Awesome

ObjectDetection-CoreML

supporting models: YOLOv8, YOLOv5, YOLOv3, MobileNetV2+SSDLite

This project is Object Detection on iOS with Core ML.<br>If you are interested in iOS + Machine Learning, visit here you can see various DEMOs.<br>

Requirements

• Xcode 10.3+
• iOS 13.0+
• Swift 4.2

How To Build and Run the Project

1. Clone the project

git clone https://github.com/tucan9389/ObjectDetection-CoreML

2. Prepare Core ML model

• You can download COCO models or another model from here

Or if you want to make and use model with custom dataset,

1. follow roboflow tutorial from scratch or yolov5 repo's tutorial
2. and convert the .pt model to .mlmodel model with our issue.

3. Add the model to the project

By default, the project uses the yolov8s model. If you want to use another model, you can replace the model file in the project.

4. Set model name properly in ViewController.swift

5. Build and Run

How To Run with your own model

1. Convert your model to Core ML

At this moment(23.04.08), there is error when converting yolov8 models to Core ML. Once https://github.com/ultralytics/ultralytics/pull/1791 is merged, you can use the following steps. (Or you can use this PR alternatively.)

Pre-requirements

pip install ultralytics
pip install coremltools

Option 1) With shell

yolo export model=yolov8n.pt format=coreml nms

Option 2) With python script

# mian.py
from ultralytics import YOLO

if __name__ == '__main__':
    model = YOLO("yolov8n.pt", task='detect')  # load a pretrained model
    model.overrides['nms'] = True
    success = model.export(format="coreml")  # export the model to CoreML format

# in terminal
python main.py
# then you can see the `.mlpackage` or `.mlmodel` file in your current directory
# (btw you can check your current directory with `pwd` command)

2. Follow the steps above from Step 3

Models

Model Matadata

Model Size, Minimum iOS Version, Download Link

Trained Dataset Infos

• https://github.com/ultralytics/yolov5/blob/9da6d0f9f5bc37fa386b7b82d2a963f94650949a/data/coco.yaml#L17-L97

• Bagel
• Banana
• Coffee
• Croissant
• Egg
• Waffle

Performance

Build Setting:<br> Xcoede > Build Settings > Apple Clang - Code Generation > Optimization Level > Fastest [-O3]

Infernece Time (ms)

Total Time (ms)

FPS

See also

• motlabs/awesome-ml-demos-with-ios<br> : The challenge using machine learning model created from tensorflow on iOS
• Machine Learning - Models - Apple Developer
• hollance/coreml-survival-guide
• vonholst/SSDMobileNet_CoreML<br> : iOS project for object detection(SSDMobileNet V1) using Core ML.
• ultralytics/ultralytics<br> : YOLOv8 repository
• ultralytics/yolov5<br> : YOLOv5 repository