Add tram-v2 model card

README.md

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+---
+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
+
+```bash
+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](https://huggingface.co/aghoraguru/tram-v1) · [tram-v2](https://huggingface.co/aghoraguru/tram-v2) · [tram-v3](https://huggingface.co/aghoraguru/tram-v3)