Parameter fusion about ultralytics HOT 4 OPEN

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Install

Pip install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

Environments

YOLOv8 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

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.

from ultralytics.

To integrate external module parameters into YOLOv8 for joint training, you can modify the model's architecture to include your custom module. Here’s a brief example using PyTorch:

import torch
import torch.nn as nn
from ultralytics import YOLO

class CustomFilterModule(nn.Module):
    def __init__(self):
        super(CustomFilterModule, self).__init__()
        # Define your filters here, e.g., a convolutional layer
        self.conv = nn.Conv2d(in_channels=3, out_channels=3, kernel_size=3, padding=1)

    def forward(self, x):
        # Apply your filters
        return self.conv(x)

# Load your YOLOv8 model
model = YOLO('yolov8n.pt')

# Add your custom filter module before the YOLOv8 model
class CustomYOLO(nn.Module):
    def __init__(self, yolo_model, filter_module):
        super(CustomYOLO, self).__init__()
        self.filter = filter_module
        self.yolo = yolo_model

    def forward(self, x):
        x = self.filter(x)
        return self.yolo(x)

# Combine them
custom_model = CustomYOLO(model, CustomFilterModule())

# Now custom_model can be trained with both the filter and YOLOv8 parameters being updated

This setup allows the parameters of both the custom filter and the YOLOv8 model to be updated during training. Adjust the CustomFilterModule to include your specific filters and integrate it accordingly.

from ultralytics.

@glenn-jocher I really appreciate your response, and I am currently trying out the solution you proposed. Thank you very much

from ultralytics.

You're welcome! If you have any more questions or need further assistance as you implement the solution, feel free to reach out. Happy coding! 😊

from ultralytics.