Blame for README.md

---
license: cc-by-nc-4.0
tags:
  - object-detection
  - underwater
  - synthetic-data
  - domain-randomization
  - yolo
---

# MSc Dissertation — Synthetic Underwater Imagery & Sim-to-Real Detection

Supporting data and model checkpoints for an MSc dissertation on synthetic benthic-survey
imagery generation and sim-to-real object detection. Benthic scenes are generated with
diffusion models (Flux.2-dev, Hunyuan), domain-randomised through a Jerlov water-column
physics model, then used to train YOLO detectors evaluated zero-shot against the real
[DUO dataset](https://github.com/chongweiliu/DUO).

Full pipeline code, configs, and training scripts: **[github.com/rithish007/ImgGen_AI](https://github.com/rithish007/ImgGen_AI)**
(MIT license). The descoped Unreal Engine 5.8 simulator arm that preceded this pipeline is
archived at [github.com/rithish007/Shallow_Seabed](https://github.com/rithish007/Shallow_Seabed)
(MIT license).

Classes: `starfish`, `sea_urchin`, `scallop`.

## Contents

```
datasets/
  flux2dev/
    base/            1000 images — Flux.2-dev v9 generations, no domain randomization
    placeholder_dr/  2000 images — base set + placeholder-DR copies
    duo_scatter_dr/  2000 images — base set + DUO-calibrated-scatter DR copies (used to train the best model)
    duo_dr/          2000 images — base set + DUO-calibrated DR copies
  hunyuan/            892 images — Hunyuan v8 generations (generated only; not yet trained or evaluated — see note below)

weights/
  flux2dev_base.pt               YOLO trained on datasets/flux2dev/base
  flux2dev_placeholder_dr.pt     YOLO trained on datasets/flux2dev/placeholder_dr
  flux2dev_duo_scatter_dr_BEST.pt  YOLO trained on datasets/flux2dev/duo_scatter_dr — best DUO mAP50 so far
  flux2dev_duo_dr.pt             YOLO trained on datasets/flux2dev/duo_dr

duo_test_detections/  Predictions of flux2dev_duo_scatter_dr_BEST.pt on the real DUO test set
  *.jpg                images with predicted boxes drawn
  labels/*.txt         raw YOLO-format predicted boxes (class x_center y_center width height conf)

datasets/shallowseabed/  DESCOPED -- see "ShallowSeabed" section below. Different classes
                         (coral/kelp/rock/sponge), from the Unreal Engine simulator this
                         project moved away from, kept for the reproducibility record only.
  base/              500 images (train+val) -- original UE5.8 render, no domain randomization
  dr_transformed/    500 images (train+val), same frames/labels -- a DR-transformed render

weights/
  shallowseabed_no_aug.pt              YOLO trained on datasets/shallowseabed/base, no augmentation
  shallowseabed_with_aug_BEST.pt       YOLO trained on datasets/shallowseabed/base, with augmentation -- best of the 4
  shallowseabed_no_aug_dr_trans.pt     YOLO trained on datasets/shallowseabed/dr_transformed, no augmentation
  shallowseabed_with_aug_dr_trans.pt   YOLO trained on datasets/shallowseabed/dr_transformed, with augmentation
```

Each `datasets/flux2dev/*` and `datasets/shallowseabed/*` folder is a standard YOLO layout
(`images/train`, `images/val`, `labels/train`, `labels/val`, `data.yaml`).

## Results: zero-shot evaluation on real DUO test set

All four models are trained only on synthetic data and evaluated with no fine-tuning on
real imagery.

| Variant | Train images | DUO mAP50 | DUO mAP50-95 |
|---|---|---|---|
| flux2dev_base | 900 | 10.31% | 4.78% |
| flux2dev_placeholder_dr | 1800 | 10.40% | 5.21% |
| flux2dev_duo_dr | 1800 | 11.21% | 5.84% |
| **flux2dev_duo_scatter_dr (best)** | 1800 | **11.82%** | **6.18%** |

Domain randomization calibrated against real DUO water statistics (`duo_dr`, `duo_scatter_dr`)
consistently outperforms uncalibrated placeholder randomization, which in turn outperforms
no randomization at all.

## ShallowSeabed — descoped Unreal Engine simulator arm

**This dataset was descoped and is not part of the reported pipeline.** Before adopting the
diffusion-model approach (Flux.2-dev/Hunyuan) documented above, this project built an Unreal
Engine 5.8 underwater survey simulator with a custom stencil-based instance annotation
pipeline — see [github.com/rithish007/Shallow_Seabed](https://github.com/rithish007/Shallow_Seabed).
It produces a **different 4-class set** (`coral`, `kelp`, `rock`, `sponge`), not the 3-class DUO
set (`starfish`, `sea_urchin`, `scallop`) used everywhere else in this repo. It's included here
as the reproducibility record for that abandoned track, not as usable pretrained models.

Two 500-frame image sets from the same underlying survey (same frames, same labels, different
render/domain transform), each trained no-augmentation vs. with-augmentation:

| Variant | mAP50 (val) | mAP50-95 (val) |
|---|---|---|
| shallowseabed_no_aug | 13.3% | 4.2% |
| **shallowseabed_with_aug (best overall)** | **23.5%** | **9.5%** |
| shallowseabed_no_aug_dr_trans | 8.1% | 2.6% |
| shallowseabed_with_aug_dr_trans | 14.5% | 5.7% |

**Why it was descoped — sim-to-real transfer failed at scale.** All four models were run against
~1955 real (non-simulated) underwater photos as a held-out test set:

| Model | Real-photo detection rate (≥1 box, conf ≥ 0.25) |
|---|---|
| shallowseabed_no_aug | 0/1955 (0.0%) |
| shallowseabed_with_aug | 2/1955 (0.1%) |
| shallowseabed_no_aug_dr_trans | 26/1955 (1.3%) |
| shallowseabed_with_aug_dr_trans | 94/1955 (4.8%) |

Even the best case detects something in fewer than 5% of real photos — the domain gap holds at
scale despite reasonable in-domain (synthetic validation) accuracy above. This result is the
concrete evidence behind the project's pivot to the diffusion-model generation pipeline, which
closes this gap far better (see the DUO zero-shot results above). The real photos themselves
are not republished here (third-party sourced, not original to this project).

## Known limitations

- The Hunyuan dataset was generated but has not yet been used to train or evaluate a model —
  it's included here for completeness/reproducibility, not as a paired dataset+weights set.
- Scallop and diver false-positive behaviour is under active investigation; see the GitHub repo
  for the current state of that analysis.

## License

This dataset (images, labels, and model checkpoints) is released under
[CC-BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/) — reuse and adaptation are
permitted for non-commercial purposes, with attribution. For commercial licensing, contact
the author. The companion `ImgGen_AI` code repository is released separately under the MIT
License.

## Citation

If you use this dataset, please cite it:

```bibtex
@dataset{ramamoorthysathya2026dataset,
  author    = {Ramamoorthy Sathya, Rithish},
  title     = {{MSc Dissertation Datasets and Model Checkpoints: Synthetic Underwater Imagery and YOLO Detectors}},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/Rithish007/MScDissertation},
  note      = {CC-BY-NC-4.0. University of Sheffield MSc Robotics Dissertation.}
}
```

If you use the generation/training pipeline, please also cite the code:

```bibtex
@misc{ramamoorthysathya2026imggen,
  author       = {Ramamoorthy Sathya, Rithish},
  title        = {{ImgGen\_AI: Synthetic Underwater Imagery Generation and Sim-to-Real Object Detection}},
  year         = {2026},
  howpublished = {\url{https://github.com/rithish007/ImgGen_AI}},
  note         = {Code repository}
}
```

If you use the descoped Unreal Engine simulator arm, please cite it separately:

```bibtex
@misc{ramamoorthysathya2026shallowseabed,
  author       = {Ramamoorthy Sathya, Rithish},
  title        = {{ShallowSeabed: Unreal Engine 5 Synthetic Underwater Scene Simulator}},
  year         = {2026},
  howpublished = {\url{https://github.com/rithish007/Shallow_Seabed}},
  note         = {Code repository}
}
```