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