Update metadata

.metadata.yaml

@@ -1,18 +1,34 @@
+# upstream was `onnx/retinanet-9`, which does not exist; the README front matter
+# already had the correct `onnxmodelzoo/retinanet-9`.
+# format.version was `1.6.0` (the ONNX IR version is in fact 6); replaced by opset,
+# which was already correctly recorded as 9.
+# Input is 480x640 and the 720/36-channel outputs imply 80 classes x 9 anchors and
+# 4 box coords x 9 anchors — both read from the graph.
+
 model:
-  name: retinanet
-  display_name: RetinaNet
-  upstream: onnx/retinanet-9
+  name: retinanet-r101
+  display_name: RetinaNet-R101
+  upstream: onnxmodelzoo/retinanet-9
+  source:
+    kind: onnx-model-zoo
+    id: retinanet-9
+    url: https://github.com/onnx/models
 
 architecture:
-  family: retinanet
+  family: retinanet   # lineage only — no version, no size
   backbone: resnet101
+  dataset: coco
+  input_resolution: 480x640
+  num_classes: 80
   modality:
     - vision
-  parameters: 57.07M
+  parameters: 57.1M
+  parameters_source: measured
+  parameters_note: >-
+    57.07M counted from fp32/retinanet-9.onnx initializers
 
 format:
   type: onnx
-  version: 1.6.0
   opset: 9
 
 tasks:

README.md

@@ -14,14 +14,14 @@ tags:
 - detection
 ---
 
-# RetinaNet (ONNX) – Renesas X5H
+# RetinaNet-R101 (ONNX) – Renesas X5H
 
 ## Introduction
 
 This repository hosts **RetinaNet** in ONNX FP32 format, targeting the **Renesas R-Car X5H** platform for object detection inference on the NPX6 NPU.
 
 - **Model Architecture:** RetinaNet with ResNet101 backbone and Feature Pyramid Network (FPN)
-- **Source Model:** ONNX Model Zoo RetinaNet
+- **Source Model:** [onnxmodelzoo/retinanet-9](https://huggingface.co/onnxmodelzoo/retinanet-9) — ONNX Model Zoo [`retinanet-9`](https://github.com/onnx/models)
 - **Task:** Object Detection
 - **Dataset:** COCO
 - **Accuracy:** mAP = 0.376

fp32/benchmarks/x5h_ort_npu.yaml

@@ -11,6 +11,7 @@ hardware:
 
 runtime:
   engine: onnxruntime
+  toolchain_version: "v2.0.0 renesas"
   format: onnx
   execution_provider: npu
   execution_precision: fp32

int8/benchmarks/x5h_mwmx_npu.yaml

@@ -11,6 +11,7 @@ hardware:
 
 runtime:
   engine: mwmx             # Renesas MWMX (Middleware MX) native inference runtime
+  toolchain_version: "MWMX SDK v4.35.0"
   format: onnx             # input model format (FP32 ONNX)
   execution_provider: npu
   execution_precision: int8  # FP32 model is auto-cast to INT8 by the MWMX toolchain

int8/benchmarks/x5h_mwmx_npu_apm80_12core.yaml

@@ -0,0 +1,42 @@
+hardware:
+  vendor: renesas
+  chip: rcar-x5h
+  cpu: arm-cortex-a720
+  npu: npx6-48k
+  npu_count: 2
+  npu_cores: 12
+  npu_default_freq_mhz: 1066
+  accelerator:
+    - npu
+
+runtime:
+  engine: mwmx             # Renesas MWMX (Middleware MX) native inference runtime
+  toolchain_version: "MWMX SDK v4.35.0"
+  format: onnx             # input model format (FP32 ONNX)
+  execution_provider: npu
+  execution_precision: int8  # FP32 model is auto-cast to INT8 by the MWMX toolchain
+
+configuration:
+  npu_instances: 1         # single NPU
+  npu_cores_per_instance: 12  # 12 AI cores (APM80 ship-performance target run)
+  npu_freq_mhz: 850        # 850 MHz NPU clock frequency
+
+benchmark:
+  type: hil                # Hardware-in-the-loop — measured on physical X5H silicon
+  parameters:
+    batch_size: 1
+    input_resolution: [1, 3, 480, 640]
+
+performance:
+  fps: null
+  latency: 6.810764        # APM80 pipeline run (metawaremx_runtime CI)
+
+metrics:
+  accuracy: null
+  top5_accuracy: null
+
+memory:
+  peak_mb: null
+
+power:
+  avg_w: null

int8/benchmarks/x5h_ppa_npu.yaml

@@ -11,6 +11,7 @@ hardware:
 
 runtime:
   engine: ppa-estimator
+  toolchain_version: null
   format: onnx
   execution_provider: npu
   execution_precision: int8