

Hardware

  • 8 NVIDIA V100 GPUs

Software

  • PyTorch version: 1.0.0a0+dd2c487
  • CUDA 9.2
  • CUDNN 7.1
  • NCCL 2.2.13-1

End-to-end Faster and Mask R-CNN baselines

All the baselines were trained using the exact same experimental setup as in Detectron. We initialize the detection models with ImageNet weights from Caffe2, the same as used by Detectron.

The pre-trained models are available in the link in the model id.

backbone type lr sched im / gpu train mem(GB) train time (s/iter) total train time(hr) inference time(s/im) box AP mask AP model id
R-50-C4 Fast 1x 1 5.8 0.4036 20.2 0.17130 34.8 - 6358800
R-50-FPN Fast 1x 2 4.4 0.3530 8.8 0.12580 36.8 - 6358793
R-101-FPN Fast 1x 2 7.1 0.4591 11.5 0.143149 39.1 - 6358804
X-101-32x8d-FPN Fast 1x 1 7.6 0.7007 35.0 0.209965 41.2 - 6358717
R-50-C4 Mask 1x 1 5.8 0.4520 22.6 0.17796 + 0.028 35.6 31.5 6358801
R-50-FPN Mask 1x 2 5.2 0.4536 11.3 0.12966 + 0.034 37.8 34.2 6358792
R-101-FPN Mask 1x 2 7.9 0.5665 14.2 0.15384 + 0.034 40.1 36.1 6358805
X-101-32x8d-FPN Mask 1x 1 7.8 0.7562 37.8 0.21739 + 0.034 42.2 37.8 6358718

For person keypoint detection:

backbone type lr sched im / gpu train mem(GB) train time (s/iter) total train time(hr) inference time(s/im) box AP keypoint AP model id
R-50-FPN Keypoint 1x 2 5.7 0.3771 9.4 0.10941 53.7 64.3 9981060

Light-weight Model baselines

We provided pre-trained models for selected FBNet models.

  • All the models are trained from scratched with BN using the training schedule specified below.
  • Evaluation is performed on a single NVIDIA V100 GPU with MODEL.RPN.POST_NMS_TOP_N_TEST set to 200.

The following inference time is reported:

  • inference total batch=8: Total inference time including data loading, model inference and pre/post preprocessing using 8 images per batch.
  • inference model batch=8: Model inference time only and using 8 images per batch.
  • inference model batch=1: Model inference time only and using 1 image per batch.
  • inferenee caffe2 batch=1: Model inference time for the model in Caffe2 format using 1 image per batch. The Caffe2 models fused the BN to Conv and purely run on C++/CUDA by using Caffe2 ops for rpn/detection post processing.

The pre-trained models are available in the link in the model id.

backbone type resolution lr sched im / gpu train mem(GB) train time (s/iter) total train time (hr) inference total batch=8 (s/im) inference model batch=8 (s/im) inference model batch=1 (s/im) inference caffe2 batch=1 (s/im) box AP mask AP model id
R-50-C4 (reference) Fast 800 1x 1 5.8 0.4036 20.2 0.0875 0.0793 0.0831 0.0625 34.4 - f35857197
fbnet_chamv1a Fast 600 0.75x 12 13.6 0.5444 20.5 0.0315 0.0260 0.0376 0.0188 33.5 - f100940543
fbnet_default Fast 600 0.5x 16 11.1 0.4872 12.5 0.0316 0.0250 0.0297 0.0130 28.2 - f101086388
R-50-C4 (reference) Mask 800 1x 1 5.8 0.452 22.6 0.0918 0.0848 0.0844 - 35.2 31.0 f35858791
fbnet_xirb16d Mask 600 0.5x 16 13.4 1.1732 29 0.0386 0.0319 0.0356 - 30.7 26.9 f101086394
fbnet_default Mask 600 0.5x 16 13.0 0.9036 23.0 0.0327 0.0269 0.0385 - 29.0 26.1 f101086385

Comparison with Detectron and mmdetection

In the following section, we compare our implementation with Detectron and mmdetection. The same remarks from mmdetection about different hardware applies here.

Training speed

The numbers here are in seconds / iteration. The lower, the better.

type Detectron (P100) mmdetection (V100) maskrcnn_benchmark (V100)
Faster R-CNN R-50 C4 0.566 - 0.4036
Faster R-CNN R-50 FPN 0.544 0.554 0.3530
Faster R-CNN R-101 FPN 0.647 - 0.4591
Faster R-CNN X-101-32x8d FPN 0.799 - 0.7007
Mask R-CNN R-50 C4 0.620 - 0.4520
Mask R-CNN R-50 FPN 0.889 0.690 0.4536
Mask R-CNN R-101 FPN 1.008 - 0.5665
Mask R-CNN X-101-32x8d FPN 0.961 - 0.7562

Training memory

The lower, the better

type Detectron (P100) mmdetection (V100) maskrcnn_benchmark (V100)
Faster R-CNN R-50 C4 6.3 - 5.8
Faster R-CNN R-50 FPN 7.2 4.9 4.4
Faster R-CNN R-101 FPN 8.9 - 7.1
Faster R-CNN X-101-32x8d FPN 7.0 - 7.6
Mask R-CNN R-50 C4 6.6 - 5.8
Mask R-CNN R-50 FPN 8.6 5.9 5.2
Mask R-CNN R-101 FPN 10.2 - 7.9
Mask R-CNN X-101-32x8d FPN 7.7 - 7.8

Accuracy

The higher, the better

type Detectron (P100) mmdetection (V100) maskrcnn_benchmark (V100)
Faster R-CNN R-50 C4 34.8 - 34.8
Faster R-CNN R-50 FPN 36.7 36.7 36.8
Faster R-CNN R-101 FPN 39.4 - 39.1
Faster R-CNN X-101-32x8d FPN 41.3 - 41.2
Mask R-CNN R-50 C4 35.8 & 31.4 - 35.6 & 31.5
Mask R-CNN R-50 FPN 37.7 & 33.9 37.5 & 34.4 37.8 & 34.2
Mask R-CNN R-101 FPN 40.0 & 35.9 - 40.1 & 36.1
Mask R-CNN X-101-32x8d FPN 42.1 & 37.3 - 42.2 & 37.8