---
license: cc-by-4.0
library_name: ultralytics
pipeline_tag: object-detection
tags:
- yolo
- yolo26
- object-detection
- underwater
- marine-biology
- scallop
- rov
- fisheries
---

# FishingROV — YOLO26x L/R 1280 (augmented) — King scallop teacher

Zoo ID: `det-scallop_yolo26x_lr_1280_aug` · canonical weights: `best.pt` (training epoch 50)

High-capacity **teacher** detector for King scallops, trained on left/right split
panels of 1080p survey frames upscaled to 1280 px.

FishingROV mirrors the same **detector → crop → classifier** pattern on two tiers
with different models. On the **GPU server (RTX 3090)** this teacher generates
regions of interest and feeds the cropped detections to a **SwinV2** classifier.
The **on-device Aura** tier runs the lighter **scout** detector with a
**MobileNetV2** classifier. This model is the 3090-side detector. *The full
pipeline is still to be validated.*

## Metrics (honest, station-disjoint held-out)

Re-validated with `model.val(imgsz=1280, conf=0.001, iou=0.6)` on the public
Zenodo **Test files** stations — locations never seen during training.

| Metric | Value |
| --- | --- |
| mAP50 | **0.705** |
| mAP50-95 | **0.443** |
| Precision | 0.737 |
| Recall | 0.637 |
| Peak single-epoch mAP50 | 0.712 |

> **On data integrity.** Validation panels are the public Zenodo **Test files** stations (station-disjoint from training) and are byte-identical to the non-augmented teacher's val set — only the training set was augmented. The reported numbers are therefore honest held-out metrics, not an inflated random-frame split.

## Model details

| | |
| --- | --- |
| Architecture | YOLO26x |
| Input size | 1280 px (left/right split panels) |
| Classes | 1 (scallop) |
| Train panels | 17241 (augmented) |
| Val panels | 1376 |
| Source dataset | `DS-LR1280-v1-aug` |

Best honest L/R teacher in the FishingROV zoo. Augmentation added ~+0.05 mAP50 over the non-augmented baseline (`scallop_yolo26x_lr_1280`, mAP50 0.657) on the same held-out stations.

## SwinV2 classifier metrics (same-crop eval)

The 3090-tier classifier paired with this detector is **SwinV2-B (256)**.
It was trained on `DS-CLS224` (`classifier_data`) and evaluated on its
**station-disjoint val split** derived from Zenodo **Test files** (no random
frame mixing). Crops are square, centered on human boxes, padded if needed,
then resized to 224px; negatives are sampled away from GT boxes.

| Metric | Value |
| --- | --- |
| Macro precision | 0.700 |
| Macro recall | 0.654 |
| Macro F1 | 0.661 |
| Accuracy | 0.966 |

Per-class metrics (from `class_eval_best.json`):

| Class | Precision | Recall | F1 | Support |
| --- | --- | --- | --- | --- |
| dead | 0.464 | 0.642 | 0.539 | 81 |
| king | 0.391 | 0.237 | 0.295 | 76 |
| not_a_scallop | 0.991 | 0.996 | 0.993 | 5781 |
| queen | 0.818 | 0.899 | 0.857 | 296 |
| recessed | 0.837 | 0.497 | 0.623 | 145 |

## Intended use & limitations

- The **3090-side detector**: it generates regions of interest and feeds the
  cropped detections to a **SwinV2** classifier. The same detector → classifier
  pattern is mirrored on the on-device Aura tier with a lighter scout detector
  and a **MobileNetV2** classifier (different models).
- Also usable as an offline pseudo-labelling / auto-annotation teacher to
  bootstrap training data. Not a final stock-assessment instrument.
- **The full pipeline is still to be validated.**
- Trained only on the public St Andrews survey distribution; performance on other
  gear, lighting, or substrate is unverified.
- Partially buried and king-scallop instances remain the hardest cases.

## Files

- `best.pt` — canonical weights (fitness-best epoch 50).
- `last.pt` — final-epoch weights.
- `results.csv`, `results.png`, curves — training history and PR/F1 curves.

## Attribution & License

This model is a derivative work based on the **University of St Andrews King Scallop dataset**.
- Original DOI: [10.5281/zenodo.10156830](https://doi.org/10.5281/zenodo.10156830)

In accordance with the original dataset's terms, this derivative work is released under the **Creative Commons Attribution 4.0 International (CC-BY 4.0)** license. You are free to share and adapt this material, provided you give appropriate credit to the original authors and indicate if changes were made.