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README.md

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mask_rcnn_r50_caffe_fpn_syncbn-all_rpn-2conv_lsj_100e_coco.py

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mask_rcnn_r50_caffe_fpn_syncbn-all_rpn-2conv_lsj_400e_coco.py

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mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_lsj_100e_coco.py

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mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_lsj_100e_fp16_coco.py

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README.md

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.