object detection using yolov8 model for ios

mobile/README.md

@@ -49,6 +49,12 @@ The example app uses object detection which is able to continuously detect the o
 - [iOS Object Detection](examples/object_detection/ios)
 - [Android Object Detection](examples/object_detection/android)
 
+### Object Detection using yolov8
+
+This example app use yolov8 model to identify objects in images and provides bounding boxes, identified class and score.
+
+- [iOS Object Detection (yolov8)](examples/object_detection_yolov8/ios)
+
 ### Xamarin VisionSample
 
 The [Xamarin.Forms](https://dotnet.microsoft.com/apps/xamarin/xamarin-forms) example app demonstrates the use of several vision-related models, from the ONNX Model Zoo collection.

mobile/examples/object_detection_yolov8/ios/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 Eumentis
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

mobile/examples/object_detection_yolov8/ios/README.md

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+## ONNX Runtime Mobile object detection iOS sample application
+
+This sample application makes use of Yolov8 object detection model to identify objects in images and provides identified class, bounding boxes and score as a result.
+
+
+### Model
+We use pre-trained yolov8 model in this sample app. The original yolov8n.pt model can be downloaded. [Here](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8n.pt) <br/>
+- We converted this yolov8n.pt model to yolov8n.onnx using https://docs.ultralytics.com/modes/export/#usage-examples <br/>
+- Generated yolov8n.ort from yolov8n.onnx using https://onnxruntime.ai/docs/performance/model-optimizations/ort-format-models.html#convert-onnx-models-to-ort-format
+
+### Requirements
+- Install Xcode 12.5 and above (preferably latest version)
+
+### Steps to build and run
+
+#### Step 1: Clone the ONNX runtime mobile examples source code
+
+Clone this repository to get the sample application. Then open the project under the folder `mobile\examples\object_detection_yolov8\ios`.
+
+
+#### Step 2: Install app
+
+1. In terminal, run pod install under onnxruntime-object-detection-yolo-ios/yolo-ios to generate the workspace file.
+   - At the end of this step, you should get a file called yolo-ios.xcworkspace.
+2. Open onnxruntime-object-detection-yolo-ios/yolo-ios/yolo-ios.xcworkspace in Xcode and make sure to select your corresponding development team under Target-General-Signing for a proper codesign 
+
+#### Step 3: Connect IOS Device and Run the app
+  Connect your IOS Device to your computer or select the IOS Simulator in Xcode.
+
+  Then after running this app it will be installed on your device or simulator.
+
+  Now you can try and test the object detection IOS app by 
+  1. Click the "+" button from top, it will open your photo album, pick any   image that you want to test.
+  2. Your selected image will be shown on the screen. Then click the "Start Processing" button, and it will begin identifying objects in the image/photo. The loader will appear on the screen while the image is being processed.
+  3. After completion, you'll see an image with bounding boxes and the identified class name.
+
+#
+Here are some sample example screenshots of the app.
+
+<img width=20% src="yolo-ios/yolo-ios/images/IMG_1139.PNG" alt="App Screenshot 1" />
+<img width=20% src="yolo-ios/yolo-ios/images/IMG_1140.PNG" alt="App Screenshot 2" />

mobile/examples/object_detection_yolov8/ios/yolo-ios/.gitignore

@@ -0,0 +1,2 @@
+/Pods/
+.DS_Store

mobile/examples/object_detection_yolov8/ios/yolo-ios/Podfile

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+# Uncomment the next line to define a global platform for your project
+# platform :ios, '9.0'
+
+target 'yolo-ios' do
+  # Comment the next line if you don't want to use dynamic frameworks
+  use_frameworks!
+
+  # Pods for yolo-ios
+
+  target 'yolo-iosTests' do
+    inherit! :search_paths
+    # Pods for testing
+  end
+
+  target 'yolo-iosUITests' do
+    # Pods for testing
+  end
+
+  # Pods for ORTObjectDetection
+ pod 'onnxruntime-objc'
+
+end

mobile/examples/object_detection_yolov8/ios/yolo-ios/Podfile.lock

@@ -0,0 +1,22 @@
+PODS:
+  - onnxruntime-c (1.17.1)
+  - onnxruntime-objc (1.17.1):
+    - onnxruntime-objc/Core (= 1.17.1)
+  - onnxruntime-objc/Core (1.17.1):
+    - onnxruntime-c (= 1.17.1)
+
+DEPENDENCIES:
+  - onnxruntime-objc
+
+SPEC REPOS:
+  trunk:
+    - onnxruntime-c
+    - onnxruntime-objc
+
+SPEC CHECKSUMS:
+  onnxruntime-c: 58803cb3fcc1fb0a7baa9b16b2bad119d6e3eb78
+  onnxruntime-objc: 089530244113a131305642b7e03fa6f8a9ee9ae6
+
+PODFILE CHECKSUM: 9379fba48d706acf709a30decc58da0f9ab2b681
+
+COCOAPODS: 1.14.3