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[nguyenvandang1998]

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How to train and predict yolo11 in ultralytics hub with grayscale images? I couldn't find any settings related to grayscale images when training with ultralytics hub

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[UltralyticsAssistant]

👋 Hello @nguyenvandang1998, thank you for raising an issue about Ultralytics HUB 🚀! Please check out our HUB Docs to learn more about managing your projects and models:

• Quickstart. Get started with training and deploying YOLO models on HUB in seconds.
• Datasets: Preparing and Uploading. Discover how to prepare your datasets for grayscale images and upload them to HUB in YOLO format.
• Projects: Creating and Managing. Optimize your project organization by grouping your models effectively.
• Models: Training and Exporting. Train YOLOv5 and YOLOv8 models on custom datasets, including those with grayscale images, and export in various formats for easy deployment.
• Integrations. Find integration options for your trained models, such as TensorFlow, ONNX, OpenVINO, CoreML, and PaddlePaddle.
• Inference API. Use the Inference API to run your trained models in the cloud for generating predictions seamlessly.

If this is a 🐛 Bug Report, please provide a minimum reproducible example (MRE) with screenshots and steps to reproduce your issue to help us expedite the process.

If this is a ❓ Question, please share more details about your requirements, including your dataset, model, and environment setup, so our engineers can provide enhanced support 🎯.

This is an automated response, but know that an Ultralytics engineer will be here to assist you soon. We appreciate your patience and understanding!

[pderrenger]

@nguyenvandang1998 hello! 😊

Thank you for reaching out with your question about training YOLO models with grayscale images on Ultralytics HUB. While the platform primarily supports RGB images, you can still work with grayscale images by converting them to a compatible format.

Here's a step-by-step approach you can follow:

1. Convert Grayscale to RGB: Before uploading your dataset to Ultralytics HUB, you can convert your grayscale images to RGB format. This can be done using image processing libraries like OpenCV or PIL in Python. Here's a quick example using OpenCV:

   import cv2
   import glob
   
   # Path to your grayscale images
   image_paths = glob.glob('path/to/grayscale/images/*.png')
   
   for path in image_paths:
      # Read the grayscale image
      gray_image = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
      # Convert to RGB
      rgb_image = cv2.cvtColor(gray_image, cv2.COLOR_GRAY2RGB)
      # Save the RGB image
      cv2.imwrite(path.replace('grayscale', 'rgb'), rgb_image)

2. Upload Dataset: Once your images are converted, you can upload the dataset to Ultralytics HUB as usual.

3. Train Model: Proceed with training your YOLO model on the HUB. The model will treat the images as RGB, but since the content is originally grayscale, it should still perform effectively for your use case.

If you encounter any issues or have further questions, feel free to ask. The community and the Ultralytics team are here to help! 🚀

Best of luck with your project!

[nguyenvandang1998]

@pderrenger hi! Thank you but perhaps you misunderstood my meaning. What I mean is that I want to train yolo11 with grayscale images with 1 color channel, not RGB images. because I want to reduce model size and reduce prediction time.

[pderrenger]

Hello @nguyenvandang1998! 😊

Thank you for clarifying your question. Training YOLO models with grayscale images (1 color channel) is indeed a valid approach to reduce model size and potentially improve prediction time. While Ultralytics HUB primarily supports RGB images, you can still work with grayscale images by making some adjustments.

Here's how you can proceed:

1. Modify the Model Configuration: You can adjust the model's input layer to accept a single channel. This involves modifying the model architecture to handle grayscale images. If you're using a custom setup, you can change the input shape from (height, width, 3) to (height, width, 1).

2. Preprocess Your Dataset: Ensure your dataset is in grayscale format before uploading. If you're using a custom training script, you can load images in grayscale mode using libraries like OpenCV:

   import cv2
   
   # Load image in grayscale
   gray_image = cv2.imread('path/to/image.png', cv2.IMREAD_GRAYSCALE)

3. Custom Training Script: If you're using Ultralytics HUB, you might need to use a custom training script locally to handle the single-channel input. You can then upload the trained model to the HUB for deployment and further analysis.

4. Experiment and Validate: Once you've set up your training pipeline for grayscale images, it's important to validate the model's performance and ensure it meets your requirements.

If you have any more questions or need further assistance, feel free to ask. The community and the Ultralytics team are always here to help! 🚀

Best of luck with your project!