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Detectron2 Model Zoo and Baselines

Introduction

This file documents a large collection of baselines trained with detectron2 in Sep-Oct, 2019. The corresponding configurations for all models can be found under the configs/ directory. Unless otherwise noted, the following settings are used for all runs:

Common Settings

  • All models were trained on Big Basin servers with 8 NVIDIA V100 GPUs, with data-parallel sync SGD and a total minibatch size of 16 images.

  • All models were trained with CUDA 9.2, cuDNN 7.4.2 or 7.6.3 (the difference in speed is found to be negligible).

  • Training curves and other statistics can be found in metrics for each model.

  • The default settings are not directly comparable with Detectron. For example, our default training data augmentation uses scale jittering in addition to horizontal flipping.

    For configs that are comparable to Detectron's settings, see Detectron1-Comparisons for accuracy comparison, and benchmarks for speed comparison.

  • Inference speed is measured by tools/train_net.py --eval-only, or inference_on_dataset(), with batch size 1 in detectron2 directly. The actual deployment should in general be faster than the given inference speed due to more optimizations.

  • Training speed is averaged across the entire training. We keep updating the speed with latest version of detectron2/pytorch/etc., so they might be different from the metrics file.

  • All COCO models were trained on train2017 and evaluated on val2017.

  • For Faster/Mask R-CNN, we provide baselines based on 3 different backbone combinations:

    • FPN: Use a ResNet+FPN backbone with standard conv and FC heads for mask and box prediction, respectively. It obtains the best speed/accuracy tradeoff, but the other two are still useful for research.
    • C4: Use a ResNet conv4 backbone with conv5 head. The original baseline in the Faster R-CNN paper.
    • DC5 (Dilated-C5): Use a ResNet conv5 backbone with dilations in conv5, and standard conv and FC heads for mask and box prediction, respectively. This is used by the Deformable ConvNet paper.

  • Most models are trained with the 3x schedule (~37 COCO epochs). Although 1x models are heavily under-trained, we provide some ResNet-50 models with the 1x (~12 COCO epochs) training schedule for comparison when doing quick research iteration.

  • The model id column is provided for ease of reference. To check downloaded file integrity, any model on this page contains its md5 prefix in its file name. Each model also comes with a metrics file with all the training statistics and evaluation curves.

ImageNet Pretrained Models

We provide backbone models pretrained on ImageNet-1k dataset. These models are different from those provided in Detectron: we do not fuse BatchNorm into an affine layer.

  • R-50.pkl: converted copy of MSRA's original ResNet-50 model
  • R-101.pkl: converted copy of MSRA's original ResNet-101 model
  • X-101-32x8d.pkl: ResNeXt-101-32x8d model trained with Caffe2 at FB

Pretrained models in Detectron's format can still be used. For example:

  • X-152-32x8d-IN5k.pkl: ResNeXt-152-32x8d model trained on ImageNet-5k with Caffe2 at FB (see ResNeXt paper for details on ImageNet-5k).
  • R-50-GN.pkl: ResNet-50 with Group Normalization.
  • R-101-GN.pkl: ResNet-101 with Group Normalization.

License

All models available for download through this document are licensed under the Creative Commons Attribution-ShareAlike 3.0 license.

COCO Object Detection Baselines

Faster R-CNN:

Name lr
sched		 train
time
(s/iter)		 inference
time
(s/im)		 train
mem
(GB)		 box
AP		 model id download
R50-C4 1x 0.551 0.110 4.8 35.7 137257644 model | metrics
R50-DC5 1x 0.380 0.068 5.0 37.3 137847829 model | metrics
R50-FPN 1x 0.210 0.055 3.0 37.9 137257794 model | metrics
R50-C4 3x 0.543 0.110 4.8 38.4 137849393 model | metrics
R50-DC5 3x 0.378 0.073 5.0 39.0 137849425 model | metrics
R50-FPN 3x 0.209 0.047 3.0 40.2 137849458 model | metrics
R101-C4 3x 0.619 0.149 5.9 41.1 138204752 model | metrics
R101-DC5 3x 0.452 0.082 6.1 40.6 138204841 model | metrics
R101-FPN 3x 0.286 0.063 4.1 42.0 137851257 model | metrics
X101-FPN 3x 0.638 0.120 6.7 43.0 139173657 model | metrics

RetinaNet:

Name lr
sched		 train
time
(s/iter)		 inference
time
(s/im)		 train
mem
(GB)		 box
AP		 model id download
R50 1x 0.200 0.062 3.9 36.5 137593951 model | metrics
R50 3x 0.201 0.063 3.9 37.9 137849486 model | metrics
R101 3x 0.280 0.080 5.1 39.9 138363263 model | metrics

RPN & Fast R-CNN:

Name lr
sched		 train
time
(s/iter)		 inference
time
(s/im)		 train
mem
(GB)		 box
AP		 prop.
AR		 model id download
RPN R50-C4 1x 0.130 0.051 1.5 51.6 137258005 model | metrics
RPN R50-FPN 1x 0.186 0.045 2.7 58.0 137258492 model | metrics
Fast R-CNN R50-FPN 1x 0.140 0.035 2.6 37.8 137635226 model | metrics

COCO Instance Segmentation Baselines with Mask R-CNN

Name lr
sched		 train
time
(s/iter)		 inference
time
(s/im)		 train
mem
(GB)		 box
AP		 mask
AP		 model id download
R50-C4 1x 0.584 0.117 5.2 36.8 32.2 137259246 model | metrics
R50-DC5 1x 0.471 0.074 6.5 38.3 34.2 137260150 model | metrics
R50-FPN 1x 0.261 0.053 3.4 38.6 35.2 137260431 model | metrics
R50-C4 3x 0.575 0.118 5.2 39.8 34.4 137849525 model | metrics
R50-DC5 3x 0.470 0.075 6.5 40.0 35.9 137849551 model | metrics
R50-FPN 3x 0.261 0.055 3.4 41.0 37.2 137849600 model | metrics
R101-C4 3x 0.652 0.155 6.3 42.6 36.7 138363239 model | metrics
R101-DC5 3x 0.545 0.155 7.6 41.9 37.3 138363294 model | metrics
R101-FPN 3x 0.340 0.070 4.6 42.9 38.6 138205316 model | metrics
X101-FPN 3x 0.690 0.129 7.2 44.3 39.5 139653917 model | metrics

COCO Person Keypoint Detection Baselines with Keypoint R-CNN

Name lr
sched		 train
time
(s/iter)		 inference
time
(s/im)		 train
mem
(GB)		 box
AP		 kp.
AP		 model id download
R50-FPN 1x 0.315 0.083 5.0 53.6 64.0 137261548 model | metrics
R50-FPN 3x 0.316 0.076 5.0 55.4 65.5 137849621 model | metrics
R101-FPN 3x 0.390 0.090 6.1 56.4 66.1 138363331 model | metrics
X101-FPN 3x 0.738 0.142 8.7 57.3 66.0 139686956 model | metrics

COCO Panoptic Segmentation Baselines with Panoptic FPN

Name lr
sched		 train
time
(s/iter)		 inference
time
(s/im)		 train
mem
(GB)		 box
AP		 mask
AP		 PQ model id download
R50-FPN 1x 0.304 0.063 4.8 37.6 34.7 39.4 139514544 model | metrics
R50-FPN 3x 0.302 0.063 4.8 40.0 36.5 41.5 139514569 model | metrics
R101-FPN 3x 0.392 0.078 6.0 42.4 38.5 43.0 139514519 model | metrics

LVIS Instance Segmentation Baselines with Mask R-CNN

Mask R-CNN baselines on the LVIS dataset, v0.5. These baselines are described in Table 3(c) of the LVIS paper.

NOTE: the 1x schedule here has the same amount of iterations as the COCO 1x baselines. They are roughly 24 epochs of LVISv0.5 data. The final results of these configs have large variance across different runs.

Name lr
sched		 train
time
(s/iter)		 inference
time
(s/im)		 train
mem
(GB)		 box
AP		 mask
AP		 model id download
R50-FPN 1x 0.292 0.127 7.1 23.6 24.4 144219072 model | metrics
R101-FPN 1x 0.371 0.124 7.8 25.6 25.9 144219035 model | metrics
X101-FPN 1x 0.712 0.166 10.2 26.7 27.1 144219108 model | metrics

Cityscapes & Pascal VOC Baselines

Simple baselines for

  • Mask R-CNN on Cityscapes instance segmentation (initialized from COCO pre-training, then trained on Cityscapes fine annotations only)
  • Faster R-CNN on PASCAL VOC object detection (trained on VOC 2007 train+val + VOC 2012 train+val, tested on VOC 2007 using 11-point interpolated AP)
Name train
time
(s/iter)		 inference
time
(s/im)		 train
mem
(GB)		 box
AP		 box
AP50		 mask
AP		 model id download
R50-FPN, Cityscapes 0.240 0.092 4.4 36.5 142423278 model | metrics
R50-C4, VOC 0.537 0.086 4.8 51.9 80.3 142202221 model | metrics

Other Settings

Ablations for Deformable Conv and Cascade R-CNN:

Name lr
sched		 train
time
(s/iter)		 inference
time
(s/im)		 train
mem
(GB)		 box
AP		 mask
AP		 model id download
Baseline R50-FPN 1x 0.261 0.053 3.4 38.6 35.2 137260431 model | metrics
Deformable Conv 1x 0.342 0.061 3.5 41.5 37.5 138602867 model | metrics
Cascade R-CNN 1x 0.317 0.066 4.0 42.1 36.4 138602847 model | metrics
Baseline R50-FPN 3x 0.261 0.055 3.4 41.0 37.2 137849600 model | metrics
Deformable Conv 3x 0.349 0.066 3.5 42.7 38.5 144998336 model | metrics
Cascade R-CNN 3x 0.328 0.075 4.0 44.3 38.5 144998488 model | metrics

Ablations for normalization methods: (Note: The baseline uses 2fc head while the others use 4conv1fc head. According to the GroupNorm paper, the change in head does not improve the baseline by much)

Name lr
sched		 train
time
(s/iter)		 inference
time
(s/im)		 train
mem
(GB)		 box
AP		 mask
AP		 model id download
Baseline R50-FPN 3x 0.261 0.055 3.4 41.0 37.2 137849600 model | metrics
SyncBN 3x 0.464 0.063 5.6 42.0 37.8 143915318 model | metrics
GN 3x 0.356 0.077 7.3 42.6 38.6 138602888 model | metrics
GN (scratch) 3x 0.400 0.077 9.8 39.9 36.6 138602908 model | metrics

A few very large models trained for a long time, for demo purposes:

Name inference
time
(s/im)		 train
mem
(GB)		 box
AP		 mask
AP		 PQ model id download
Panoptic FPN R101 0.123 11.4 47.4 41.3 46.1 139797668 model | metrics
Mask R-CNN X152 0.281 15.1 49.3 43.2 18131413 model | metrics
above + test-time aug. 51.4 45.5