What should i do to have a good OD model?[P]

Reddit r/MachineLearning / 4/20/2026

💬 OpinionSignals & Early TrendsIdeas & Deep AnalysisTools & Practical Usage

Key Points

  • The author reports repeated attempts to train object detection models with different datasets, but results remain impractical even when mAP50 is numerically high (around 80%).
  • They are using YOLO11n on a Raspberry Pi 5 (16GB RAM, without an AI accelerator) and are asking how to improve detection quality for real-world use.
  • The post highlights a common problem where evaluation metrics do not translate into usable performance, implying issues like dataset quality, annotation accuracy, training setup, or deployment constraints.
  • The author is not confident in their AI expertise and is seeking guidance on what to check or adjust to reliably achieve a usable OD model.
  • The content is framed as a question to the community rather than a report of a new discovery or product update.

I’m tired of training a lot of models and trying different datasets but still my model is trash and can’t detect clearly it sometimes has mAP50 pf 80% but it is only in numbers not practical, what can i do to have a good model that can be used?

I trained using YOLO11n to use it in RPI5 16GB RAM no AI hat, but still can’t get the results i want, i tried searching and learning what could go wrong but I can’t seem to find the right solution+ i’m not that big of an AI expert so.

submitted by /u/vDHMii
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