using multi class segmentation dataset for lower number of class segmentation task? about ultralytics

[glenn-jocher]

Hello,

Yes, you can use a multi-class segmentation dataset for a task with fewer classes by modifying your YAML configuration file. In your YAML file, you would specify only the classes you are interested in. For example, if your dataset originally has 10 classes and you want to use only 5, you can list only those 5 classes under the names section of your YAML file.

Make sure that the class indices in your dataset labels correspond to the correct classes as defined in your modified YAML file. This approach allows you to focus on a subset of classes without needing to alter the dataset itself.

If you have any more questions or need further assistance, feel free to ask!

[bhralzz]

thanks for your kindly replying.

I think plus than tuning the data.yaml file start them from 0 number and decreasing the classes, it should be considered to renumbering classes in label folders as they should be obeyed from yaml file. an I right?

Also if we want to do this journey for a two class dataset and want to do two 1 class segmentation task, whats your proposed roadmap?

please consider both questions and answer them.

regrads

[glenn-jocher]

@bhralzz hello,

You're absolutely right about the need to renumber the classes in your label files to match the class indices specified in your data.yaml file. This ensures consistency between your dataset annotations and the model's understanding of class labels.

For segmenting a two-class dataset into two separate one-class segmentation tasks, you can follow these steps for each task:

1. Modify the data.yaml: For each task, specify only the class you are interested in under the names section.
2. Prepare the Labels: Ensure that the labels for the class of interest are correctly indexed (e.g., 0 for the first task, and 0 for the second task when handled separately).
3. Train the Model: Train your model separately for each task with the modified data.yaml file.

This approach allows you to focus on one class at a time, potentially simplifying the learning process and improving performance for specific class detection.

[bhralzz]

after doing these steps:
I saw the below report for training for one of the classes:

whats the explanation behind such result?

and this is my code

device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = YOLO('yolov8n-seg.yaml') # build a new model from YAML
model = YOLO('yolov8n-seg.pt').to(device) # load a pretrained YOLOv8n segmentation model

results = model.train(data=dataset_path+'/data.yaml',
single_cls=True,
epochs=200,
patience=0, #I am setting patience=0 to disable early stopping.
batch= 64,
device=[0],
workers=0
)

any idea?

[glenn-jocher]

Hello @bhralzz,

Thank you for sharing the details of your training setup and the confusion matrices. From the images you've posted, it seems like there might be a few areas to explore to understand the results better:

1. Class Imbalance: If one class is significantly more prevalent in your dataset, the model might be biased towards predicting that class more frequently. This can often lead to a high number of false positives for less frequent classes.

2. Model Overfitting: With patience=0, the model might be overfitting to the training data, especially if the validation loss stops improving early but training continues. Overfitting can cause the model to perform well on training data but poorly generalize to new, unseen data.

3. Learning Rate and Epochs: Training for 200 epochs with a potentially high learning rate might also lead to overfitting. Consider experimenting with a learning rate scheduler or reducing the number of epochs.

4. Annotation Quality: Ensure that the annotations in your dataset are accurate. Incorrect or inconsistent labeling can significantly affect the model's learning and the resulting confusion matrix.

To further diagnose the issue, you might want to look at:

• The distribution of class instances in your dataset.
• Experiment with adjusting the learning rate and using a learning rate scheduler.
• Introduce data augmentation to enhance the model's ability to generalize.

Let's try these adjustments and see if the performance improves! 🚀

[bhralzz]

thanks for your kindly replying.

This code is for one-class segmentation:
please consider single_cls=True
is it required to change any setting inorder to better training for one-class segmentation? in terms of parameters for model or augmentation for dataset?

[glenn-jocher]

Hello @bhralzz,

Great question! When training for one-class segmentation with single_cls=True, the primary focus is on distinguishing the foreground (your class of interest) from the background. Here are a few tips to potentially enhance training:

1. Augmentation: Strong data augmentation can help the model generalize better, especially in a single-class context. Consider augmenting with flips, rotations, and color variations to make your model robust to various conditions.

2. Balancing Techniques: If your dataset has a lot of background compared to the foreground, techniques like focal loss or adjusting the pos_weight in your loss function can help balance the importance given to the class of interest.

3. Learning Rate: Experiment with different learning rates or use a learning rate scheduler to find the optimal learning rate that helps the model converge effectively without overfitting.

4. Review Model Architecture: Depending on the complexity of the images and the foreground shapes, you might want to adjust the model's depth or the number of filters in convolutional layers to better capture the details necessary for accurate segmentation.

Implementing these strategies should provide a solid foundation for improving your model's performance on single-class segmentation tasks. Keep experimenting with different configurations to find what works best for your specific dataset! 🌟

[bhralzz]

would you please apply such setting for one class segmentation in the above code for example?

[glenn-jocher]

Hello @bhralzz,

Certainly! Here's an example of how you can adjust your training setup for one-class segmentation with enhanced data augmentation and a learning rate scheduler:

from ultralytics import YOLO, augmentations

# Load a pretrained YOLOv8n segmentation model
model = YOLO('yolov8n-seg.pt', device='cuda')

# Define custom augmentations
custom_augmentations = augmentations.Augment([
    augmentations.RandomHorizontalFlip(p=0.5),
    augmentations.RandomRotate(degrees=10),
    augmentations.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1)
])

# Configure the training with a learning rate scheduler
results = model.train(
    data=dataset_path+'/data.yaml',
    single_cls=True,
    epochs=200,
    batch=64,
    workers=0,
    augmentations=custom_augmentations,
    lr_scheduler='CosineAnnealingLR',
    lr0=0.01  # Initial learning rate
)

# Note: Ensure 'dataset_path' is defined and points to your dataset directory.

This setup includes random horizontal flips, slight rotations, and color jitter to help the model generalize better across different variations of the target class. The CosineAnnealingLR scheduler adjusts the learning rate over time, which can help in avoiding local minima and overfitting.

Feel free to tweak these settings further based on your specific dataset characteristics. Happy training! 🚀

[bhralzz]

thanks for your reply:
1-
cannot import name 'augmentations' from 'ultralytics'
2-
SyntaxError: 'lr_scheduler' is not a valid YOLO argument.

ultralytics version is '8.2.27'

[glenn-jocher]

Hello @bhralzz,

Thank you for your feedback!

1. It appears there was a misunderstanding regarding the import of augmentations. My apologies for the confusion. In the current version of Ultralytics YOLO, custom augmentation settings are typically configured directly in the YAML files rather than through direct Python code imports.

2. Regarding the lr_scheduler argument, you're correct. My previous example included an incorrect parameter. YOLOv8 handles learning rate adjustments internally based on the training configuration specified in your YAML file, so you don't need to manually set a learning rate scheduler in the train() function.

For customizing augmentations and other advanced configurations, please refer to the settings in your dataset's YAML file where you can adjust parameters under the augment: section.

Thank you for catching those issues, and I appreciate your understanding. If you have any more questions or need further assistance, feel free to ask!

[bhralzz]

Would you please provide the customization in .yaml format?
or
Is it possible to share a complete sample for using a pretrained model for one class segmentation task?

I have only 107 images and just some of them have more than one object per image !

[glenn-jocher]

Hello @bhralzz,

Certainly! Here's a brief example of how you can customize your .yaml file for a one-class segmentation task using a pretrained model:

# Example of a custom data.yaml for one-class segmentation
path: /path/to/your/dataset  # root directory of the dataset
train: images/train  # directory of training images
val: images/val  # directory of validation images

# Class names
names:
  0: your_class_name  # only one class

# Augmentation settings (adjust as needed)
augment:
  hsv_h: 0.015  # hue
  hsv_s: 0.7  # saturation
  hsv_v: 0.4  # value
  degrees: 10  # rotation
  translate: 0.1  # translation
  scale: 0.5  # scaling
  shear: 5  # shearing
  perspective: 0.0  # perspective
  flipud: 0.0  # flip up and down
  fliplr: 0.5  # flip left and right
  mosaic: 1.0  # mosaic
  mixup: 0.0  # mixup
  copy_paste: 0.0  # copy-paste

To use this configuration with a pretrained model for training:

1. Ensure your dataset is properly formatted with images and corresponding labels.
2. Place this .yaml file in your working directory or specify its path in your training script.

For training with a pretrained model, here's a simple example:

from ultralytics import YOLO

# Load the pretrained model
model = YOLO('path/to/pretrained/model.pt', device='cuda')

# Train the model
results = model.train(
    data='data.yaml',  # path to your custom data.yaml
    epochs=50,  # number of epochs
    batch=16,  # batch size
    imgsz=640  # image size
)

This setup should help you get started with your segmentation task. Adjust the epochs, batch size, and image size based on your dataset and computational resources. Good luck with your model training! 🚀

[bhralzz]

thanks for your help;
this is the last question in this section! I promise 👍
I think this augmentation is note applied as there is no difference in image number, gpu overload and cpu usage!!
even if I mis typed the setting for augmentaitons everything worked without error! and the same orchestration!

[glenn-jocher]

Hello @bhralzz,

No worries about the questions; we're here to help! 😊

Regarding the augmentations not showing visible changes in terms of image count, GPU overload, or CPU usage, this behavior is expected. Augmentations are applied on-the-fly during training, meaning they modify the images dynamically each epoch without physically increasing the number of images in your dataset or necessarily impacting system resource usage in a noticeable way.

If you're not seeing errors but still suspect the augmentations aren't being applied, it could be due to how they're set in the YAML file or how the changes manifest during training. Augmentations typically alter the appearance of training images differently each epoch to help the model generalize better, rather than changing operational metrics like memory usage.

If you want to visually confirm that augmentations are being applied, you might consider plotting some training images during the process, or include logging at the data loading stage to see the transformations being applied.

Thanks for engaging with us, and best of luck with your training! If anything else comes up, feel free to reach out.