Inquiry About Live Video Feed Deployment on Ultralytics Hub

[Hariii17]

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Question

Dear Ultralytics Support Team,

I hope this message finds you well.
I have been working on an object detection project using a CCTV camera, but my local system struggles with CPU utilization, often reaching 100% and causing the process to terminate. To address this, I have transitioned to using Ultralytics Hub for training my model.

My query is regarding the deployment phase: Is it possible to deploy the trained object detection model on a live video feed directly within Ultralytics Hub? Given that my local system lacks the necessary GPU compute capabilities, I am looking for a solution that leverages the computational resources available on Ultralytics Hub.

Your guidance on this matter would be highly appreciated.

Additional

No response

[github-actions]

👋 Hello @Hariii17, thank you for raising an issue about Ultralytics HUB 🚀! Please visit our HUB Docs to learn more:

• Quickstart. Start training and deploying YOLO models with HUB in seconds.
• Datasets: Preparing and Uploading. Learn how to prepare and upload your datasets to HUB in YOLO format.
• Projects: Creating and Managing. Group your models into projects for improved organization.
• Models: Training and Exporting. Train YOLOv5 and YOLOv8 models on your custom datasets and export them to various formats for deployment.
• Integrations. Explore different integration options for your trained models, such as TensorFlow, ONNX, OpenVINO, CoreML, and PaddlePaddle.
• Ultralytics HUB App. Learn about the Ultralytics App for iOS and Android, which allows you to run models directly on your mobile device.
  • iOS. Learn about YOLO CoreML models accelerated on Apple's Neural Engine on iPhones and iPads.
  • Android. Explore TFLite acceleration on mobile devices.
• Inference API. Understand how to use the Inference API for running your trained models in the cloud to generate predictions.

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[pderrenger]

@Hariii17 hello,

Thank you for reaching out and detailing your situation. I'm glad to hear that you've chosen Ultralytics HUB for training your model!

Regarding your query on deploying the trained model for live video feed analysis directly within Ultralytics HUB: Currently, HUB primarily supports training and model management, and does not directly host live video feed processing. However, you can export your trained model to various formats suitable for deployment on different platforms that support live video processing.

For real-time video processing, you might consider deploying your model on a cloud service with GPU support, or on local servers equipped with the necessary hardware. Our Inference API could also be a viable option, depending on your specific requirements and setup.

If you need further assistance or have more questions, please feel free to ask!

[Hariii17]

Suppose if this project is assigned for your team will you be able to do it. Can we discuss about this?

[pderrenger]

@Hariii17 hello,

Thank you for considering our team for your project! We're always ready to discuss potential collaborations and how we can assist you. Please let us know a convenient time for you, or you can directly share more details about your project requirements here.

Looking forward to our discussion! 😊

[Hariii17]

Yeah thats good if you are up for collaboration can you please provide your contact number and your free time ?

[pderrenger]

Hello @Hariii17,

Thank you for your interest in collaborating! For privacy and security reasons, we handle all professional communications through official channels. Could you please reach out to us via our support email provided on the Ultralytics HUB website? From there, we can schedule a time to discuss your project in detail.

Looking forward to hearing from you soon!

[Hariii17]

Okay can you mention your support email id.

[sergiuwaxmann]

@Hariii17 Hi!

You you can use this form:
https://www.ultralytics.com/contact

[Hariii17]

Done

[pderrenger]

Hello @Hariii17,

Great! If you need any further assistance, feel free to reach out. Happy to help! 😊