Add YOLOv26n RDK X5 model and quantized artifacts

x5_quantization/quantize_x5.sh

@@ -0,0 +1,58 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+MODEL_DIR="$(cd "${SCRIPT_DIR}/.." && pwd)"
+REPO_DIR="$(cd "${MODEL_DIR}/.." && pwd)"
+IMAGE="openexplorer/ai_toolchain_ubuntu_20_x5_cpu:v1.2.8"
+
+docker run --rm --platform linux/amd64 \
+  -v "${REPO_DIR}:/workspace/smart_car_2026" \
+  "${IMAGE}" bash -lc '
+    set -euo pipefail
+    cd /workspace/smart_car_2026/model/x5_quantization
+    python3 extract_yolo26_bpu.py \
+      --input ../best.onnx \
+      --output best_bpu.onnx
+    python3 prepare_calibration.py \
+      --dataset /workspace/smart_car_2026/dataset \
+      --output calibration_data \
+      --samples 50
+    cat > best_bpu_int8.yaml <<"YAML"
+model_parameters:
+  onnx_model: "./best_bpu.onnx"
+  march: "bayes-e"
+  layer_out_dump: false
+  working_dir: "mapper_output_bpu"
+  output_model_file_prefix: "best_bpu_bayese_640x640_nv12"
+
+input_parameters:
+  input_name: "images"
+  input_type_rt: "nv12"
+  input_type_train: "rgb"
+  input_layout_train: "NCHW"
+  norm_type: "data_scale"
+  scale_value: 0.003921568627451
+
+calibration_parameters:
+  cal_data_dir: "./calibration_data"
+  cal_data_type: "float32"
+  calibration_type: "default"
+  optimization: "set_Softmax_input_int8,set_Softmax_output_int8"
+
+compiler_parameters:
+  jobs: 8
+  compile_mode: "latency"
+  debug: true
+  optimize_level: "O3"
+YAML
+    hb_mapper checker \
+      --model best_bpu.onnx \
+      --model-type onnx \
+      --march bayes-e \
+      --input-shape images 1x3x640x640
+    hb_mapper makertbin \
+      --config best_bpu_int8.yaml \
+      --model-type onnx
+    ls -lh mapper_output_bpu/best_bpu_bayese_640x640_nv12.bin
+  '