Update eval numbers to post-crop Hidden Trespass at validation-selected operating point

README.md

@@ -53,13 +53,14 @@ operating point).
 from paddlex import create_model
 
 model = create_model(model_name="PP-DocLayout-L", model_dir="inference")
-for res in model.predict("page.jpg", threshold=0.68):  # recommended global conf
+for res in model.predict("page.jpg", threshold=0.61):  # recommended global conf
     res.print()          # boxes with label in {header, text-area, footnote, footer}
     res.save_to_img("out/")
 ```
 
 A ready-made `infer.py` (batch, YOLO-format output) is included in this repo.
-Recommended global operating confidence: **0.68** (the best-mean-F1 point).
+Recommended global operating confidence: **0.61** (the validation-selected
+best-mean-F1 point).
 
 ## Evaluation (TiBLAD v4, 833-page test)
 
@@ -67,36 +68,40 @@ Recommended global operating confidence: **0.68** (the best-mean-F1 point).
 |---|---|---|---|
 | license | AGPL-3.0 | **Apache-2.0** | Apache-2.0 |
 | base model | RT-DETR-l (Ultralytics) | **PP-DocLayout-L (PaddleOCR, RT-DETR-L)** | RF-DETR-L (Roboflow) |
-| mean F1 (canonical 3-class) | 0.959 | **0.958** | 0.927 |
-|   header-footer F1 | 0.952 | **0.951** | 0.949 |
-|   text-area F1 | 0.999 | **0.997** | 0.996 |
-|   footnote F1 | 0.925 | **0.925** | 0.835 |
+| mean F1 (canonical 3-class) | 0.952 | **0.955** | 0.921 |
+|   header-footer F1 | 0.954 | **0.953** | 0.947 |
+|   text-area F1 | 0.999 | **0.998** | 0.996 |
+|   footnote F1 | 0.902 | **0.914** | 0.821 |
 | mean AP@0.50 | 0.974 | **0.959** | 0.925 |
 | mean AP@[0.50:0.95] | 0.786 | **0.781** | 0.667 |
 | shared-class mAP@[.50:.95] (DocLayNet-aligned) | 0.650 | **0.641** | 0.604 |
-| Hidden Trespass — header/footer | 0.008 | **0.003** | 0.020 |
-| Hidden Trespass — footnote | 0.037 | **0.037** | 0.216 |
+| Hidden Trespass — header/footer | 0.009 | **0.004** | 0.021 |
+| Hidden Trespass — footnote | 0.043 | **0.037** | 0.178 |
 | COTe (Trespass) | 0.975 (0.001) | **0.978 (0.000)** | 0.974 (0.002) |
-| operating confidence | 0.74 | **0.68** | 0.26 |
+| operating confidence | 0.64 | **0.61** | 0.47 |
 
-*"operating confidence" is the single global best-mean-F1 confidence used for the
-reported F1.*
+*"operating confidence" is the single global best-mean-F1 confidence, selected on
+the leak-free validation split and frozen for test (no test-set tuning). COCO AP
+rows are threshold-free (all detections above the fixed 0.05 floor).*
 
-**Hidden Trespass** = the missed peripheral (header/footer/footnote) ground-truth
-**area** that falls inside the predicted `text-area` crop; area-based,
-micro-averaged over the test set. Lower is better (less clutter bled into the OCR
-region). Formal definition in the [paper](https://github.com/buda-base/papers).
+**Hidden Trespass** = peripheral (header/footer/footnote) ground-truth **area**
+that survives in the *actual OCR body crop* `C = E \ P`, where `E` is the predicted
+`text-area` envelope and `P` is the union of the predicted peripheral boxes the
+pipeline subtracts; area-based, micro-averaged over the test set. Lower is better
+(less peripheral text bled into the OCR region). Formal definition in the
+[paper](https://github.com/buda-base/papers).
 
 ## Which checkpoint to pick
 
 | checkpoint | license | mean F1 | shared mAP | footnote HT |
 |---|---|---|---|---|
-| TiBLA-RTDETR (primary) | AGPL-3.0 | 0.959 | 0.650 | 0.037 |
-| **TiBLA-PP-DocLayout-L** | Apache-2.0 | 0.958 | 0.641 | 0.037 |
-| TiBLA-RFDETR | Apache-2.0 | 0.927 | 0.604 | 0.216 |
+| TiBLA-RTDETR (primary) | AGPL-3.0 | 0.952 | 0.650 | 0.043 |
+| **TiBLA-PP-DocLayout-L** | Apache-2.0 | 0.955 | 0.641 | 0.037 |
+| TiBLA-RFDETR | Apache-2.0 | 0.921 | 0.604 | 0.178 |
 
-RT-DETR-l has the top scores but its weights are AGPL-3.0 (Ultralytics). If you
-need a permissive license, PP-DocLayout-L matches it at Apache-2.0; RF-DETR is a
+RT-DETR-l leads on mAP, shared-class mAP and the 5-seed mean F1 (0.961 ± 0.009),
+but its weights are AGPL-3.0 (Ultralytics). If you need a permissive license,
+PP-DocLayout-L is an Apache-2.0 match (statistically on par on F1); RF-DETR is a
 lighter PyTorch-native Apache-2.0 option.
 
 ## Citation