ultralytics / hub

Ultralytics HUB tutorials and support

Home Page:https://hub.ultralytics.com

Geek Repo:Geek Repo

Github PK Tool:Github PK Tool

convert the trained yolov8 model to

Smith-S-S opened this issue · comments

Search before asking

Question

When I am trying to convert the trained yolov8 model to ( !yolo export model="/content/drive/MyDrive/PPE_Detection_YOLOV8/dataset/runs/detect/train/weights/best.pt" format= tfjs)
its only giving this "best_saved_model" but I want "best_web_model" in the tutorial iam following one they run the code they got best_web_model. can you help me with this guys.

best_saved_model: this was I got
Screenshot 2024-02-16 at 1 08 15 AM

best_web_model: They got

Screenshot 2024-02-16 at 1 07 56 AM Screenshot 2024-02-16 at 1 06 21 AM

Additional

No response

👋 Hello @Smith-S-S, 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.

If this is a 🐛 Bug Report, please provide screenshots and steps to reproduce your problem to help us get started working on a fix.

If this is a ❓ Question, please provide as much information as possible, including dataset, model, environment details etc. so that we might provide the most helpful response.

We try to respond to all issues as promptly as possible. Thank you for your patience!

dataset
!pip install roboflow

from roboflow import Roboflow
rf = Roboflow(api_key="R38i0N5gBDDkzbmApwoO")
project = rf.workspace("aabbcceeffgg").project("brain-tumor-detection-69d9s")
dataset = project.version(2).download("yolov5")

full code:
Screenshot 2024-02-16 at 1 43 17 AM
Screenshot 2024-02-16 at 1 43 51 AM
Screenshot 2024-02-16 at 1 43 17 AM
Screenshot 2024-02-16 at 1 43 08 AM
Screenshot 2024-02-16 at 1 43 51 AM

@Smith-S-S hello! It seems like you're experiencing an issue with the naming of the exported model when converting a YOLOv8 model to TensorFlow.js format. The naming convention (best_saved_model vs best_web_model) might differ based on the version of the export tool or the specific command used.

To resolve this, please ensure you're following the latest documentation and using the correct export command as per the Ultralytics HUB Docs. If the issue persists, it could be related to a recent update or change in the export process.

For further assistance, please provide the exact command you're using and any output logs that show the issue. This will help us understand the problem better and guide you accordingly. If there's a discrepancy with the tutorial you're following, it might also be helpful to check if there's an updated version or notes on the naming convention changes.

Remember, the Ultralytics community and the team are here to help, so don't hesitate to reach out if you need more guidance! 😊

Thank you too much guys

@Smith-S-S you're welcome! If you have any more questions or need further assistance in the future, feel free to reach out. Happy to help and wishing you the best with your YOLOv8 projects! 😄🚀

👋 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:

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 ⭐