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How to Create a Custom Ultralytics Trainer in 3 simple steps
In the ever-evolving field of computer vision, off-the-shelf solutions often fall short when faced with unique datasets. One such challenge arises when working with slice-labeled datasets: these are images labeled as distinct slices or segments, where resizing operations can distort the data and degrade the model’s performance.
Ultralytics, the powerhouse behind the YOLO series, offers a modular framework that empowers developers to build customized training pipelines. This article walks you through creating a tailored trainer for slice-labeled datasets, ensuring no resolution is lost during preprocessing.
Why Slice-Labeled Datasets Need Special Attention
Imagine you’re training a model to detect microscopic cracks on industrial surfaces or localize structures in high-resolution medical images. These datasets often have labels tied to very specific regions of interest. Resizing such images can smear critical details, leading to poor detection results.
By removing resizing from the pipeline and instead focusing on tailored augmentations and dataset handling, you can maintain the integrity of these images while training a model designed to excel under these constraints.
