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

Reddit r/MachineLearning 04/20/26, 11:52 AM Tools

object-detection yolo training deployment raspberry-pi computer-vision model-optimization

Summary

A user is seeking advice on improving their object detection model trained with YOLO11n for deployment on a Raspberry Pi 5, struggling with the gap between theoretical mAP50 metrics and practical detection performance.

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.

Original Article

Similar Articles

/yolo

Reddit r/LocalLLaMA

Article concerning YOLO, the widely used real-time object detection model family.

(Rant ;)) Make your benchmarks realistic

Reddit r/LocalLLaMA

A community rant urging realistic AI model benchmarks that account for context size, multimodal features, hardware specifics, and parallel processing, rather than just raw speed.

@tenderizzation: it’s literally off the scale! welcome back yolov3

X AI KOLs Following

A social media post expresses excitement about the return or renewed relevance of the YOLOv3 object detection model.

@AdinaYakup: MOSS-VL Vision model from @Open_MOSS Model: https://huggingface.co/collections/OpenMOSS-Team/moss-vl… Demo: https://hug…

X AI KOLs Following

Open_MOSS released MOSS-VL, an 11B Apache 2.0 vision-language model using cross-attention and XRoPE that outperforms Qwen3-VL-8B by 8.3 points on VSI-bench.

Optimizing Transformer model size & inference beyond FP16 + ONNX (pruning/graph opt didn’t help much) [P]

Reddit r/MachineLearning

Author shares experience hitting diminishing returns with FP16 + ONNX + pruning on 162 MB transformer, seeks advice on next best steps among quantization, distillation, low-rank factorization, or hardware-specific tricks.