Error reported when setting model single channel ch: 1 #11247
Comments
|
👋 Hello @zqstdy, thank you for your interest in Ultralytics YOLOv8 🚀! We recommend a visit to the Docs for new users where you can find many Python and CLI usage examples and where many of the most common questions may already be answered. If this is a 🐛 Bug Report, please provide a minimum reproducible example to help us debug it. If this is a custom training ❓ Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our Tips for Best Training Results. Join the vibrant Ultralytics Discord 🎧 community for real-time conversations and collaborations. This platform offers a perfect space to inquire, showcase your work, and connect with fellow Ultralytics users. InstallPip install the pip install ultralyticsEnvironmentsYOLOv8 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
Status
If this badge is green, all Ultralytics CI tests are currently passing. CI tests verify correct operation of all YOLOv8 Modes and Tasks on macOS, Windows, and Ubuntu every 24 hours and on every commit. |
|
@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 channelsFurthermore, 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! |
|
A grayscale image with a size of 640 * 640 |
|
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 channelsAnd 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. 😊 |
|
First of all, thank you for your reply
ch: 1
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 |
|
@zqstdy hello! Thank you for the additional information and clarification. Indeed, training YOLOv8 with single-channel images involves some specific setup. The 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 dimensionAnd 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! 😊 |
|
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 |
|
@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. 😊 |
|
👋 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 ⭐ |
Search before asking
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?
The text was updated successfully, but these errors were encountered: