Keep internal evaluation details private in model card

README.md

@@ -120,40 +120,37 @@ one hardware/runtime configuration, not a latency guarantee.
 | Optimized ONNX CUDA FP32 | 34.423 | 40.770 | 27.76 | 76.2384 | 90.9896 | 86.8844 | 55.8988 |
 | Optimized ONNX CUDA + TF32 | 25.792 | 32.395 | 35.91 | 76.2414 | 90.9891 | 86.8792 | 55.9025 |
 
-AP uses all 11 classes on 6,489 development pages, with a 0.001 score floor
-for evaluation. The public prediction API uses a 0.30 score floor.
-Latency uses batch one,
-three counterbalanced passes of 100 pages after untimed startup samples.
-The timed region is decoded RGB to CPU box rows, including letterboxing,
-normalization, transfers, inference, validation and postprocessing; it excludes
-image-file decoding, IPC, initialization and evaluation. API serialization
-costs and host-load variation can change application latency. The measured p50
-reduction is 44.2% versus the eager reference. Small AP differences do not
-establish an accuracy improvement.
-
-On two additional financial-document development cohorts (256 and 100 pages),
-table AP changed by -0.0639 and +0.0730 points. The largest class AP decrease
-on the general development cohort was 0.0328 points. Corpus and per-page data
-are not included.
+AP averages IoU thresholds 0.50:0.95; AP50, AP70 and AP90 use their
+respective IoU thresholds. Scores are percentages. Evaluation definitions
+are private, so these numbers are not independently reproducible benchmarks.
+The public prediction API uses a 0.30 score floor.
+
+Latency measures batch-one decoded RGB to CPU box rows, including
+preprocessing, transfers, inference, validation and postprocessing. It excludes
+image decoding, IPC, initialization, evaluation and API serialization.
+Application latency varies with host load. The measured median reduction is
+44.2% versus the eager reference. Small AP differences do not establish an
+accuracy improvement.
+
+Two additional internal evaluations showed AP changes of -0.0639 and +0.0730
+points. The largest class AP decrease in the primary evaluation was 0.0328
+points. Validate on representative inputs before deployment.
 
 The tested graph placed convolutions, matrix multiplications, GridSample and
 Softmax on CUDA. Some shape/control and arithmetic nodes execute on CPU;
 this is not an all-GPU graph claim.
 
-## Release installation validation
+## Published inference runtime
 
-A separate clean installation of the CUDA-only inference package, with no
-PyTorch installed, was evaluated on all 6,489 general development pages:
+The CUDA-only inference package does not require PyTorch. Its internal
+development accuracy measurements are:
 
 | AP | AP50 | AP70 | AP90 |
 |---:|---:|---:|---:|
 | 76.2396 | 90.9855 | 86.8767 | 55.9099 |
 
-The same installation passed the predefined development quality margins on
-both financial-document cohorts. An additional 128-page API check verified
-preprocessing and exact postprocessing of the captured inference outputs.
-Independent CUDA invocations are not bitwise-equivalent; these checks do not
-claim cross-session raw-detection equivalence or independent acceptance.
+These results are not independent acceptance. Independent CUDA invocations
+are not bitwise equivalent.
 
 ## Input and output