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641e004 676796b 641e004 cb9ad32 641e004 676796b 641e004 676796b 641e004 676796b 641e004 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 | #!/usr/bin/env bash
# SPDX-License-Identifier: MIT
# Copyright (C) Intel Corporation
#
# Export a YOLO26 person detector to OpenVINO IR.
# Usage: ./export_and_quantize.sh [MODEL_VARIANT] [PRECISION]
# Example: ./export_and_quantize.sh yolo26n FP16
#
# Supported precisions:
# FP32 -- Full-precision floating-point weights
# FP16 -- Half-precision floating-point weights (default)
# INT8 -- Quantized 8-bit integer weights (requires NNCF)
#
# Precision / device compatibility:
# | Precision | CPU | GPU | NPU |
# |-----------|-----|-----|-----|
# | FP32 | Yes | Yes | No |
# | FP16 | Yes | Yes | Yes |
# | INT8 | Yes | Yes | Yes |
set -euo pipefail
MODEL_NAME="${1:-yolo26n}"
PRECISION="${2:-FP16}"
PRECISION="$(echo "${PRECISION}" | tr '[:lower:]' '[:upper:]')"
if [[ "${PRECISION}" != "FP32" && "${PRECISION}" != "FP16" && "${PRECISION}" != "INT8" ]]; then
echo "ERROR: unsupported precision '${PRECISION}'. Choose FP32, FP16, or INT8." >&2
exit 1
fi
echo "--- Installing dependencies ---"
if [[ "${PRECISION}" == "INT8" ]]; then
pip install -qU "openvino>=2026.0.0" "nncf>=3.0.0" ultralytics
else
pip install -qU "openvino>=2026.0.0" ultralytics
fi
echo "--- Downloading sample test image ---"
if [[ ! -f test.jpg ]]; then
wget -q -O test.jpg https://ultralytics.com/images/zidane.jpg
echo "Downloaded: test.jpg"
else
echo "Already present: test.jpg"
fi
echo "--- Downloading sample test video ---"
if [[ ! -f test_video.mp4 ]]; then
wget -q -O test_video.mp4 \
https://github.com/intel-iot-devkit/sample-videos/raw/master/people-detection.mp4
echo "Downloaded: test_video.mp4"
else
echo "Already present: test_video.mp4"
fi
if [[ "${PRECISION}" == "FP32" ]]; then
HALF_FLAG="False"
EXPORT_LABEL="FP32"
else
HALF_FLAG="True"
EXPORT_LABEL="FP16"
fi
echo "--- Exporting ${MODEL_NAME} to OpenVINO IR (${EXPORT_LABEL}) ---"
python3 -c "
from ultralytics import YOLO
model = YOLO('${MODEL_NAME}.pt')
model.export(format='openvino', half=${HALF_FLAG}, dynamic=False, imgsz=640)
print('Export complete: ${MODEL_NAME}_openvino_model/')
"
if [[ "${PRECISION}" == "INT8" ]]; then
echo "--- Quantizing to INT8 with NNCF ---"
python3 -c "
import nncf
import openvino as ov
import numpy as np
import cv2
core = ov.Core()
model = core.read_model('${MODEL_NAME}_openvino_model/${MODEL_NAME}.xml')
# Use the downloaded test image for calibration instead of random noise.
img = cv2.imread('test.jpg')
img = cv2.resize(img, (640, 640))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB).astype(np.float32) / 255.0
img = img.transpose(2, 0, 1)[np.newaxis, ...] # NCHW
def transform_fn(data_item):
return img
calibration_dataset = nncf.Dataset(list(range(300)), transform_fn)
quantized = nncf.quantize(
model,
calibration_dataset,
preset=nncf.QuantizationPreset.MIXED,
subset_size=300,
)
ov.save_model(quantized, '${MODEL_NAME}_person_int8.xml')
print('Quantization complete: ${MODEL_NAME}_person_int8.xml')
"
fi
echo "--- Done ---"
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