feat: dynamic Frigate score ceiling; consolidate quality replacement branches

FRIGATE_SCORE_CEILING now defaults to dynamic mode (unset): below-cap
candidates are skipped if their pre-upload Frigate score exceeds the
most-redundant tracked file's score. This catches conditions already
covered by manually-added Frigate images that winnow cannot track —
the embedding-based diversity selection has no visibility into those.
Set FRIGATE_SCORE_CEILING=0 to disable; a positive value (e.g. 0.85)
still acts as a fixed hard ceiling. First-run safety is unchanged
(pre_run_count==0 prevents recognize_face from being called).

The two quality replacement branches (Frigate-score and blur-score)
shared identical structure and are merged into a single code path
parameterised by score source and comparison direction.

Also raises MIN_FACE_COUNT default from 0 to 3 and updates the
config test to match.

CHANGELOG.md

@@ -7,6 +7,17 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## [0.4.9] - 2026-06-14
+
+### Changed
+
+- **`FRIGATE_SCORE_CEILING` is now dynamic by default**: previously defaulted to `0` (disabled). Now unset (default) enables a self-calibrating novelty gate — below-cap candidates are skipped if their pre-upload Frigate score exceeds the most-redundant tracked file's score. This catches conditions already covered by manually-added Frigate images that winnow cannot track. Set `FRIGATE_SCORE_CEILING=0` to disable entirely; set a positive value (e.g. `0.85`) for a fixed hard ceiling.
+- **Quality replacement branches consolidated**: the Frigate-score and blur-score replacement paths in the upload loop shared identical structure. Merged into a single code path parameterised by score source and comparison direction.
+- **`MIN_FACE_COUNT` default raised from `0` to `3`**: people with fewer than 3 tagged photos produce degenerate training sets; skipping them by default avoids noisy runs.
+- **`STRATEGY=adaptive`** is the new primary name for embedding-based diversity selection; `auto` remains a silent alias for backwards compatibility.
+- **`MERGE_DUPLICATE_PEOPLE` and `TRACE_CROP_SIZE`** added to the README env var table (were in the codebase but undocumented).
+- **CUDA version corrected** in the image tags table (was 13.3, actual base image is 12.8.1).
+
 ## [0.4.8] - 2026-06-14
 
 ### Changed

README.md

@@ -207,7 +207,7 @@ In scheduled mode the process (and loaded models) stays resident between runs. T
 | `BLUR_THRESHOLD` | `120.0` | Laplacian variance threshold — lower accepts more blur |
 | `MAX_AUTO_IMAGES` | `80` | Maximum training images per person in Frigate |
 | `QUALITY_REPLACEMENT` | `true` | When at cap, swap a weaker tracked image for a better candidate. With Frigate scoring active, targets the most redundant image (highest pre-upload recognize score); otherwise uses blur score. Never touches manually added Frigate files. Set `false` to skip people at cap |
-| `FRIGATE_SCORE_CEILING` | `0.0` | Skip uploads whose pre-upload Frigate recognize score exceeds this value — they are already well-covered. `0` disables; requires at least one prior run to have scores |
+| `FRIGATE_SCORE_CEILING` | *(unset)* | Below-cap novelty gate against Frigate's live model (catches conditions covered by manually-added images too). Unset: dynamic — skips candidates whose Frigate score exceeds the most-redundant tracked file's score, auto-calibrates each run. `0`: disable entirely. Positive value (e.g. `0.85`): fixed hard ceiling. No effect on the first run or when `ENABLE_FRIGATE_SCORES=false` |
 | `ENABLE_FRIGATE_SCORES` | `true` | Call Frigate's recognize endpoint pre-upload to store diversity scores used for quality replacement. Adds ~200 ms per upload. Disable to use blur-score replacement only |
 
 ### GPU & Models

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "winnow"
-version = "0.4.8"
+version = "0.4.9"
 description = "Selects diverse, high-quality photos from Immich as training data for Frigate face recognition and object classification."
 license = "AGPL-3.0-or-later"
 requires-python = ">=3.13"

tests/test_config.py

@@ -18,7 +18,7 @@ def test_config_loads_defaults(monkeypatch):
     assert cfg.OUTPUT_DIR == "./frigate_train"
     assert cfg.YEARS_FILTER == 10
     assert cfg.MIN_FACE_WIDTH == 90
-    assert cfg.MIN_FACE_COUNT == 0
+    assert cfg.MIN_FACE_COUNT == 3
     assert cfg.BLUR_THRESHOLD == 120.0
     assert cfg.MIN_CONFIDENCE == 0.7
     assert cfg.MAX_AUTO_IMAGES == 80

winnow/config.py

@@ -31,7 +31,7 @@ class _Config:
     MIN_CONFIDENCE: float = 0.7
     MAX_AUTO_IMAGES: int = 80
     QUALITY_REPLACEMENT: bool = True
-    FRIGATE_SCORE_CEILING: float = 0.0
+    FRIGATE_SCORE_CEILING: float | None = None
     ENABLE_FRIGATE_SCORES: bool = True
 
     # People filtering
@@ -66,7 +66,8 @@ class _Config:
         self.MIN_CONFIDENCE = float(os.getenv("MIN_CONFIDENCE", "0.7"))
         self.MAX_AUTO_IMAGES = int(os.getenv("MAX_AUTO_IMAGES", "80"))
         self.QUALITY_REPLACEMENT = os.getenv("QUALITY_REPLACEMENT", "true").lower() in ("true", "1", "yes")
-        self.FRIGATE_SCORE_CEILING = float(os.getenv("FRIGATE_SCORE_CEILING", "0.0"))
+        _ceiling_env = os.getenv("FRIGATE_SCORE_CEILING", "").strip()
+        self.FRIGATE_SCORE_CEILING = float(_ceiling_env) if _ceiling_env else None
         self.ENABLE_FRIGATE_SCORES = os.getenv("ENABLE_FRIGATE_SCORES", "true").lower() in ("true", "1", "yes")
         self.FACE_MARGIN = float(os.getenv("FACE_MARGIN", "0.15"))
         self.USE_FULL_RESOLUTION = os.getenv("USE_FULL_RESOLUTION", "true").lower() in ("true", "1", "yes")

winnow/executor.py

@@ -482,14 +482,28 @@ def upload_to_frigate(jobs: list[dict]) -> None:
                         if _result is not None and (_result[0] or "").casefold() == name.casefold():
                             pre_fscore = _result[1]
 
-                # Ceiling check: skip if the existing training set already covers this
-                # face condition well. Applies below cap only — at cap, replacement logic
-                # drives the decision.
-                if not at_cap and Config.FRIGATE_SCORE_CEILING > 0 and pre_run_count > 0:
-                    if pre_fscore is not None and pre_fscore > Config.FRIGATE_SCORE_CEILING:
+                # Below-cap novelty gate: skip candidates already covered by the Frigate model,
+                # including conditions learned from manually-added images winnow can't track.
+                # pre_fscore is None on the first run (pre_run_count == 0 skips recognize_face
+                # above), so this block never fires on the first run without an extra guard.
+                if not at_cap and pre_fscore is not None:
+                    _ceiling = Config.FRIGATE_SCORE_CEILING
+                    if _ceiling is None:
+                        # Dynamic default: bar = most-redundant tracked file's Frigate score.
+                        # Falls back to uploading freely when no tracked scores exist yet.
+                        _bar = get_most_redundant_mapped_file(name)
+                        _skip = _bar is not None and pre_fscore > _bar[2]
+                        _bar_str = f"most redundant tracked {_bar[2]:.2f}" if _bar else ""
+                    elif _ceiling == 0.0:
+                        _skip = False  # explicitly disabled
+                        _bar_str = ""
+                    else:
+                        _skip = pre_fscore > _ceiling
+                        _bar_str = f"ceiling {_ceiling:.2f}"
+                    if _skip:
                         progress.console.print(
                             f"    [dim]⏭  {fname}: Frigate score {pre_fscore:.2f}"
-                            f" > ceiling {Config.FRIGATE_SCORE_CEILING:.2f}, already covered[/dim]"
+                            f" > {_bar_str}, already covered[/dim]"
                         )
                         progress.advance(upload_task)
                         continue
@@ -503,70 +517,50 @@ def upload_to_frigate(jobs: list[dict]) -> None:
                     using_fscore = person_has_fscores and Config.ENABLE_FRIGATE_SCORES
                     if using_fscore:
                         candidate_score = pre_fscore
-                        if candidate_score is None:
-                            progress.console.print(
-                                f"    [dim]⏭  {fname}: Frigate recognize unavailable, skipping replacement[/dim]"
-                            )
-                            progress.advance(upload_task)
-                            continue
-                        # Low score = more novel than the most redundant mapped file = replace
-                        target = get_most_redundant_mapped_file(name, exclude=failed_deletes)
-                        if target is None or candidate_score >= target[2]:
-                            target_score_str = f"{target[2]:.3f}" if target is not None else "N/A"
-                            progress.console.print(
-                                f"    [dim]⏭  {fname}: frigate {candidate_score:.3f} ≥ most redundant"
-                                f" {target_score_str}, not more novel[/dim]"
-                            )
-                            progress.advance(upload_task)
-                            continue
-                        target_frigate_file, _target_asset_id, target_score = target
-                        progress.console.print(
-                            f"    🔄 {fname}: frigate {candidate_score:.3f} < {target_score:.3f},"
-                            f" replacing {target_frigate_file} (more novel)"
-                        )
-                        if delete_frigate_person_files(name, [target_frigate_file]):
-                            remove_frigate_file(name, target_frigate_file)
-                            person_has_fscores = has_frigate_scores(name)
-                            effective_count -= 1
-                            # clear any blur-mode slot floor — Frigate uses a different score metric
-                            min_quality_score_for_slot = None
-                        else:
-                            logger.warning(f"Failed to delete {target_frigate_file} for {name}, skipping replacement")
-                            failed_deletes.add(target_frigate_file)
-                            progress.advance(upload_task)
-                            continue
+                        get_target = get_most_redundant_mapped_file
+                        score_label, better_note = "frigate", " (more novel)"
+                        no_score_msg = "Frigate recognize unavailable, skipping replacement"
                     else:
                         candidate_score = score_map.get(fname)
-                        if candidate_score is None:
-                            progress.console.print(
-                                f"    [dim]⏭  {fname}: no quality score, skipping replacement[/dim]"
-                            )
-                            progress.advance(upload_task)
-                            continue
-                        target = get_lowest_quality_mapped_file(name, exclude=failed_deletes)
-                        if target is None or candidate_score <= target[2]:
-                            target_score_str = f"{target[2]:.3f}" if target is not None else "N/A"
-                            progress.console.print(
-                                f"    [dim]⏭  {fname}: blur {candidate_score:.3f} ≤ worst"
-                                f" {target_score_str}, skipping[/dim]"
-                            )
-                            progress.advance(upload_task)
-                            continue
-                        target_frigate_file, _target_asset_id, target_score = target
+                        get_target = get_lowest_quality_mapped_file
+                        score_label, better_note = "blur", ""
+                        no_score_msg = "no quality score, skipping replacement"
+
+                    if candidate_score is None:
+                        progress.console.print(f"    [dim]⏭  {fname}: {no_score_msg}[/dim]")
+                        progress.advance(upload_task)
+                        continue
+
+                    target = get_target(name, exclude=failed_deletes)
+                    not_better = target is None or (
+                        candidate_score >= target[2] if using_fscore else candidate_score <= target[2]
+                    )
+                    if not_better:
+                        target_str = f"{target[2]:.3f}" if target is not None else "N/A"
+                        op = "<" if using_fscore else ">"
                         progress.console.print(
-                            f"    🔄 {fname}: blur {candidate_score:.3f} > {target_score:.3f},"
-                            f" replacing {target_frigate_file}"
+                            f"    [dim]⏭  {fname}: {score_label} {candidate_score:.3f}"
+                            f" not {op} {target_str}, skipping[/dim]"
                         )
-                        if delete_frigate_person_files(name, [target_frigate_file]):
-                            remove_frigate_file(name, target_frigate_file)
-                            person_has_fscores = has_frigate_scores(name)
-                            effective_count -= 1
-                            min_quality_score_for_slot = score_map.get(fname)
-                        else:
-                            logger.warning(f"Failed to delete {target_frigate_file} for {name}, skipping replacement")
-                            failed_deletes.add(target_frigate_file)
-                            progress.advance(upload_task)
-                            continue
+                        progress.advance(upload_task)
+                        continue
+
+                    target_frigate_file, _target_asset_id, target_score = target
+                    op = "<" if using_fscore else ">"
+                    progress.console.print(
+                        f"    🔄 {fname}: {score_label} {candidate_score:.3f} {op} {target_score:.3f},"
+                        f" replacing {target_frigate_file}{better_note}"
+                    )
+                    if delete_frigate_person_files(name, [target_frigate_file]):
+                        remove_frigate_file(name, target_frigate_file)
+                        person_has_fscores = has_frigate_scores(name)
+                        effective_count -= 1
+                        min_quality_score_for_slot = None if using_fscore else candidate_score
+                    else:
+                        logger.warning(f"Failed to delete {target_frigate_file} for {name}, skipping replacement")
+                        failed_deletes.add(target_frigate_file)
+                        progress.advance(upload_task)
+                        continue
 
                 for attempt in range(1, max_retries + 1):
                     try: