metadata
license: cc-by-4.0
tags:
- object-detection
- instance-segmentation
- mask-rcnn
- swin-transformer
- sonar
- underwater
library_name: pytorch
pipeline_tag: object-detection
TRAM-V2
Reference checkpoint for TRAM-V2 from the paper TRAM: Transformer-Based Mask R-CNN Framework for Underwater Object Detection in Side-Scan Sonar Data (Makam, Sundaram, & Sundaram).
- Architecture: MST (Swin-Base) + FPN + CBAM + Mask R-CNN
- Input: 224×224 RGB
- Training data: SSS_OD-5 (SeabedObjects-KLSG-derived, plane + ship)
- Best epoch: 27 / 47
- Random seed: not fixed
Validation metrics (this run)
| Metric | This run | Paper |
|---|---|---|
| Det mAP@0.5 | 0.8356 | 0.8321 |
| Det mAP@0.5:0.95 | 0.5496 | 0.5293 |
| Seg mAP@0.5 | 0.7538 | 0.7352 |
| Seg mAP@0.5:0.95 | 0.4244 | 0.4412 |
Usage
git clone -b final-tram-v123 https://github.com/CrypticCortex/iisc-sss-codes.git
cd iisc-sss-codes
pip install -r final/requirements.txt
python -m final.tram_v2.inference \
--weights /path/to/tram_v2_best.pth \
--data-root /path/to/SSS_OD-5/valid \
--output-dir runs/tram-v2/inference \
--evaluate-map
Files
tram_v2_best.pth— best checkpoint (highest val bbox mAP@0.5:0.95)training.log— per-epoch training/validation log
Source
- Code: https://github.com/CrypticCortex/iisc-sss-codes/tree/final-tram-v123/final
- Other variants: tram-v1 · tram-v2 · tram-v3