# sea-turtle-assets

Public assets for the [sea turtle behavior pipeline](https://github.com/bara495/turtle_behavior_pipeline).

## Contents

| Path | Description |
|------|-------------|
| `models/yolo/best.pt` | YOLO11m-seg model — sea turtle detection |
| `models/yolo/inference_config.json` | Calibrated `conf`/`iou`/`max_det` this checkpoint needs — see below |
| `models/breathing/best_model.keras` | ResNet50 classifier - breathing detection |
| `models/breathing/optimal_threshold.json` | F1-optimal threshold for breathing classifier |
| `1min_sample/` | 1-minute sample clips (2 cameras, 2 days) for pipeline testing |

Downloaded automatically by `setup.sh` -- no manual action needed.

## YOLO inference settings

Always run this model with `max_det=1` — every tank holds exactly one turtle,
so this is a domain constraint, not a tuning choice. Use `inference_config.json`'s
`conf` (0.40) rather than a generic default: it's calibrated against the full
validation set under `max_det=1`, the model's actual deployment shape, and
holds P=R=F1=1.0 across a wide safe range (0.05-0.75) rather than sitting at
either edge of it.

2026-09-05: replaced the previous checkpoint, which scored well on paper
(fitness in the top 10 of ~40 sweep runs) but had 89 false positives and 80%
recall when actually run at real inference settings. Root cause was two
independent bugs — a checkpoint-selection bug in training.py that could save
the wrong epoch's weights as best.pt, and 2 of 50 validation images carrying
duplicate ground-truth labels — both since fixed. This checkpoint is a full
retrain under the corrected code and corrected labels, verified with a clean
P=R=F1=1.0 sweep on the full val set.