Hello everyone, I am trying to implement an object detection model on an MCU with a NPU using several YOLO models. Right now, I am trying to implement it with YOLOv11n, however, even though i apply int8 quantization, I cannot achieve the size I want to achieve. My MCU can only accept int8, therefor…

On MCUs/NPUs, pruning only helps if it’s structured pruning (removing whole channels/filters so the network is physically smaller). Unstructured pruning (just zeroing individual weights) usually won’t shrink your exported INT8 model or speed it up unless your toolchain has sparse-kernel support. The…

You can read this [image] YOLO modeling weight pruning Guide Resources Really cool article shared on the Ultralytics Subreddit from @Toxite for YOLO pruning

Thank you for your reply. I am aiming a model size below 2 MB. Right now, my exported YOLOv11 model is 2.6 MB for both full_quant model and int8 model. We are using eIQ Neutron SDK for our model implementation on our board. I have also tried YOLOv26n but I did not achieve different results on tha…

If you want to train a smaller model, you can change this to [0.5, 0.25, 512] and train a new model using the YAML: ultralytics/cfg/models/26/yolo26.yaml 4cb6d659b # Ultralytics YOLO26 object detection model with P3/8 - P5/32 outputs # Model docs: htt…

This decreased the size incredibly well. But I have a question, what are the possible values for max_channels. Because I have a rather small dataset to train my model for a specific case and this causes the mAP50-95 value to decrase substantially.

YOLOv11n Pruning

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Toxite February 15, 2026, 11:30am 3

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YOLOv11n Pruning

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