Introduce iOS demo repo for YOLOv5 Core ML models 📱

[tucan9389]

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• I have searched the YOLOv5 issues and found no similar feature requests.

Description

Hi everyone 🙋‍♂️! Yesterday, my tucan9389/ObjectDetection-CoreML repository supported YOLOv5 models with model converting guidelines and a light-weight demo project.

I made guidelines for Core ML converting and how to run the model on iOS.

The only things if you want to run the yolov5 model on iOS are downloading the yolov5 .mlmodels, importing them into the repo Xcode project, and changing a line to the model name(like yolov5m()). You can check it out in detail in here.

Thanks to @mshamash, @mattrichard-datascience, and people who discussed in #5157 and #7263. Your conversation was really helpful.

If there is a better iOS demo repository for yolov5, please comment below(Until now, I couldn’t find it).
Or if you have any questions in the demo, please make an issue on the repo!
Any contribution or ideas are helpful and appreciated.

I hope this repo will be helpful and save your time.

Thank you.

Use case

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Additional

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Are you willing to submit a PR?

• Yes I'd like to help by submitting a PR!

[github-actions]

👋 Hello @tucan9389, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

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Requirements

Python>=3.7.0 with all requirements.txt installed including PyTorch>=1.7. To get started:

git clone https://github.com/ultralytics/yolov5  # clone
cd yolov5
pip install -r requirements.txt  # install

Environments

YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Google Colab and Kaggle notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Status

If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training (train.py), validation (val.py), inference (detect.py) and export (export.py) on macOS, Windows, and Ubuntu every 24 hours and on every commit.

[github-actions]

👋 Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs.

Access additional YOLOv5 🚀 resources:

• Wiki – https://github.com/ultralytics/yolov5/wiki
• Tutorials – https://docs.ultralytics.com/yolov5
• Docs – https://docs.ultralytics.com

Access additional Ultralytics ⚡ resources:

• Ultralytics HUB – https://ultralytics.com/hub
• Vision API – https://ultralytics.com/yolov5
• About Us – https://ultralytics.com/about
• Join Our Team – https://ultralytics.com/work
• Contact Us – https://ultralytics.com/contact

Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!

Thank you for your contributions to YOLOv5 🚀 and Vision AI ⭐!