Document the descoped ShallowSeabed UE simulator dataset and its sim-to-real failure results

README.md

@@ -43,10 +43,22 @@ weights/
 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/*` folder is a standard YOLO layout (`images/train`, `images/val`,
-`labels/train`, `labels/val`, `data.yaml`).
+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
 
@@ -64,6 +76,42 @@ Domain randomization calibrated against real DUO water statistics (`duo_dr`, `du
 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 —