Why Object Detection Fails on Small Objects | Mean Average Precision | SAHI tiled Inference | AI 🚀
Mean Average Precision (mAP) is one of the most important metrics for evaluating object detection models, but understanding it in isolation isn’t enough. Real-world performance depends on multiple factors, especially when dealing with small object detection. In this video, we break down how mAP works and explore why small objects are inherently harder to detect. We then discuss how techniques such as SAHI (Sliced Aided Hyper Inference) improve detection performance for small objects. We also cover the importance of dataset quality, annotations, and data augmentation, as well as the trade-offs involved in deploying models in real-world environments.
Chapters:
00:00 - Introduction to mean average precision (mAP)
02:09 - Why small objects are hard to detect
04:00 - Improving detection with SAHI
06:10 - Importance of dataset quality and annotations
07:00 - Role of data augmentation
08:46 - Deployment constraints and real-world tradeoffs
09:04 - Conclusion and key takeaways
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