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object-detection

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Object Detection

Property	Value
Category	General Object Detection (80-class COCO)
Base Model	YOLO26 (Ultralytics)
Source Framework	PyTorch (Ultralytics)
Supported Precisions	FP32, FP16, INT8 (mixed-precision)
Inference Engine	OpenVINO
Hardware	CPU, GPU, NPU
Detected Class(es)	All 80 COCO classes

Overview

Object Detection is a Metro Analytics use case that detects and classifies objects across the full 80-class COCO taxonomy (person, vehicle, animal, everyday objects, etc.). It is built on YOLO26, a state-of-the-art real-time object detector, quantized to INT8 for efficient inference on Intel hardware. Unlike the specialized person or vehicle detectors, this model keeps all 80 classes active, making it suitable for general-purpose scene understanding.

Typical Metro deployments include:

Scene Understanding -- identify and classify all objects visible in a camera feed.
Inventory Monitoring -- detect specific items (bags, suitcases, bottles) on platforms.
Anomaly Detection -- flag unexpected objects in restricted areas.
Multi-Class Analytics -- gather statistics across people, vehicles, and other categories.

Available variants: yolo26n, yolo26s, yolo26m, yolo26l, yolo26x. Smaller variants (yolo26n, yolo26s) are recommended for high-FPS edge deployment; larger variants improve recall for small objects.

Prerequisites

Python 3.11+
Install OpenVINO (latest version)
Install Intel DLStreamer (latest version)

Create and activate a Python virtual environment before running the scripts:

bash

python3 -m venv .venv --system-site-packages
source .venv/bin/activate

Note: The --system-site-packages flag is required so the virtual environment can access the system-installed OpenVINO and DLStreamer Python packages.

Getting Started

Download and Quantize Model

Run the provided script to download, export to OpenVINO IR, and optionally quantize:

bash

chmod +x export_and_quantize.sh
./export_and_quantize.sh

This exports the default yolo26n model in FP16 precision.

Optional: Select a Different Variant or Precision

bash

./export_and_quantize.sh yolo26n FP32   # full-precision
./export_and_quantize.sh yolo26n INT8   # quantized
./export_and_quantize.sh yolo26s        # larger variant, default FP16

Replace yolo26n with any variant (yolo26s, yolo26m, yolo26l, yolo26x). The second argument selects the precision (FP32, FP16, INT8); the default is FP16.

The script performs the following steps:

Installs dependencies (openvino, ultralytics; adds nncf for INT8).
Downloads a sample test image (test.jpg) and a sample test video (test_video.mp4).
Downloads the PyTorch weights and exports to OpenVINO IR.
(INT8 only) Quantizes the model using NNCF post-training quantization.

Output files:

yolo26n_openvino_model/ -- FP32 or FP16 OpenVINO IR model directory.
yolo26n_objdet_int8.xml / yolo26n_objdet_int8.bin -- INT8 quantized model (only when INT8 is selected).

Precision / Device Compatibility

Precision	CPU	GPU	NPU
FP32	Yes	Yes	No
FP16	Yes	Yes	Yes
INT8	Yes	Yes	Yes

Note: The INT8 calibration uses the bundled sample image. For production accuracy, replace it with a representative set of frames from the target deployment site.

OpenVINO Sample

The sample below runs YOLO26 inference on all 80 COCO classes and prints every detected object with its class name and confidence. YOLO26 is end-to-end (NMS-free), so no manual non-maximum suppression is needed. Change the device string to run on CPU, GPU, or NPU.

python

import cv2
import numpy as np
import openvino as ov

CONF_THRESHOLD = 0.4
INPUT_SIZE = 640

core = ov.Core()
model = core.read_model("yolo26n_openvino_model/yolo26n.xml")

# YOLO26 embeds the 80 COCO class names in rt_info -- read them instead of
# hardcoding the list. Ultralytics separates multi-word names with
# underscores (e.g. "traffic_light"), so restore spaces for display.
COCO_NAMES = [
    name.replace("_", " ")
    for name in model.get_rt_info()["model_info"]["labels"].value.split()
]

# Change device to "GPU" or "NPU" to run on integrated GPU or NPU.
compiled = core.compile_model(model, "CPU")

image = cv2.imread("test.jpg")
h0, w0 = image.shape[:2]

blob = cv2.resize(image, (INPUT_SIZE, INPUT_SIZE))
blob = cv2.cvtColor(blob, cv2.COLOR_BGR2RGB).astype(np.float32) / 255.0
blob = blob.transpose(2, 0, 1)[np.newaxis, ...]  # NCHW

# YOLO26 end-to-end output: [1, 300, 6] = [x1, y1, x2, y2, confidence, class_id]
output = compiled([blob])[compiled.output(0)][0]
mask = output[:, 4] >= CONF_THRESHOLD
dets = output[mask]

sx, sy = w0 / INPUT_SIZE, h0 / INPUT_SIZE
print(f"Total detections: {len(dets)}")

colors = np.random.RandomState(42).randint(0, 255, (80, 3)).tolist()
for det in dets:
    x1 = int(det[0] * sx)
    y1 = int(det[1] * sy)
    x2 = int(det[2] * sx)
    y2 = int(det[3] * sy)
    cid = int(det[5])
    conf = float(det[4])
    label = f"{COCO_NAMES[cid]} {conf:.2f}"
    color = colors[cid]
    cv2.rectangle(image, (x1, y1), (x2, y2), color, 2)
    cv2.putText(image, label, (x1, y1 - 5),
                cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
    print(f"  {label} at ({x1},{y1})-({x2},{y2})")

cv2.imwrite("output_openvino.jpg", image)

Device targets:

"CPU" -- default, works on all Intel platforms.
"GPU" -- Intel integrated or discrete GPU.
"NPU" -- Intel NPU (validate with benchmark_app -d NPU).

Try It on a Sample Image

The export_and_quantize.sh script downloads test.jpg automatically. Re-run the OpenVINO sample above. The script reads test.jpg, prints each detected object to the console, and writes the annotated frame to output_openvino.jpg.

Expected console output (representative):

text

Total detections: 5
  person 0.92 at (49,396)-(236,904)
  bus 0.92 at (0,229)-(804,744)
  person 0.91 at (670,393)-(809,880)
  person 0.90 at (223,403)-(345,862)
  person 0.50 at (0,553)-(68,869)

Expected Output

DLStreamer Sample

The pipeline below runs the FP16 YOLO26 detector on the sample video via gvadetect, overlays bounding boxes, saves the annotated result to output_dlstreamer.mp4, and prints all detections per frame.

Notes on running this sample:

Use the FP16 IR (yolo26n_openvino_model/yolo26n.xml). Class names are read automatically from the model's embedded metadata.yaml by DLStreamer 2026.0+ -- no external labels-file is required.

Export PYTHONPATH so the DLStreamer Python module is importable:

bash
1source /opt/intel/openvino_2026/setupvars.sh 2source /opt/intel/dlstreamer/scripts/setup_dls_env.sh 3export PYTHONPATH=/opt/intel/dlstreamer/python:\ 4/opt/intel/dlstreamer/gstreamer/lib/python3/dist-packages:${PYTHONPATH:-}

python

import gi

gi.require_version("Gst", "1.0")
gi.require_version("GstAnalytics", "1.0")
from gi.repository import Gst, GLib, GstAnalytics

Gst.init([])

INPUT_VIDEO = "test_video.mp4"

# For CPU: change device=GPU to device=CPU.
# For NPU: change device=GPU to device=NPU (batch-size=1, nireq=4 recommended).
pipeline_str = (
    f"filesrc location={INPUT_VIDEO} ! decodebin3 ! "
    "videoconvert ! "
    "gvadetect model=yolo26n_openvino_model/yolo26n.xml "
    "device=GPU "
    "threshold=0.4 ! queue ! "
    "gvawatermark ! videoconvert ! video/x-raw,format=I420 ! "
    "openh264enc ! h264parse ! "
    "mp4mux ! filesink name=sink location=output_dlstreamer.mp4"
)
pipeline = Gst.parse_launch(pipeline_str)


def on_buffer(pad, info):
    buf = info.get_buffer()
    rmeta = GstAnalytics.buffer_get_analytics_relation_meta(buf)
    if rmeta is None:
        return Gst.PadProbeReturn.OK
    idx = 1
    while True:
        ok, od = rmeta.get_od_mtd(idx)
        if not ok:
            break
        label = GLib.quark_to_string(od.get_obj_type())
        _, x, y, w, h, conf = od.get_location()
        print(f"  {label} at ({int(x)},{int(y)})", flush=True)
        idx += 1
    return Gst.PadProbeReturn.OK


sink = pipeline.get_by_name("sink")
sink_pad = sink.get_static_pad("sink")
sink_pad.add_probe(Gst.PadProbeType.BUFFER, on_buffer)

pipeline.set_state(Gst.State.PLAYING)
bus = pipeline.get_bus()
bus.timed_pop_filtered(
    Gst.CLOCK_TIME_NONE,
    Gst.MessageType.EOS | Gst.MessageType.ERROR,
)
pipeline.set_state(Gst.State.NULL)

Expected Output

Device targets:

device=GPU -- default in the sample code.
device=CPU -- change device=GPU to device=CPU.
device=NPU -- change device=GPU to device=NPU; use batch-size=1 and nireq=4 for best NPU utilization.

License

Licensed under the MIT License. See LICENSE for details.

Intel/object-detection

Object Detection

Overview

Prerequisites

Getting Started

Download and Quantize Model

Optional: Select a Different Variant or Precision

Precision / Device Compatibility

OpenVINO Sample

Try It on a Sample Image

Expected Output

DLStreamer Sample

Expected Output

License

References

Intel/
object-detection