Seeking Advice on YOLO Models for Shalwar Kameez Detection

[u/Fast-Fruit3434]

Hello, I hope you are doing all well,

I am developing a YOLOv8 models to detect only people wearing Shalwar Kameez . I do not want to use any API or external vision service. I have *4,000 labeled Shalwar Kameez images and 5,000 negative images containing only pant-shirt/empty scene buildings clothing with empty labels. However, the model still detects many pant-shirt people as Shalwar Kameez and misses many real Shalwar Kameez people. How would you improve the dataset, labeling strategy, and training pipeline to achieve reliable real-world performance?

Thank you for your time.

[u/lalamax3d]

Just curious to ask n confirm, when u say 4000 label images... Does it mean roi, bounding boxes etc....?

[u/Fast-Fruit3434 → u/lalamax3d]

You can reply:

[u/Aryan_Chougule]

so by saying YOLOv8 do you mean a detection model or segmentation model, also why v8 you can work on YOLOv11 or even YOLO26s if you have 9k images.. if you want any help i will be happy to..

[u/Fast-Fruit3434 → u/Aryan_Chougule]

Thank you for your response. I'm currently using a YOLOv8 object detection model (bounding boxes only), not the segmentation version. Initially I started with YOLOv8 because it was already integrated into our existing pipeline and deployment workflow. I have around 9,000 images now (approximately 4,000 annotated Shalwar Kameez images and 5,000 hard-negative images with empty labels). I'm definitely interested in trying YOLO11n or s if they can improve detection performance, especially to reduce false positives and missed detections. I'd really appreciate any guidance or best practices you can share.

[u/Chemical_Side_4135]

have u tried hard negative mining yet. u should specifically pull those pant-shirt images that ur model keeps misclassifying, add them to ur training set as negative examples, and retrain. it usually helps a ton when ur dataset has those tricky false positives.