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FishingROV/scallop_yolo26x_lr_1280_aug

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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
notascallop	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

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.