docs: clarify that 2-pass mortality detection is disabled by default for higher accuracy

README.md

@@ -266,10 +266,10 @@ chicken-counter mortality --date 2026-05-23
 
 Key features:
 - **Multi-Image & Multi-Day Support**: Processes multiple images per day (e.g. morning/afternoon scans), aggregates the grand total carcass count (`total_mortality_count`), and saves outputs into date-isolated directories.
-- **2-Pass Detect & Refine**: runs the YOLO segmentation model once to find candidate regions, then uses `cv2.matchTemplate` to do a similarity search over each candidate before confirming it as a detection. This reduces false positives significantly.
+- **Direct High-Precision Segmentation (Default)**: Uses the **segmentation model** (`models/chicken-detection-model-v26n-seg-300e-best-2026-04-18.pt`) directly. 2-pass Detect & Refine (`two_pass: false`) is disabled by default because direct segmentation achieves higher accuracy and avoids false rejection on real farm photos.
+- **Optional 2-Pass Refine (`--two-pass`)**: An optional mode combining initial segmentation candidate proposals with `cv2.matchTemplate` similarity refinement.
 - **Containment filtering**: boxes where `IoA > 0.50` against a larger box are suppressed.
 - **Centroid deduplication**: detections whose centroids are within `dedupe_radius_px` of each other are merged to prevent counting the same carcass twice.
-- Uses the **segmentation model** (`models/chicken-detection-model-v26n-seg-300e-best-2026-04-18.pt`) which provides higher boundary precision than the standard detection model.
 
 Outputs for each daily run:
 - `output_<name>.jpg` — annotated images with bounding boxes and carcass IDs

RUN.md

@@ -66,7 +66,7 @@ Put your model files in the `models/` directory.
 | :--- | :--- | :--- |
 | Batch video counting (TensorRT) | `models/chicken-detection-model-v26n-300e-best-2026-05-02-NEW.engine` | Maximum GPU throughput |
 | Base PyTorch weights | `models/chicken-detection-model-v26n-300e-best-2026-05-02-NEW.pt` | Used for portable runs and auto-recompiling engines |
-| Mortality detection | `models/chicken-detection-model-v26n-seg-300e-best-2026-04-18.pt` | 2-pass segmentation model |
+| Mortality detection | `models/chicken-detection-model-v26n-seg-300e-best-2026-04-18.pt` | Direct segmentation model (2-pass disabled by default for accuracy) |
 
 > **Self-Healing Recompilation on New Machines**: If you move the project to a new machine with a different GPU or OS, the pipeline will detect any incompatible `.engine`, automatically locate the matching `.pt` model, recompile a new `.engine` for the host machine, and update `configs/cycle7_batch_optimized.yaml` automatically.
 
@@ -149,7 +149,7 @@ A `mortality_report.json` is also saved there with full detection data.
 | `iou` | IoU NMS threshold |
 | `min_box_area_px` | Minimum bounding box area in pixels |
 | `dedupe_radius_px` | Centroid deduplication radius in pixels |
-| `two_pass` | Enable 2x Detect & Refine pipeline |
+| `two_pass` | 2x Detect & Refine pipeline (`false` by default; single-pass direct achieves higher accuracy on farm footage) |
 | `classes` | `[0]` = chicken only; ignores background/text/equipment |
 
 ---

src/chicken_counter/mortality.py

@@ -262,7 +262,7 @@ def run_mortality_count(
     iou_threshold = iou_threshold if iou_threshold is not None else float(cfg.get("iou", 0.45))
     min_box_area_px = min_box_area_px if min_box_area_px is not None else int(cfg.get("min_box_area_px", 2500))
     dedupe_radius_px = dedupe_radius_px if dedupe_radius_px is not None else float(cfg.get("dedupe_radius_px", 30.0))
-    two_pass = two_pass if two_pass is not None else bool(cfg.get("two_pass", True))
+    two_pass = two_pass if two_pass is not None else bool(cfg.get("two_pass", False))
     device = device if device is not None else cfg.get("device", "0")
     classes = classes if classes is not None else cfg.get("classes", [0])
     imgsz = imgsz if imgsz is not None else int(cfg.get("imgsz", 640))