Ultralytics Yolo | GitLocker.com Product

Description:

ultralytics yolo

Ultralytics YOLO for Flutter #
A Flutter plugin for integrating Ultralytics YOLO computer vision models into your mobile apps. The plugin supports both Android and iOS platforms, and provides APIs for object detection and image classification.
Features #

Feature
Android
iOS

Detection
✅
✅

Classification
✅
✅

Pose Estimation
❌
❌

Segmentation
❌
❌

OBB Detection
❌
❌

Before proceeding further or reporting new issues, please ensure you read this documentation thoroughly.
Usage #
Ultralytics YOLO is designed specifically for mobile platforms, targeting iOS and Android apps. The plugin leverages Flutter Platform Channels for communication between the client (app/plugin) and host (platform), ensuring seamless integration and responsiveness. All processing related to Ultralytics YOLO APIs is handled natively using Flutter's native APIs, with the plugin serving as a bridge between your app and Ultralytics YOLO.
Prerequisites #
Export Ultralytics YOLO Models
Before you can use Ultralytics YOLO in your app, you must export the required models. The exported models are in the form of .tflite and .mlmodel files, which you can then include in your app. Use the Ultralytics YOLO CLI to export the models.

IMPORTANT: The parameters in the commands above are mandatory. Ultralytics YOLO plugin for Flutter only supports the models exported using the commands above. If you use different parameters, the plugin will not work as expected. We're working on adding support for more models and parameters in the future.

The following commands are used to export the models:

Android
Detection
yolo export format=tflite model=yolov8n imgsz=320 int8
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Classification
yolo export format=tflite model=yolov8n-cls imgsz=320 int8
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Then use file yolov8n_int8.tflite or yolov8n-cls_int8.tflite

iOS
To export the YOLOv8n Detection model for iOS, use the following command:
yolo export format=mlmodel model=yolov8n imgsz=[320, 192] half nms
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Installation #
After exporting the models, you will get the .tflite and .mlmodel files. Include these files in your app's assets folder.
Permissions
Ensure that you have the necessary permissions to access the camera and storage.

Android
Add the following permissions to your AndroidManifest.xml file:
<uses-permission android:name="android.permission.CAMERA" />
<uses-permission android:name="android.permission.WRITE_EXTERNAL_STORAGE" />
<uses-permission android:name="android.permission.READ_EXTERNAL_STORAGE" />
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iOS
Add the following permissions to your `Info.plist` file:
<key>NSCameraUsageDescription</key>
<string>Camera permission is required for object detection.</string>
<key>NSPhotoLibraryUsageDescription</key>
<string>Storage permission is required for object detection.</string>
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Add the following permissions to your Podfile:
post_install do |installer|
installer.pods_project.targets.each do |target|
flutter_additional_ios_build_settings(target)

# Start of the permission_handler configuration
target.build_configurations.each do |config|
config.build_settings['GCC_PREPROCESSOR_DEFINITIONS'] ||= [
'$(inherited)',

## dart: PermissionGroup.camera
'PERMISSION_CAMERA=1',

## dart: PermissionGroup.photos
'PERMISSION_PHOTOS=1',
]
end
# End of the permission_handler configuration
end
end
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Usage #
Predictor
Create a predictor object using the LocalYoloModel class. This class requires the following parameters:
final model = LocalYoloModel(
id: id,
task: Task.detect /* or Task.classify */,
format: Format.tflite /* or Format.coreml*/,
modelPath: modelPath,
metadataPath: metadataPath,
);
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Object Detector
final objectDetector = ObjectDetector(model: model);
await objectDetector.loadModel();
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Image Classifier
final imageClassifier = ImageClassifier(model: model);
await imageClassifier.loadModel();
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Camera Preview
The UltralyticsYoloCameraPreview widget is used to display the camera preview and the results of the prediction.
final _controller = UltralyticsYoloCameraController();
UltralyticsYoloCameraPreview(
predictor: predictor, // Your prediciton model data
controller: _controller, // Ultralytics camera controller
// For showing any widget on screen at the time of model loading
loadingPlaceholder: Center(
child: Wrap(
direction: Axis.vertical,
crossAxisAlignment: WrapCrossAlignment.center,
children: [
const CircularProgressIndicator(
color: Colors.white,
strokeWidth: 2,
),
const SizedBox(height: 20),
Text(
'Loading model...',
style: theme.typography.base.copyWith(
color: Colors.white,
fontSize: 14,
),
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Image
Use the detect or classify methods to get the results of the prediction on an image.
objectDetector.detect(imagePath: imagePath)
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or
imageClassifier.classify(imagePath: imagePath)
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Issues #
Please report any issues you find with the plugin on the GitHub repository. We'll do our best to address them as soon as possible.
Contributing #
We love your input! YOLOv5 and YOLOv8 would not be possible without help from our community. Please see our Contributing Guide to get started, and fill out our Survey to send us feedback on your experience. Thank you 🙏 to all our contributors!
License #
Ultralytics YOLO for Flutter is licensed under the AGPL 3.0 License. Ultralytics offers two licensing options to accommodate diverse use cases:

AGPL-3.0 License: This OSI-approved open-source license is ideal for students and enthusiasts, promoting open collaboration and knowledge sharing. See the LICENSE file for more details.
Enterprise License: Designed for commercial use, this license permits seamless integration of Ultralytics software and AI models into commercial goods and services, bypassing the open-source requirements of AGPL-3.0. If your scenario involves embedding our solutions into a commercial offering, reach out through Ultralytics Licensing.