Rithish007/MScDissertation

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.

Full pipeline code, configs, and training scripts: 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 (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. 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 — 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:

@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:

@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:

@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}
}