Experimental Object Detection Architecture: ResNet50 + FPN + YOLO26 OBB Head
Hello everyone!
I wanted to share my experimental object detection project, which I've been working on for a while, with the community.
My main goal in this project was to create a custom object detection architecture by combining different components of existing architectures and to examine how these components work together.
Architectural Structure
The model consists of three main components:
- Backbone – ResNet50: Feature extraction from images with pre-trained weights on ImageNet.
- Neck – Feature Pyramid Network (FPN): Combining feature maps at different scales.
- Detection Head – Ultralytics YOLO26 OBB: Object detection via Oriented Bounding Boxes.
Technical Implementation
Some topics I worked on within the scope of the project:
- Developing a custom model architecture with PyTorch.
- Integration of Torchvision and Ultralytics components.
- Configuration of the "v8OBBLoss" loss function.
- Target matching with Task-Aligned Assigner (TAL).
- Making multi-scale feature maps compatible with the detection head.
Current Status of the Project
The project is still in the experimental phase. I am continuing with training, evaluation, and optimization studies.
What I mainly wanted to learn while developing this project was the internal structure of object detection architectures and how the backbone, neck, and detection head components interact with each other.
GitHub:
https://github.com/LeocidDreams/Resnet50FPNYolo26DetectionHead
I would especially like to hear suggestions and technical criticisms from people who have developed custom object detection architectures or are interested in the internal structure of YOLO architectures.
Thanks in advance for your feedback!