Update commands reproduce

int8/benchmarks/x5h_mwmx_npu_apm80_12core.yaml

@@ -52,8 +52,14 @@ power:
 # command below is identical to the 1-core run.
 reproduce:
   steps:
+    - title: Activate the Python environment
+      command: >-
+        Activate the Python virtual environment that has the `hf` CLI
+        (huggingface_hub) and the NNAC toolchain installed, e.g.
+        `source nnac_venv/bin/activate` -- path depends on your toolchain install.
+      kind: note
     - title: Download the ONNX model and compile config
-      command: hf download Renesas/CenterNet-R18-ONNX --repo-type=model --include "fp32/*" --include "compile_config/*" --local-dir .
+      command: hf download Renesas/CenterNet-R18-ONNX --repo-type=model --include "fp32/*" --include "compile_config/*" --local-dir ./CenterNet-R18-ONNX-fp32
     - title: Configure the network for the 12-core build
       command: |
         cat >> ${WORKDIR}/nnac_config/config.yaml << 'EOF'
@@ -66,7 +72,7 @@ reproduce:
             global_config:
               weight_symmetric: true
             optimization_options:
-              - -segmentation_json ./compile_config/l2seg_csm_16mb.json
+              - -segmentation_json ./CenterNet-R18-ONNX-fp32/compile_config/l2seg_csm_16mb.json
               - -enable_shallow_conv_opt
               - -enable_dma_fusion
               - -stu_weight
@@ -76,12 +82,12 @@ reproduce:
         # previous model), merge this block into it instead of appending a duplicate key.
     - title: Compile with the NNAC toolchain (INT8 auto-cast from the FP32 graph)
       command: |
-        python3 nnac_frontend/legalize.py -d binary/nnx ./fp32/centernet_r18_8xb16_crop512_140e_coco.onnx
+        python3 nnac_frontend/legalize.py -d binary/nnx ./CenterNet-R18-ONNX-fp32/fp32/centernet_r18_8xb16_crop512_140e_coco.onnx --num-core 12
     - title: Set up the R-Car X5H board
       command: Configure the board per the AI Compiler (NNAC) "Getting Started" guide, section 3.4 (host TFTP/NFS setup, bootloader flashing, U-Boot, Linux boot, login) -- exact steps depend on your board/network setup.
       kind: note
     - title: Copy the compiled artifact to the board
-      command: Copy ${WORKDIR}/binary/nnx to the board -- method may vary (NFS mount, scp, USB, etc.).
+      command: Copy ${WORKDIR}/binary/nnx (the working directory from the download/compile steps above) to the board -- method may vary (NFS mount, scp, USB, etc.).
       kind: note
     - title: Run on R-Car X5H (single NPU cluster, 12 AI cores)
       command: |

int8/benchmarks/x5h_mwmx_npu_apm80_1core.yaml

@@ -46,16 +46,22 @@ power:
 # in place of the generic placeholder flow — see downloadRunHTML() / parse_reproduce().
 reproduce:
   steps:
+    - title: Activate the Python environment
+      command: >-
+        Activate the Python virtual environment that has the `hf` CLI
+        (huggingface_hub) and the NNAC toolchain installed, e.g.
+        `source nnac_venv/bin/activate` -- path depends on your toolchain install.
+      kind: note
     - title: Download the ONNX model
-      command: hf download Renesas/CenterNet-R18-ONNX --repo-type=model --include "fp32/*" --local-dir .
+      command: hf download Renesas/CenterNet-R18-ONNX --repo-type=model --include "fp32/*" --local-dir ./CenterNet-R18-ONNX-fp32
     - title: Compile with the NNAC toolchain (INT8 auto-cast from the FP32 graph)
       command: |
-        python3 nnac_frontend/legalize.py -d binary/nnx ./fp32/centernet_r18_8xb16_crop512_140e_coco.onnx
+        python3 nnac_frontend/legalize.py -d binary/nnx ./CenterNet-R18-ONNX-fp32/fp32/centernet_r18_8xb16_crop512_140e_coco.onnx --num-core 1
     - title: Set up the R-Car X5H board
       command: Configure the board per the AI Compiler (NNAC) "Getting Started" guide, section 3.4 (host TFTP/NFS setup, bootloader flashing, U-Boot, Linux boot, login) -- exact steps depend on your board/network setup.
       kind: note
     - title: Copy the compiled artifact to the board
-      command: Copy ${WORKDIR}/binary/nnx to the board -- method may vary (NFS mount, scp, USB, etc.).
+      command: Copy ${WORKDIR}/binary/nnx (the working directory from the download/compile steps above) to the board -- method may vary (NFS mount, scp, USB, etc.).
       kind: note
     - title: Run on R-Car X5H (single NPU cluster)
       command: |

int8/benchmarks/x5h_mwmx_npu_apm80_1core_2026-09-16.yaml

@@ -47,16 +47,22 @@ power:
 # in place of the generic placeholder flow — see downloadRunHTML() / parse_reproduce().
 reproduce:
   steps:
+    - title: Activate the Python environment
+      command: >-
+        Activate the Python virtual environment that has the `hf` CLI
+        (huggingface_hub) and the NNAC toolchain installed, e.g.
+        `source nnac_venv/bin/activate` -- path depends on your toolchain install.
+      kind: note
     - title: Download the ONNX model
-      command: hf download Renesas/CenterNet-R18-ONNX --repo-type=model --include "fp32/*" --local-dir .
+      command: hf download Renesas/CenterNet-R18-ONNX --repo-type=model --include "fp32/*" --local-dir ./CenterNet-R18-ONNX-fp32
     - title: Compile with the NNAC toolchain (INT8 auto-cast from the FP32 graph)
       command: |
-        python3 nnac_frontend/legalize.py -d binary/nnx ./fp32/centernet_r18_8xb16_crop512_140e_coco.onnx
+        python3 nnac_frontend/legalize.py -d binary/nnx ./CenterNet-R18-ONNX-fp32/fp32/centernet_r18_8xb16_crop512_140e_coco.onnx --num-core 1
     - title: Set up the R-Car X5H board
       command: Configure the board per the AI Compiler (NNAC) "Getting Started" guide, section 3.4 (host TFTP/NFS setup, bootloader flashing, U-Boot, Linux boot, login) -- exact steps depend on your board/network setup.
       kind: note
     - title: Copy the compiled artifact to the board
-      command: Copy ${WORKDIR}/binary/nnx to the board -- method may vary (NFS mount, scp, USB, etc.).
+      command: Copy ${WORKDIR}/binary/nnx (the working directory from the download/compile steps above) to the board -- method may vary (NFS mount, scp, USB, etc.).
       kind: note
     - title: Run on R-Car X5H (single NPU cluster)
       command: |