Object Counting about ultralytics

[github-actions]

👋 Hello @MBilal187, thank you for your interest in Ultralytics YOLOv8 🚀! We recommend a visit to the Docs for new users where you can find many Python and CLI usage examples and where many of the most common questions may already be answered.

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Install

Pip install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

Environments

YOLOv8 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• 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 Ultralytics CI tests are currently passing. CI tests verify correct operation of all YOLOv8 Modes and Tasks on macOS, Windows, and Ubuntu every 24 hours and on every commit.

[glenn-jocher]

Hello!

Thank you for reaching out with your question. Yes, object counting is supported in YOLOv8. You can utilize the YOLOv8 models for this purpose, and for deployment on an Android app, converting the model to TensorFlow Lite is indeed the correct approach.

For object counting, you might consider starting with the YOLOv8n model due to its balance between speed and accuracy. Once you have the model trained or if you're using a pre-trained model, you can export it to TensorFlow Lite format, which is suitable for Android applications.

If you need detailed guidance on exporting models to TensorFlow Lite, please refer to the model export section of our documentation.

Best of luck with your implementation, and do not hesitate to ask if you have more questions!

[github-actions]

👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help.

For additional resources and information, please see the links below:

• Docs: https://docs.ultralytics.com
• HUB: https://hub.ultralytics.com
• Community: https://community.ultralytics.com

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 YOLO 🚀 and Vision AI ⭐