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Detectron2 model zoo's experimental settings and a few implementation details are different from Detectron.

The differences in implementation details are shared in Compatibility with Other Libraries.

The differences in model zoo's experimental settings include:

  • Use scale augmentation during training. This improves AP with lower training cost.
  • Use L1 loss instead of smooth L1 loss for simplicity. This sometimes improves box AP but may affect other AP.
  • Use POOLER_SAMPLING_RATIO=0 instead of 2. This does not significantly affect AP.
  • Use ROIAlignV2. This does not significantly affect AP.

In this directory, we provide a few configs that do not have the above changes. They mimic Detectron's behavior as close as possible, and provide a fair comparison of accuracy and speed against Detectron.

Name lr
sched
train
time
(s/iter)
inference
time
(s/im)
train
mem
(GB)
box
AP
mask
AP
kp.
AP
model id download
Faster R-CNN 1x 0.219 0.038 3.1 36.9 137781054 model | metrics
Keypoint R-CNN 1x 0.313 0.071 5.0 53.1 64.2 137781195 model | metrics
Mask R-CNN 1x 0.273 0.043 3.4 37.8 34.9 137781281 model | metrics

Comparisons:

  • Faster R-CNN: Detectron's AP is 36.7, similar to ours.
  • Keypoint R-CNN: Detectron's AP is box 53.6, keypoint 64.2. Fixing a Detectron's bug lead to a drop in box AP, and can be compensated back by some parameter tuning.
  • Mask R-CNN: Detectron's AP is box 37.7, mask 33.9. We're 1 AP better in mask AP, due to more correct implementation. See this article for details.

For speed comparison, see benchmarks.