Object Detection (YOLOv8 / YOLOv11)

Build a real-time on-device object detection application using YOLOv8 or YOLOv11 with ZETIC Melange. This tutorial walks you through exporting the model, deploying it to Melange, and running inference on Android and iOS.

What You Will Build

An on-device object detection application that identifies and localizes objects in real-time camera frames using YOLOv8 or YOLOv11 models accelerated by NPU hardware.

Prerequisites

• A ZETIC Melange account with a Personal Key (sign up at melange.zetic.ai)
• Python 3.8+ with ultralytics, opencv-python, and numpy installed
• Android Studio or Xcode for mobile deployment

What is YOLOv11?

YOLOv11 is the latest version of the acclaimed real-time object detection and image segmentation model by Ultralytics.

• Official documentation: YOLOv11 Docs
• Currently, only detector mode is supported. Additional features will be supported later.

Step 1: Export the Model

Export the YOLOv11 model to ONNX format. You will get yolo11n.onnx after running this script:

from ultralytics import YOLO
import torch

model = YOLO("yolo11n.pt")
model.export(format="onnx", opset=12, simplify=True, dynamic=False, imgsz=640)

Step 2: Prepare Input Sample

Prepare your input from an image file:

import cv2
import numpy as np

def preprocess_image(image_path, target_size=(640, 640)):
    img = cv2.imread(image_path)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    img = cv2.resize(img, target_size)
    img = img.astype(np.float32) / 255.0
    img = np.transpose(img, (2, 0, 1))
    img = np.expand_dims(img, axis=0)
    return img

Step 3: Generate Melange Model

Upload the model and inputs via the Melange Dashboard or use the CLI:

zetic gen -p $PROJECT_NAME -i images.npy yolo11n.onnx

Step 4: Implement ZeticMLangeModel

Initialize the Melange model in your mobile application and run inference.

// (1) Load model
val model = ZeticMLangeModel(this, PERSONAL_KEY, MODEL_NAME)

// (2) Prepare model inputs
val inputs: Array<Tensor> = // Prepare your inputs

// (3) Run and get output tensors of the model
val outputs = model.run(inputs)

// (1) Load model
let model = try ZeticMLangeModel(personalKey: PERSONAL_KEY, name: MODEL_NAME, version: VERSION)

// (2) Prepare model inputs
let inputs: [Tensor] = [] // Prepare your inputs

// (3) Run and get output tensors of the model
let outputs = try model.run(inputs)

Step 5: Use the YOLOv8 Pipeline

We provide a YOLOv8 feature extractor as an Android and iOS module. This feature extractor works with both YOLOv8 and YOLOv11 models.

val model = ZeticMLangeModelWrapper(this, PERSONAL_KEY, MODEL_NAME)

val pipeline = ZeticMLangePipeline(
    feature = YOLOv8(this, model = model),
    inputSource = CameraSource(this, preview.holder, preferredSize),
)

pipeline.loop { result ->
    // visualize YOLO result here
}

import ZeticMLange
import ext

let model = try ZeticMLangeModelWrapper(PERSONAL_KEY, MODEL_NAME)

let pipeline = ZeticMLangePipeline(feature: model, inputSource: CameraSource())

pipeline.startLoop()
while true {
    let frame = pipeline.latestResult
    // visualize YOLO result here
}
pipeline.stopLoop()

Conclusion

With ZETIC Melange, you can build on-device AI object detection applications with NPU acceleration in minutes. We continuously upload models to our examples and HuggingFace page.