How can a model trained on Ultralytics HUB perform inference prediction on the test set? about yolov5

[github-actions]

👋 Hello @Aq114, 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>=3.8.0 with all requirements.txt installed including PyTorch>=1.8. To get started:

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

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• Google Cloud Deep Learning VM. See GCP Quickstart Guide
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• Docker Image. See Docker Quickstart Guide

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Introducing YOLOv8 🚀

We're excited to announce the launch of our latest state-of-the-art (SOTA) object detection model for 2023 - YOLOv8 🚀!

Designed to be fast, accurate, and easy to use, YOLOv8 is an ideal choice for a wide range of object detection, image segmentation and image classification tasks. With YOLOv8, you'll be able to quickly and accurately detect objects in real-time, streamline your workflows, and achieve new levels of accuracy in your projects.

Check out our YOLOv8 Docs for details and get started with:

pip install ultralytics

[glenn-jocher]

@Aq114 hello,

Thank you for reaching out and for providing detailed information about your issue. It sounds like you've encountered a KeyError during inference with a model trained on Ultralytics HUB. Let's address this step-by-step.

KeyError Issue

The KeyError: 5059 suggests that the model is trying to access a class index that doesn't exist in the names list. This can happen if there is a mismatch between the class indices in your dataset and the model's configuration.

Steps to Resolve

1. Verify Class Names: Ensure that the class names in your dataset match those expected by the model. You can check the data.yaml file used during training to confirm the class names and indices.

2. Update to Latest Versions: Make sure you are using the latest versions of YOLOv5 and PyTorch. This can resolve many issues related to compatibility and bugs.

   pip install --upgrade torch
   git pull https://github.com/ultralytics/yolov5

3. Reproduce the Issue: If the issue persists, please provide a minimum reproducible example. This helps us to investigate the problem more effectively. You can follow the guidelines here: Minimum Reproducible Example.

4. Local Inference: For performing local inference, you can use the following code snippet. Ensure that the best.pt file is correctly specified and that the data.yaml file is accessible.

   import torch
   
   # Load model
   model = torch.hub.load('ultralytics/yolov5', 'custom', path='path/to/best.pt')
   
   # Perform inference
   img = 'path/to/your/image.jpg'
   results = model(img)
   
   # Print results
   results.print()
   results.save()  # Save results to runs/hub

5. Batch Inference: If you need to perform batch inference on a test set, you can use the following approach:

   import torch
   from pathlib import Path
   
   # Load model
   model = torch.hub.load('ultralytics/yolov5', 'custom', path='path/to/best.pt')
   
   # Directory containing test images
   test_dir = Path('path/to/test/images')
   
   # Perform inference on all images in the directory
   for img_path in test_dir.glob('*.jpg'):
       results = model(img_path)
       results.save()  # Save results to runs/hub

Ultralytics HUB Batch Testing

Currently, Ultralytics HUB supports online inference for individual images. For batch testing, you would need to perform inference locally as described above.

If you continue to face issues, please share the exact steps and code you are using, along with any additional error messages. This will help us provide more targeted assistance.

Thank you for your patience and understanding. The YOLO community and the Ultralytics team are here to support you!

[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 ⭐