SingleChannel Train

[SwEngine]

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Question

I am currently trying to solve grayscale training, however, the issues opened in the repo did not helped me. Is there anyone has successfully implemented grayscale training? I could not change architecture successfully. First layer still expects 3 channels.

from n params module arguments 0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2] 1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2] 2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True] 3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2] 4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True] 5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] 6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True] 7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] 8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True] 9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5] 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] 12 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1] 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] 15 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1] 16 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] 18 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1] 19 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] 21 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1] 22 [15, 18, 21] 1 752872 ultralytics.nn.modules.head.Detect [8, [64, 128, 256]] Model summary: 225 layers, 3012408 parameters, 3012392 gradients, 8.2 GFLOPs

Additional

No response

[SwEngine]

Single channel inference is beneficial for cpu inference.

[glenn-jocher]

@SwEngine to train a YOLOv8 model with grayscale images, you'll need to adjust the first layer of the network to accept a single channel input. This involves changing the 3 in the arguments of the first convolutional layer to 1, which corresponds to the number of color channels in your images. After making this modification, ensure that your dataset is also prepared with single-channel images. Remember to adjust any data augmentation processes to handle single-channel data as well. For further guidance, please refer to our documentation on custom training configurations. Happy training! 😊🚀

[SwEngine]

How can I change the first convolutional layer? Any suggestion?

[glenn-jocher]

@SwEngine to change the first convolutional layer for grayscale training, you'll need to modify the configuration file where the model architecture is defined. Locate the first convolutional layer and change the input channels from 3 to 1. Ensure your dataset consists of single-channel images before proceeding with training. For detailed steps, please refer to the model configuration section in our documentation. 🛠️👁️

[glenn-jocher]

@SwEngine noted, we'll consider adding support for single-channel training and inference in future updates. Thank you for the feedback! 🙌🛠️

[rusvagzur]

@SwEngine have you tried adding ch: 1 in the beginning of the .yml config file? Something like:

Ultralytics YOLO 🚀, AGPL-3.0 license

YOLOv8 object detection model with P3-P6 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect

Parameters

nc: 80 # number of classes
ch: 1
....rest of the config file

[glenn-jocher]

@SwEngine apologies for the confusion earlier. If you've already tried modifying the .yml configuration file without success, we appreciate your patience as we work to improve our support for single-channel training and inference. Your input is valuable, and we'll take it into account for future updates. Thank you for bringing this to our attention. 🙏🛠️

[bennaa]

Are there any updates? Did someone managed to train a grayscale model?

[bzisl]

Still not. I plan to restart the work on this topic next month. I'm currently overloaded in my current work.

[glenn-jocher]

@bzisl to train a YOLOv8 model on grayscale images, you need to modify the first convolutional layer to accept a single channel input instead of the default three channels for RGB images. Here's a concise guide on how to do this:

1. Modify the Model Configuration: Locate the .yaml file that defines your model architecture. In the first layer, change the number of input channels from 3 to 1. For example, if the first layer is defined as - [3, 16, 3, 2], change it to - [1, 16, 3, 2].

2. Preprocess the Dataset: Ensure that your dataset images are converted to grayscale. You can use OpenCV or PIL to convert RGB images to grayscale.

3. Train the Model: After making the changes to the model configuration file, you can train the model using the modified .yaml file.

If you encounter any issues or need further assistance, feel free to reach out to the community or open a new discussion in the Ultralytics YOLOv8 Discussions. 🚀

[bennaa]

I found a solution using https://github.com/cparcerisas/ultralytics/tree/sound-detection-adaptations fork from #948 and running model.train(data=path, amp=False).

The problem is that the default amp=True downloads automatically a yolov8n.pt file with pretrained weights for 3 channels images, and that file set those weights in the model overwriting the 1 channel one.

[glenn-jocher]

@bennaa to train a YOLOv8 model on grayscale images, you need to modify the first convolutional layer to accept a single channel input instead of the default three channels for RGB images. Here's how you can do it:

1. Modify the Model Configuration File: The model configuration file (.yaml) defines the architecture of the model. You need to change the input channels of the first convolutional layer from 3 to 1. For example, if your model configuration file is yolov8n.yaml, you would change the first layer from [3, 16, 3, 2] to [1, 16, 3, 2].

2. Preprocess the Input Images: Ensure that your dataset preprocessing pipeline converts RGB images to grayscale before feeding them into the model. You can use OpenCV or PIL to convert images to grayscale.

3. Load the Modified Model for Training: Load the modified model configuration file and proceed with the training as usual.

Here's an example of how you might modify the model configuration file and train the model:

# Example of modified first layer in yolov8n.yaml
backbone:
  [[-1, 1, ultralytics.nn.modules.conv.Conv, [1, 16, 3, 2]],  # Change the first value from 3 to 1
   ... # Rest of the layers
  ]

And in your training script:

from ultralytics import YOLO

# Load the modified model configuration
model = YOLO('yolov8n_modified.yaml')  # Use the path to your modified .yaml file

# Train the model using the 'coco128.yaml' dataset for 3 epochs
results = model.train(data='coco128.yaml', epochs=3)

Make sure that your dataset loading and preprocessing code converts the images to grayscale. Here's an example using OpenCV:

import cv2

# Function to convert an image to grayscale
def load_grayscale_image(image_path):
    image = cv2.imread(image_path)
    grayscale_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    return grayscale_image

# Use this function to preprocess your images before training

Remember to adjust your dataset preprocessing pipeline accordingly to provide single-channel grayscale images to the model during training.

[rusvagzur]

I found a solution using https://github.com/cparcerisas/ultralytics/tree/sound-detection-adaptations fork from #948 and running model.train(data=path, amp=False).

The problem is that the default amp=True downloads automatically a yolov8n.pt file with pretrained weights for 3 channels images, and that file set those weights in the model overwriting the 1 channel one.

This is actually a very good point. Hope it doesn't go unnoticed. In the past, Ultralytics found some issue from people which the amp option would not work with their GPUs. Therefore they added the amp=True or False. Later they added the amp check to automatically set it to false when amp is not available on your GPU. For that, Ultralytics downloads yolov8n.pt and performs a test. In theory, it should not interfere with your "single channel" architecture when you do the modifications on the .yaml file, so I wonder what's actually happening.

[glenn-jocher]

@rusvagzur great catch on the amp behavior, and thanks for sharing your workaround! It's interesting that setting amp=False prevents the automatic download of the yolov8n.pt file with 3-channel weights. It sounds like there might be an opportunity to refine the automatic mixed precision (AMP) check to better support single-channel models. We'll keep an eye on this for future updates. Keep up the good work! 👍🔍