Researchers Solved a Decade-old Problem in Object Detection
...explained visually.
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Researchers solved a decade-old problem in object detection
From the outside, object detection looks simple.
You give the model an image, and it gives you boxes around objects.
But the process in between is where things get interesting.
In traditional YOLO, the model generates multiple boxes for each object it finds.
A car might get 10 boxes, a person might get 15. This seems wasteful, but having multiple predictions helps the model learn better patterns during training.
That said, inference time still needs one box per object.
So after the model finishes predicting, a separate cleanup step called Non-Maximum Suppression (NMS) filters through all those boxes and keeps only the best ones.
The problem here is that the cleanup step happens outside the neural network, so it’s extra code that runs after the model is done.
What if you could skip that cleanup step entirely?
That’s what end-to-end inference does.
Ultralytics YOLO26 uses this approach to produce final predictions directly in a single pass, with no separate post-processing required.
The visual below depicts how it differs from traditional YOLO:
It uses a dual-head architecture with two modes: a one-to-one head that outputs clean predictions by default, and a one-to-many head for traditional NMS-based processing if needed.
Here’s what the default mode means in practice:
Up to 300 detections per image with one box per object
No filtering or post-processing step required
Faster inference and simpler deployment pipelines
Consistent behavior across different hardware platforms
You can always switch to the one-to-many head with NMS if your application requires it.
Beyond faster inference, the end-to-end design changes how models deploy.
The model’s output is final and predictable. You don’t need to port cleanup logic across platforms or tune thresholds for different scenarios.
This makes integration simpler across edge and low-power devices.
This approach solves the training-versus-deployment tradeoff that has existed in object detection for years.
You can try YOLO26 now on the Ultralytics Platform here →
It’s open-source under AGPL for research, with enterprise licensing for commercial use.
Thanks for reading!
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