Hello author, I am currently using v8 for industrial defect segmentation, but the effect is not very good. What do I need to do to improve the effect? about ultralytics

[glenn-jocher]

Hello! Thank you for reaching out and detailing your experience with YOLOv8 for industrial defect segmentation.

Improving the segmentation performance can depend on several factors. Here are a few suggestions you might consider:

1. Data Quality and Quantity: Ensure that your dataset is sufficiently large and accurately annotated. More diverse and representative data can significantly enhance model performance.

2. Model Tuning: Experiment with different hyperparameters, such as learning rate, batch size, or augmentation strategies. Adjusting these can help the model learn better from your specific dataset.

3. Advanced Architectures: If you haven't already, try using a more complex YOLOv8 model variant that might capture finer details better than the base models.

4. Pre-training: Utilize transfer learning by pre-training the model on a similar task with a large dataset before fine-tuning it on your specific defect segmentation task.

5. Post-processing: Sometimes, tweaking the post-processing steps like threshold adjustments for segmentation masks can refine the results.

If you continue to face challenges, consider providing more details such as the types of defects, examples of current outputs, and specific issues with the segmentation results. This additional context can help in giving more targeted advice.

Best of luck with your segmentation tasks! 🚀

[yxl23]

What exactly do I need to do? Can you give me an example? My samples are only over 10 and they are all small targets. What do I need to do

[glenn-jocher]

@yxl23 hello! Working with a small dataset of only 10 samples, especially with small targets, can indeed be challenging. Here are a few steps you can take to improve your model's performance:

1. Data Augmentation: Apply data augmentation techniques such as rotation, scaling, and translation to artificially increase the size of your dataset and help the model generalize better.

2. Transfer Learning: Start with a model pre-trained on a larger dataset and fine-tune it on your small dataset. This can leverage learned features that might be beneficial for your specific task.

3. Focus on Small Objects: Adjust the anchor sizes if possible, or modify the network to better detect smaller objects. This might involve changing the stride or using higher resolution inputs.

4. Experiment with Hyperparameters: Since your dataset is small, be cautious with the learning rate and regularization to avoid overfitting.

5. Ensemble Methods: If feasible, training multiple models and using ensemble techniques might improve detection performance by leveraging the strengths of different models.

Implementing these strategies should provide a solid foundation for enhancing your model's ability to detect small targets in limited datasets. Good luck! 🚀

[yxl23]

Thank you, but how do I modify the anchor to fit the segmentation object of my small target?

[glenn-jocher]

Hello @yxl23! To modify the anchors for small target segmentation, you can follow these steps:

1. Analyze Your Dataset: Calculate the average dimensions of your small targets. This data will guide the resizing of your anchors.

2. Modify Configuration: Adjust the anchor sizes in your model's configuration file to better match the dimensions you've calculated. This helps the model to better predict the scale of your small targets.

3. Retrain the Model: After adjusting the anchors, retrain your model with the new configuration to let it adapt to these changes.

Adjusting the anchors to better fit the dimensions of your targets can significantly improve detection performance. If you need further assistance, feel free to ask. Good luck! 🚀

[yxl23]

Can you provide me with further guidance, such as how to calculate the average size of small targets, how to modify configurations, which file to modify, and specific modification steps

[glenn-jocher]

Hello @yxl23! I'd be happy to provide further guidance on how to calculate the average size of your small targets and how to modify the configurations to fit them better. Let's break it down step-by-step:

Step 1: Calculate the Average Size of Small Targets

First, you need to calculate the average width and height of your small targets. Here's a simple Python script to help you do that:

import os
import cv2

# Path to your dataset annotations (assuming YOLO format)
annotations_path = 'path/to/annotations/'

# Initialize variables to calculate average width and height
total_width = 0
total_height = 0
num_boxes = 0

# Loop through all annotation files
for annotation_file in os.listdir(annotations_path):
    with open(os.path.join(annotations_path, annotation_file), 'r') as file:
        for line in file:
            _, x_center, y_center, width, height = map(float, line.strip().split())
            total_width += width
            total_height += height
            num_boxes += 1

# Calculate average width and height
average_width = total_width / num_boxes
average_height = total_height / num_boxes

print(f'Average Width: {average_width}, Average Height: {average_height}')

Step 2: Modify the Configuration File

Once you have the average dimensions, you can modify the anchor sizes in your YOLOv8 configuration file. Here's how you can do it:

1. Locate the Configuration File: Typically, this file is named something like yolov8.yaml or similar, depending on your model variant.

2. Edit the Anchors: Open the configuration file and find the anchors section. Replace the existing anchor sizes with the new values based on your average dimensions.

Example configuration snippet:

anchors:
  - [10, 13, 16, 30, 33, 23]  # Original anchors
  - [30, 61, 62, 45, 59, 119]
  - [116, 90, 156, 198, 373, 326]

# Replace with your calculated average sizes
anchors:
  - [average_width, average_height, ...]  # Add more anchors as needed

Step 3: Retrain the Model

After modifying the configuration file, retrain your model to adapt to the new anchor sizes:

yolo detect train data=coco128.yaml model=yolov8.yaml epochs=100 imgsz=640

Additional Tips

• Data Augmentation: Since you have a small dataset, consider using data augmentation techniques to artificially increase the size of your dataset.
• Hyperparameter Tuning: Experiment with different learning rates and batch sizes to find the optimal settings for your specific task.

Feel free to reach out if you have any more questions or need further assistance. Happy training! 🚀

[yxl23]

The author used yolov8-seg-p6.yaml, but it seems that there are no anchors in it:

Ultralytics YOLO 🚀, AGPL-3.0 license

YOLOv8-seg-p6 instance segmentation model. For Usage examples see https://docs.ultralytics.com/tasks/segment

Parameters

nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n-seg-p6.yaml' will call yolov8-seg-p6.yaml with scale 'n'

[depth, width, max_channels]

n: [0.33, 0.25, 1024]
s: [0.33, 0.50, 1024]
m: [0.67, 0.75, 768]
l: [1.00, 1.00, 512]
x: [1.00, 1.25, 512]

YOLOv8.0x6 backbone

backbone:

[from, repeats, module, args]

• [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
• [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
• [-1, 3, C2f, [128, True]]
• [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
• [-1, 6, C2f, [256, True]]
• [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
• [-1, 6, C2f, [512, True]]
• [-1, 1, Conv, [768, 3, 2]] # 7-P5/32
• [-1, 3, C2f, [768, True]]
• [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64
• [-1, 3, C2f, [1024, True]]
• [-1, 1, SPPF, [1024, 5]] # 11

YOLOv8.0x6 head

head:

• [-1, 1, nn.Upsample, [None, 2, "nearest"]]

• [[-1, 8], 1, Concat, [1]] # cat backbone P5

• [-1, 3, C2, [768, False]] # 14

• [-1, 1, nn.Upsample, [None, 2, "nearest"]]

• [[-1, 6], 1, Concat, [1]] # cat backbone P4

• [-1, 3, C2, [512, False]] # 17

• [-1, 1, nn.Upsample, [None, 2, "nearest"]]

• [[-1, 4], 1, Concat, [1]] # cat backbone P3

• [-1, 3, C2, [256, False]] # 20 (P3/8-small)

• [-1, 1, Conv, [256, 3, 2]]

• [[-1, 17], 1, Concat, [1]] # cat head P4

• [-1, 3, C2, [512, False]] # 23 (P4/16-medium)

• [-1, 1, Conv, [512, 3, 2]]

• [[-1, 14], 1, Concat, [1]] # cat head P5

• [-1, 3, C2, [768, False]] # 26 (P5/32-large)

• [-1, 1, Conv, [768, 3, 2]]

• [[-1, 11], 1, Concat, [1]] # cat head P6

• [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge)

• [[20, 23, 26, 29], 1, Segment, [nc, 32, 256]] # Pose(P3, P4, P5, P6)

[yxl23]

How should I change the numerical value obtained after modifying the code
Average Width: 25.203863777246706, Average Height: 24.297516845105136

[glenn-jocher]

Hello @yxl23! Thanks for providing the average dimensions of your small targets. Since the YOLOv8-seg-p6 model configuration doesn't explicitly include anchor settings, you don't need to modify anchors directly. Instead, you can focus on adjusting the input image size and other hyperparameters to better suit your small targets.

Step-by-Step Guide

1. Adjust Input Image Size: Given the small size of your targets, increasing the input image resolution can help the model detect smaller objects more accurately. You can set a higher resolution in your training command.

2. Data Augmentation: Enhance your dataset with augmentation techniques to improve model robustness.

3. Hyperparameter Tuning: Fine-tune hyperparameters like learning rate, batch size, and epochs to optimize performance.

Example Configuration

Here's how you can adjust the input image size and other settings in your training command:

yolo segment train data=path/to/your/data.yaml model=yolov8-seg-p6.yaml epochs=100 imgsz=1024 batch=4

Data Augmentation Example

You can apply data augmentation directly in your training script. Here's an example using the Ultralytics library:

from ultralytics import YOLO

# Load your model
model = YOLO('yolov8-seg-p6.yaml')

# Define your data augmentation pipeline
augmentations = {
    'flipud': 0.5,  # Flip images upside down with 50% probability
    'fliplr': 0.5,  # Flip images left to right with 50% probability
    'scale': 0.5,   # Scale images by a factor of 0.5
    'rotate': 10,   # Rotate images by 10 degrees
}

# Train your model with the augmentations
model.train(data='path/to/your/data.yaml', epochs=100, imgsz=1024, batch=4, augmentations=augmentations)

Hyperparameter Tuning

Experiment with different values for learning rate and batch size. Here's an example:

yolo segment train data=path/to/your/data.yaml model=yolov8-seg-p6.yaml epochs=100 imgsz=1024 batch=4 lr0=0.001

By increasing the image size and applying data augmentation, you can help the model better detect small targets. Fine-tuning hyperparameters will further optimize the model's performance.

Feel free to reach out if you have any more questions or need further assistance. Happy training! 🚀