Error reported when setting model single channel ch: 1

[zqstdy]

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• [X] I have searched the YOLOv8 issues and found no similar bug report.

YOLOv8 Component

Train

Bug

The model supports channel settings, but single training reports errors. I think this is a bug or an area that needs improvement

8.2.6

Environment

8.2.6

Minimal Reproducible Example

No response

Additional

No response

Are you willing to submit a PR?

• [ ] Yes I'd like to help by submitting a PR!

[github-actions[bot]]

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[glenn-jocher]

@zqstdy hello! Thanks for reaching out and detailing the issue you encountered with the single-channel input setup. From what you have described and the screenshots, it seems like there might be something specific about the configuration causing this problem.

Could you please share the configuration snippet you used, especially around input resolutions and channel settings? Also, enabling channels for one might require checking the compatibility of subsequent layers or operations specific to single-channel data. Here's a brief example of how you set the channels to 1 in your data configuration file:

# Inside the YAML data configuration file
nc: 1  # number of channels

Furthermore, ensure your preprocessing steps, if any, are converting images to grayscale correctly (assuming you are using grayscale images for a 1-channel model). For now, verifying your data preprocessing steps and the configuration snippet would be helpful to further diagnose this.

Looking forward to your response!

[zqstdy]

A grayscale image with a size of 640 * 640 Then I want to train the model as a single channel grayscale image, rather than a three channel BGR Can we currently achieve such a demand?

[glenn-jocher]

Hello! Yes, you can train a YOLOv8 model with single-channel grayscale images. Make sure your dataset images are properly converted to grayscale and that your model configuration is set to handle one channel.

In your data.yaml, set the number of channels like this:

nc: 1  # number of channels

And ensure your preprocessing converts images to grayscale. If using custom data loading or preprocessing scripts, here’s a simple way to convert an image to grayscale with OpenCV:

import cv2
image = cv2.imread('path_to_image')
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

This should help you set up training for single-channel images! Let us know if you have more questions. 😊

[zqstdy]

First of all, thank you for your reply nc: 1 As far as I know, the number of categories is set, and the number of channels cannot be changed

ch: 1 The number of channels can be changed, but training will report an error

I hope to use single channel training to improve the inference efficiency of the model

You can do some related testing, but I haven't found any code for single channel processing in the source code yet

[glenn-jocher]

@zqstdy hello! Thank you for the additional information and clarification. Indeed, training YOLOv8 with single-channel images involves some specific setup. The nc: 1 property you've referenced is for adjusting the number of classes, not channels.

To configure your model for one-channel (grayscale) input, you'll need to modify the input layer of your network model to accept one channel. Although the public YOLOv8 repository does not support single-channel training directly through configuration only, this can technically be achieved by altering the model's architecture and preprocessing pipeline.

For the time being, to prepare your images as single-channel inputs, you can adjust your data loading or transformation stages to convert images to grayscale before feeding them into the network. Here's an example of how you might adjust a typical image loading function in PyTorch for grayscale:

from PIL import Image
import torchvision.transforms as transforms

def load_grayscale_image(image_path):
    with Image.open(image_path).convert('L') as img:  # Convert to grayscale
        transform = transforms.Compose([
            transforms.Resize((640, 640)),  # Resize to model input dimensions
            transforms.ToTensor()  # Convert image to tensor
        ])
        return transform(img).unsqueeze(0)  # Add batch dimension

And remember that for modifying the model to accept a single channel across all layers, you may need to dive into the model's definition and adjust the first convolutional layer's input channel parameter.

Unfortunately, if the current YOLOv8 setup or your project constraints don't allow architectural changes, you might have to explore other solutions or models that nativally support single-channel inputs. If you need further assistance adjusting the model, please let me know! 😊

[zqstdy]

I made the changes according to the method on my blog, but it still reported an error and I don't know where it's different https://blog.csdn.net/m0_56276747/article/details/136757921

[glenn-jocher]

@zqstdy hello! Thanks for reaching out and sharing the link to your blog post. It looks like there might be a small discrepancy in your configuration or setup compared to what YOLOv8 expects. Could you please share the specific error message you're seeing? Also, if possible, include a snippet of the code or configuration you changed. This will help identify what might be going wrong more accurately. 😊