Update model card for yolo11-det

README.md

@@ -74,6 +74,7 @@ Row conventions in the table below:
 - **Rows whose `Δ` cell reads `ref`** are the float reference runs each quantized/compiled measurement is graded against.
 - **Rows without a number** under the metric columns are validation sessions that are currently work in progress — typically a larger size that has not yet been profiled on a given NPU, or a session that has not yet been linked to an ONNX FP32 reference. The Studio Source link tracks the current status.
 - **Rows whose `Δ vs FP32` cell carries a ⚠** are below our accuracy expectations for that platform (more than 10 percentage points under the float reference). The numbers are real measurements on real hardware, reproducible from the linked Studio session, and we publish them as-is; we are investigating the results to make improvements, and the next snapshot of this card will reflect any recovered accuracy.
+- **Rows whose metric cells read `Not supported`** indicate platforms where this model family is not currently supported due to architecture or compiler constraints. The Studio source link tracks the session for future compatibility updates.
 - **INT8 rows use the smart (multi-scale split) decoder** wherever both smart and logical decoder validations exist — smart is the variant we publish and ship. A dedicated section comparing the smart and logical decoders, using these benchmark results as reference cards, is planned.
 - **End-to-end (ms)** is the sequential per-image wall time covering the *full* pipeline — image load → JPEG decode → preprocess → inference → postprocess. **Throughput (FPS)** is the measured pipelined rate over the same full pipeline, which normally exceeds `1000 / end-to-end` because the runtime overlaps stages across frames.
 
@@ -82,8 +83,8 @@ Row conventions in the table below:
 | Nano | macOS CoreML — Neural Engine (FP16) | 53.05% | +0.00 | 37.82% | 1.83 | 5.62 | 720.5 | [v-516](https://edgefirst.studio/public/validation/v-516/details?mode=charts) |
 | Nano | macOS CoreML — Metal GPU (FP16) | 53.04% | -0.01 | 37.80% | 4.26 | 8.30 | 412.5 | [v-51e](https://edgefirst.studio/public/validation/v-51e/details?mode=charts) |
 | Nano | macOS CoreML — CPU (FP16) | 53.05% | ref | 37.78% | 15.69 | 20.37 | 124.5 | [v-51f](https://edgefirst.studio/public/validation/v-51f/details?mode=charts) |
-| Nano | NXP i.MX 8M Plus + VeriSilicon NPU | 47.49% | -5.56 | 32.17% | 92.70 | 122.21 | 9.7 | [v-52a](https://edgefirst.studio/public/validation/v-52a/details?mode=charts) |
-| Nano | NXP i.MX 95 + eIQ Neutron NPU | 10.77% | -42.28 ⚠ | 5.27% | 50.94 | 84.77 | 17.7 | [v-52b](https://edgefirst.studio/public/validation/v-52b/details?mode=charts) |
+| Nano | NXP i.MX 8M Plus + VeriSilicon NPU | 49.15% | -3.90 | 34.66% | 87.08 | 131.63 | 10.2 | [v-531](https://edgefirst.studio/public/validation/v-531/details?mode=charts) |
+| Nano | NXP i.MX 95 + eIQ Neutron NPU | Not supported | — | — | — | — | — | [v-532](https://edgefirst.studio/public/validation/v-532/details?mode=charts) |
 | Nano | Raspberry Pi 5 + Hailo-8L NPU | 50.90% | -2.15 | 36.02% | 17.86 | 28.43 | 53.7 | [v-4de](https://edgefirst.studio/public/validation/v-4de/details?mode=charts) |
 | Nano | NVIDIA Jetson Orin Nano (TensorRT FP16) | 53.03% | -0.02 | 37.81% | 7.09 | 17.56 | 161.4 | [v-4dc](https://edgefirst.studio/public/validation/v-4dc/details?mode=charts) |
 | Small | macOS CoreML — Neural Engine (FP16) | 61.17% | -0.02 | 44.88% | 4.95 | 8.74 | 328.9 | [v-517](https://edgefirst.studio/public/validation/v-517/details?mode=charts) |
@@ -92,7 +93,6 @@ Row conventions in the table below:
 | Small | Raspberry Pi 5 + Hailo-8L NPU | 58.88% | -2.31 | 42.84% | 36.53 | 47.06 | 25.2 | [v-4df](https://edgefirst.studio/public/validation/v-4df/details?mode=charts) |
 | Small | NVIDIA Jetson Orin Nano (TensorRT FP16) | 61.13% | -0.06 | 44.85% | 15.57 | 24.81 | 109.0 | [v-4dd](https://edgefirst.studio/public/validation/v-4dd/details?mode=charts) |
 
-> **⚠ Below expectations — under investigation.** The rows marked ⚠ above measure more than 10 percentage points below the same training session's float reference: the model accuracy on that platform is below our expectations. We publish the measured numbers rather than hiding them, and we are investigating the results to make improvements — the next snapshot of this card will reflect any recovered accuracy.
 
 
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