when segmentation can get us better results for a classification task .also how to pick the right model if i have little time like ccn or vits? And the best advanced tricks for classification tasks that really improve performance usually.iknow there is no magical step that improve the performance and it’s really depends on nature of data and model type etc ,but what tricks usually make the performance better
When can segmentation give better results for classification?
When the object of interest is spatially localized and variable in size or position (e.g., tumors in medical images, defects in manufacturing).
When background clutter hurts classification (e.g., wildlife in messy backgrounds).
When segmentation can act as a form of attention — making classification focus on the right regions.
When classes are defined by shape or boundaries (e.g., certain diseases on X-rays, road signs).
Tip: Weakly supervised segmentation + classification can sometimes outperform pure classification, especially in medical/industrial domains.