| # TC-SemiSAM3-new | |
| Semi-supervised SAM3 model for coronary vessel segmentation. | |
| ## Model Description | |
| This is the final checkpoint from semi-supervised training using: | |
| - 5 labeled videos | |
| - 31 unlabeled videos | |
| - Mean Teacher framework with temporal consistency | |
| ## Usage | |
| ```python | |
| import torch | |
| # Load checkpoint | |
| checkpoint = torch.load("checkpoint_final.pt", map_location="cpu") | |
| # Get student model weights | |
| state_dict = checkpoint["student_state_dict"] | |
| # Load into SAM3 model | |
| model.load_state_dict(state_dict, strict=False) | |
| ``` | |
| ## Training Details | |
| - Framework: Mean Teacher + Temporal Consistency | |
| - Labeled data: 5 videos (140 frames) | |
| - Unlabeled data: 31 videos | |
| - Confidence-aware regularization enabled | |
| ## Performance | |
| | Dataset | Dice | clDice | | |
| |---------|------|--------| | |
| | 36 Training Videos | 0.6811 | 0.7492 | | |
| | 36 Videos (Adaptive) | 0.7232 | 0.7775 | | |
| ## Related | |
| - GitHub: https://github.com/qimingfan10/TC-SemiSAM.git | |