OpenMMLab Detection Toolbox and Benchmark
https://mmdetection.readthedocs.io/
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1.6 KiB
1.6 KiB
Strong Baselines
We train Mask R-CNN with large-scale jitter and longer schedule as strong baselines. The modifications follow those in Detectron2.
Results and Models
| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download |
|---|---|---|---|---|---|---|---|---|
| R-50-FPN | pytorch | 50e | config | model | log | ||||
| R-50-FPN | pytorch | 100e | config | model | log | ||||
| R-50-FPN | caffe | 100e | 44.7 | 40.4 | config | model | log | ||
| R-50-FPN | caffe | 400e | config | model | log |
Notice
When using large-scale jittering, there are sometimes empty proposals in the box and mask heads during training. This requires MMSyncBN that allows empty tensors. Therefore, please use mmcv-full>=1.3.14 to train models supported in this directory.