Fix citation title to match ShallowSeabed's current name (was pre-rename Shallow_Seabed)
9bc5a43 verified | 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} | |
| } | |
| ``` | |