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Benchmark and Model Zoo

Environment

Hardware

  • 8 NVIDIA Tesla V100 GPUs
  • Intel Xeon 4114 CPU @ 2.20GHz

Software environment

  • Python 3.6 / 3.7
  • PyTorch 1.0
  • CUDA 9.0.176
  • CUDNN 7.0.4
  • NCCL 2.1.15

Note: The train time was measured with PyTorch 0.4.1. We will update it later, which should be about 0.02s ~ 0.05s faster.

Mirror sites

We use AWS as the main site to host our model zoo, and maintain a mirror on aliyun. You can replace https://s3.ap-northeast-2.amazonaws.com/open-mmlab with https://open-mmlab.oss-cn-beijing.aliyuncs.com in model urls.

Common settings

  • All baselines were trained using 8 GPU with a batch size of 16 (2 images per GPU).
  • All models were trained on coco_2017_train, and tested on the coco_2017_val.
  • We use distributed training and BN layer stats are fixed.
  • We adopt the same training schedules as Detectron. 1x indicates 12 epochs and 2x indicates 24 epochs, which corresponds to slightly less iterations than Detectron and the difference can be ignored.
  • All pytorch-style pretrained backbones on ImageNet are from PyTorch model zoo.
  • We report the training GPU memory as the maximum value of torch.cuda.max_memory_cached() for all 8 GPUs. Note that this value is usually less than what nvidia-smi shows, but closer to the actual requirements.
  • We report the inference time as the overall time including data loading, network forwarding and post processing.
  • The training memory and time of 2x schedule is simply copied from 1x. It should be very close to the actual memory and time.

Baselines

We released RPN, Faster R-CNN and Mask R-CNN models in the first version. More models with different backbones will be added to the model zoo.

RPN

Backbone Style Lr schd Mem (GB) Train time (s/iter) Inf time (fps) AR1000 Download
R-50-FPN caffe 1x 4.5 0.379 14.4 58.2 -
R-50-FPN pytorch 1x 4.8 0.407 14.5 57.1 model
R-50-FPN pytorch 2x 4.8 0.407 14.5 57.6 model
R-101-FPN caffe 1x 7.4 0.513 11.1 59.4 -
R-101-FPN pytorch 1x 8.0 0.552 11.1 58.6 model
R-101-FPN pytorch 2x 8.0 0.552 11.1 59.1 model
X-101-32x4d-FPN pytorch 1x 9.9 0.691 8.3 59.4 model
X-101-32x4d-FPN pytorch 2x 9.9 0.691 8.3 59.9 model
X-101-64x4d-FPN pytorch 1x 14.6 1.032 6.2 59.8 model
X-101-64x4d-FPN pytorch 2x 14.6 1.032 6.2 60.0 model

Faster R-CNN

Backbone Style Lr schd Mem (GB) Train time (s/iter) Inf time (fps) box AP Download
R-50-FPN caffe 1x 4.9 0.525 10.0 36.7 -
R-50-FPN pytorch 1x 5.1 0.554 9.9 36.4 model
R-50-FPN pytorch 2x 5.1 0.554 9.9 37.7 model
R-101-FPN caffe 1x 7.4 0.663 8.4 38.8 -
R-101-FPN pytorch 1x 8.0 0.698 8.3 38.6 model
R-101-FPN pytorch 2x 8.0 0.698 8.3 39.4 model
X-101-32x4d-FPN pytorch 1x 9.9 0.842 7.0 40.2 model
X-101-32x4d-FPN pytorch 2x 9.9 0.842 7.0 40.5 model
X-101-64x4d-FPN pytorch 1x 14.1 1.181 5.2 41.3 model
X-101-64x4d-FPN pytorch 2x 14.1 1.181 5.2 40.7 model

Mask R-CNN

Backbone Style Lr schd Mem (GB) Train time (s/iter) Inf time (fps) box AP mask AP Download
R-50-FPN caffe 1x 5.9 0.658 7.7 37.5 34.4 -
R-50-FPN pytorch 1x 5.8 0.690 7.7 37.3 34.2 model
R-50-FPN pytorch 2x 5.8 0.690 7.7 38.6 35.1 model
R-101-FPN caffe 1x 8.8 0.791 7.0 39.9 36.1 -
R-101-FPN pytorch 1x 9.1 0.825 6.7 39.4 35.9 model
R-101-FPN pytorch 2x 9.1 0.825 6.7 40.4 36.6 model
X-101-32x4d-FPN pytorch 1x 10.9 0.972 5.8 41.2 37.2 model
X-101-64x4d-FPN pytorch 2x 10.9 0.972 5.8 41.4 37.1 model
X-101-32x4d-FPN pytorch 1x 14.1 1.302 4.7 42.2 38.1 model
X-101-64x4d-FPN pytorch 2x 14.1 1.302 4.7 42.0 37.8 model

Fast R-CNN (with pre-computed proposals)

Backbone Style Type Lr schd Mem (GB) Train time (s/iter) Inf time (fps) box AP mask AP Download
R-50-FPN caffe Faster 1x 3.5 0.348 14.6 36.6 - -
R-50-FPN pytorch Faster 1x 4.0 0.375 14.5 35.8 - model
R-50-FPN pytorch Faster 2x 4.0 0.375 14.5 37.1 - model
R-101-FPN caffe Faster 1x 7.1 0.484 11.9 38.4 - -
R-101-FPN pytorch Faster 1x 7.6 0.540 11.8 38.1 - model
R-101-FPN pytorch Faster 2x 7.6 0.540 11.8 38.8 - model
R-50-FPN caffe Mask 1x 5.4 0.473 10.7 37.3 34.5 -
R-50-FPN pytorch Mask 1x 5.3 0.504 10.6 36.8 34.1 model
R-50-FPN pytorch Mask 2x 5.3 0.504 10.6 37.9 34.8 model
R-101-FPN caffe Mask 1x 8.6 0.607 9.5 39.4 36.1 -
R-101-FPN pytorch Mask 1x 9.0 0.656 9.3 38.9 35.8 model
R-101-FPN pytorch Mask 2x 9.0 0.656 9.3 39.9 36.4 model

RetinaNet

Backbone Style Lr schd Mem (GB) Train time (s/iter) Inf time (fps) box AP Download
R-50-FPN caffe 1x 6.7 0.468 9.4 35.8 -
R-50-FPN pytorch 1x 6.9 0.496 9.1 35.6 model
R-50-FPN pytorch 2x 6.9 0.496 9.1 36.5 model
R-101-FPN caffe 1x 9.2 0.614 8.2 37.8 -
R-101-FPN pytorch 1x 9.6 0.643 8.1 37.7 model
R-101-FPN pytorch 2x 9.6 0.643 8.1 38.1 model
X-101-32x4d-FPN pytorch 1x 10.8 0.792 6.7 38.7 model
X-101-32x4d-FPN pytorch 2x 10.8 0.792 6.7 39.3 model
X-101-64x4d-FPN pytorch 1x 14.6 1.128 5.3 40.0 model
X-101-64x4d-FPN pytorch 2x 14.6 1.128 5.3 39.6 model

Cascade R-CNN

Backbone Style Lr schd Mem (GB) Train time (s/iter) Inf time (fps) box AP Download
R-50-FPN caffe 1x 5.0 0.592 8.1 40.3 -
R-50-FPN pytorch 1x 5.5 0.622 8.0 40.3 model
R-50-FPN pytorch 20e 5.5 0.622 8.0 41.1 model
R-101-FPN caffe 1x 8.5 0.731 7.0 42.2 -
R-101-FPN pytorch 1x 8.7 0.766 6.9 42.1 model
R-101-FPN pytorch 20e 8.7 0.766 6.9 42.6 model
X-101-32x4d-FPN pytorch 1x 10.6 0.902 5.7 43.5 model
X-101-32x4d-FPN pytorch 20e 10.6 0.902 5.7 44.1 model
X-101-64x4d-FPN pytorch 1x 14.1 1.251 4.6 44.6 model
X-101-64x4d-FPN pytorch 20e 14.1 1.251 4.6 44.8 model

Cascade Mask R-CNN

Backbone Style Lr schd Mem (GB) Train time (s/iter) Inf time (fps) box AP mask AP Download
R-50-FPN caffe 1x 7.5 0.880 5.8 41.0 35.6 -
R-50-FPN pytorch 1x 7.6 0.910 5.7 41.3 35.7 model
R-50-FPN pytorch 20e 7.6 0.910 5.7 42.4 36.6 model
R-101-FPN caffe 1x 10.5 1.024 5.3 43.1 37.3 -
R-101-FPN pytorch 1x 10.9 1.055 5.2 42.7 37.1 model
R-101-FPN pytorch 20e 10.9 1.055 5.2 43.4 37.6 model
X-101-32x4d-FPN pytorch 1x 12.67 1.181 4.2 44.4 38.3 model
X-101-32x4d-FPN pytorch 20e 12.67 1.181 4.2 44.9 38.7 model
X-101-64x4d-FPN pytorch 1x 10.87 1.125 3.6 45.5 39.2 model
X-101-64x4d-FPN pytorch 20e 10.87 1.125 3.6 45.8 39.5 model

Notes:

  • The 20e schedule in Cascade (Mask) R-CNN indicates decreasing the lr at 16 and 19 epochs, with a total of 20 epochs.
  • Cascade Mask R-CNN with X-101-64x4d-FPN was trained using 16 GPU with a batch size of 16 (1 images per GPU).

SSD

Backbone Size Style Lr schd Mem (GB) Train time (s/iter) Inf time (fps) box AP Download
VGG16 300 caffe 120e 3.5 0.286 22.9 / 29.2 25.7 model
VGG16 512 caffe 120e 6.3 0.458 17.3 / 21.2 29.3 model

SSD (PASCAL VOC)

Backbone Size Style Lr schd Mem (GB) Train time (s/iter) Inf time (fps) box AP Download
VGG16 300 caffe 240e 1.2 0.189 40.1 / 58.0 77.8 model
VGG16 512 caffe 240e 2.9 0.261 28.1 / 36.2 80.4 model

Notes:

  • cudnn.benchmark is set as True for SSD training and testing.
  • Inference time is reported for batch size = 1 and batch size = 8.
  • The speed difference between VOC and COCO is caused by model parameters and nms.

Group Normalization (GN)

Backbone model Lr schd Mem (GB) Train time (s/iter) Inf time (fps) box AP mask AP Download
R-50-FPN (d) Mask R-CNN 2x 7.2 0.806 5.4 39.9 36.1 model
R-50-FPN (d) Mask R-CNN 3x 7.2 0.806 5.4 40.2 36.5 model
R-101-FPN (d) Mask R-CNN 2x 9.9 0.970 4.8 41.6 37.1 model
R-101-FPN (d) Mask R-CNN 3x 9.9 0.970 4.8 41.7 37.3 model
R-50-FPN (c) Mask R-CNN 2x 7.2 0.806 5.4 39.7 35.9 model
R-50-FPN (c) Mask R-CNN 3x 7.2 0.806 5.4 40.1 36.2 model

Notes:

  • (d) means pretrained model converted from Detectron, and (c) means the contributed model pretrained by @thangvubk.
  • The 3x schedule is epoch [28, 34, 36].
  • The memory is measured with torch.cuda.max_memory_allocated() instead of torch.cuda.max_memory_cached(). We will update the memory usage of other models in the future.

Deformable Convolution v2

Backbone Model Style Conv Pool Lr schd Mem (GB) Train time (s/iter) Inf time (fps) box AP mask AP Download
R-50-FPN Faster pytorch dconv(c3-c5) - 1x 3.9 0.594 10.2 40.0 - model
R-50-FPN Faster pytorch mdconv(c3-c5) - 1x 3.7 0.598 10.0 40.3 - model
R-50-FPN Faster pytorch - dpool 1x 4.6 0.714 8.7 37.9 - model
R-50-FPN Faster pytorch - mdpool 1x 5.2 0.769 8.2 38.1 - model
R-101-FPN Faster pytorch dconv(c3-c5) - 1x 5.8 0.811 8.0 42.1 - model
X-101-32x4d-FPN Faster pytorch dconv(c3-c5) - 1x 7.1 1.126 6.6 43.5 - model
R-50-FPN Mask pytorch dconv(c3-c5) - 1x 4.5 0.712 7.7 41.1 37.2 model
R-50-FPN Mask pytorch mdconv(c3-c5) - 1x 4.5 0.712 7.7 41.4 37.4 model
R-101-FPN Mask pytorch dconv(c3-c5) - 1x 6.4 0.939 6.5 43.2 38.7 model
R-50-FPN Cascade pytorch dconv(c3-c5) - 1x 4.4 0.660 7.6 44.1 - model
R-101-FPN Cascade pytorch dconv(c3-c5) - 1x 6.3 0.881 6.8 45.1 - model
R-50-FPN Cascade Mask pytorch dconv(c3-c5) - 1x 6.6 0.942 5.7 44.5 38.3 model
R-101-FPN Cascade Mask pytorch dconv(c3-c5) - 1x 8.5 1.156 5.1 45.8 39.5 model

Notes:

  • dconv and mdconv denote (modulated) deformable convolution, c3-c5 means adding dconv in resnet stage 3 to 5. dpool and mdpool denote (modulated) deformable roi pooling.
  • The memory is measured with torch.cuda.max_memory_allocated(). The batch size is 16 (2 images per GPU).
  • The dcn ops are modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch, which should be more memory efficient and slightly faster.

Comparison with Detectron

We compare mmdetection with Detectron and Detectron.pytorch, a third-party port of Detectron to Pytorch. The backbone used is R-50-FPN.

In general, mmdetection has 3 advantages over Detectron.

  • Higher performance (especially in terms of mask AP)
  • Faster training speed
  • Memory efficient

Performance

Detectron and Detectron.pytorch use caffe-style ResNet as the backbone. In order to utilize the PyTorch model zoo, we use pytorch-style ResNet in our experiments.

In the meanwhile, we train models with caffe-style ResNet in 1x experiments for comparison. We find that pytorch-style ResNet usually converges slower than caffe-style ResNet, thus leading to slightly lower results in 1x schedule, but the final results of 2x schedule is higher.

We report results using both caffe-style (weights converted from here) and pytorch-style (weights from the official model zoo) ResNet backbone, indicated as pytorch-style results / caffe-style results.

Type Lr schd Detectron Detectron.pytorch mmdetection
RPN 1x 57.2 - 57.1 / 58.2
2x - - 57.6 / -
Faster R-CNN 1x 36.7 37.1 36.4 / 36.7
2x 37.9 - 37.7 / -
Mask R-CNN 1x 37.7 & 33.9 37.7 & 33.7 37.3 & 34.2 / 37.5 & 34.4
2x 38.6 & 34.5 - 38.6 & 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 / -

Training Speed

The training speed is measure with s/iter. The lower, the better.

Type Detectron (P1001) Detectron.pytorch (XP2) mmdetection3 (V1004 / XP)
RPN 0.416 - 0.407 / 0.413
Faster R-CNN 0.544 1.015 0.554 / 0.579
Mask R-CNN 0.889 1.435 0.690 / 0.732
Fast R-CNN 0.285 - 0.375 / 0.398
Fast R-CNN (w/mask) 0.377 - 0.504 / 0.574

*1. Detectron reports the speed on Facebook's Big Basin servers (P100), on our V100 servers it is slower so we use the official reported values.

*2. Detectron.pytorch does not report the runtime and we encountered some issue to run it on V100, so we report the speed on TITAN XP.

*3. The speed of pytorch-style ResNet is approximately 5% slower than caffe-style, and we report the pytorch-style results here.

*4. We also run the models on a DGX-1 server (P100) and the speed is almost the same as our V100 servers.

Inference Speed

The inference speed is measured with fps (img/s) on a single GPU. The higher, the better.

Type Detectron (P100) Detectron.pytorch (XP) mmdetection (V100 / XP)
RPN 12.5 - 14.5 / 15.4
Faster R-CNN 10.3 9.9 / 9.8
Mask R-CNN 8.5 7.7 / 7.4
Fast R-CNN 12.5 14.5 / 14.1
Fast R-CNN (w/mask) 9.9 10.6 / 10.3

Training memory

We perform various tests and there is no doubt that mmdetection is more memory efficient than Detectron, and the main cause is the deep learning framework itself, not our efforts. Besides, Caffe2 and PyTorch have different apis to obtain memory usage whose implementation is not exactly the same.

nvidia-smi shows a larger memory usage for both detectron and mmdetection, e.g., we observe a much higher memory usage when we train Mask R-CNN with 2 images per GPU using detectron (10.6G) and mmdetection (9.3G), which is obviously more than actually required.

With mmdetection, we can train R-50 FPN Mask R-CNN with 4 images per GPU (TITAN XP, 12G), which is a promising result.