RPC-Leaderboard

The RPC dataset leaderboard

Experimental settings are consistent with the settings in the RPC paper: 53k single exemplar images for training, and 24k checkout images for test.

Method cAcc mCIoU ACD mCCD mAP50 mmAP link
CommNet v2 93.11% 99.22% 0.09 0.01 98.92% 81.20% detail
Eleme: Syn+Render 92.20% 99.22% 0.09 0.01 99.04% 83.86% detail
DPNet: Syn+Render 80.51% 97.33% 0.34 0.03 97.91% 77.04% detail, paper
CommNet: Syn+Render 75.93% 96.84% 0.39 0.03 97.41% 75.78% detail
Baseline: Syn+Render 56.68% 93.19% 0.89 0.07 96.57% 73.83% detail, project
Baseline: Render 45.60% 90.58% 1.25 0.10 95.50% 72.76% detail, project
Baseline: Syn 9.27% 69.65% 4.27 0.35 80.66% 53.08% detail, project

If you have been successful in creating a model based on the training set and it performs well on the validation set, we encourage you to run your model on the test set. The rpctool will contribute to return the corresponding results of the evaluation metrics. You can submit your results on the RPC leaderboard by creating a new issue. Your results will be ranked in the leaderboard and to benchmark your approach against that of other machine learners. We are looking forward to your submission. Please click RPC-Dataset/RPC-Leaderboard/issues