The article discusses the author's journey in creating a custom object detection training pipeline from scratch. By examining well-known frameworks like Ultralytics YOLOx and D-FINE, the author gained insights into modern techniques and successfully implemented the D-FINE model within their pipeline. Critical areas involved dataset preparation, application of image augmentations, and efficient data handling. Special emphasis was placed on the importance of maintaining alignment between augmentations and bounding box transformations. The author’s approach to managing datasets and applying custom augmentations set the groundwork for a flexible and effective training framework.
When I set out to write the entire object detection training pipeline from scratch, I focused on understanding each step to ensure maximum customization.
Leveraging existing models like YOLOx and D-FINE allowed me to implement a state-of-the-art real-time object detection model tailored to my specific tasks.
For object detection, the dataset processing is crucial; I used COCO and YOLO formats and designed my dataset class to effectively manage image and annotation loading.
Customization in data splitting and image augmentations, especially with the Albumentations library for bounding boxes, significantly enhanced my object detection training pipeline.
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