---
license: cc-by-4.0
library_name: timm
pipeline_tag: image-classification
tags:
- swinv2
- image-classification
- underwater
- marine-biology
- scallop
- rov
- fisheries
---

# FishingROV - SwinV2-B 256 classifier (crops)

Zoo ID: `cls-classifier_swinv2b_256` - canonical weights: `best.pt`

SwinV2-B classifier for 5-way scallop taxonomy on square crops. Used in the
3090-side detector -> crop -> classifier pipeline (paired with the YOLO teacher
for ROI generation). This is a classifier only; it does not perform detection.

## Dataset provenance (station-disjoint)

This model is trained on `DS-CLS224` (`classifier_data`) generated from the
public Zenodo dataset:

- Train split: Zenodo **Training files** stations
- Val split: Zenodo **Test files** stations (station-disjoint)

Crops are square, centered on human boxes, padded if needed, resized to 224px.
Negatives are sampled away from GT boxes. No augmentation.

## Metrics (same-crop validation)

All metrics below are from `classifier_data/val` (station-disjoint Test files).

| 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

- Intended for crop classification, not end-to-end detection.
- Trained only on the St Andrews survey distribution; generalization is unknown.
- King class remains the weakest; expect confusion in borderline cases.

## Files

- `best.pt` - best checkpoint (SwinV2-B 256)
- `last.pt` - final checkpoint
- `class_eval_best.json` - macro + per-class metrics
- `classes.json`, `history.json`

## Attribution & License

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

Released under **CC-BY 4.0** with attribution to the original authors.