What benefits does yolov26 offer over yolov11?

Discussion
1

I have tested both yolov26 and yolov11 both using small models and accuracy is slightly worse on yolov26 than it is on yolov26 (I did a “proof of concept” with my training).

I was wondering is this the reason due to the trade off favoring slightly higher speeds over accuracy?

2

Did you use the same parameters and dataset for training? It’s not necessarily true that the exact same training parameters for YOLO11 would be optimal for YOLO26, and optimization of those parameters could depend on the dataset.

Will try to help answer your questions as much as possible, but if you want to ask the authoritative source about everything YOLO26, I recommend checking out the upcoming live session. I just posted about it here:

3

Hi mate, I used the exact same paramaters, for example:

dataset_custom.yaml

train: images/train
val: images/val

nc: 1

names: ["mc"]

train.py

from ultralytics import YOLO

# model = YOLO("yolo11s.pt")
model = YOLO("yolo26s.pt")
model.train(data = "dataset_custom.yaml", imgsz = 640,
batch = 8, epochs = 100, workers = 0, device = "cpu")

I am pretty sure this is optimal for both yolo11s and yolo26s

I had 11 images for training and 5 images for validation, its just for a proof of concept rather than an offical product I am trying to build, hence the lack of image training.

4

This would definitely be something you could try for a proof of concept, but would not use this for a basis of comparison WRT performance. If you wanted to see if it was at all possible for a model to learn the class(es) you wished to train, that’s what you could use this to prove, but you’ll need significantly more data for anything else.

If you want to check performance between YOLO11 and YOLO26 on datasets other than the COCO dataset, you could download a pre-annotated dataset and train on that instead. If you can find one that has objects similar to your dataset, you might be able to consider it as a proxy estimate of performance on your custom data, but nothing is certain without testing. I generally test with the VisDrone dataset, because it’s smaller (relatively), but has a high number of quality annotations.

One thing to keep in mind. The larger the dataset, the longer your training will be, and even moreso if you’re exclusively training on CPU. A good reference point, would be the docs Guide for Tips and Tricks for Model Training. You will definitely need more data, and as someone who attempted to train 200k images using a laptop CPU, I can tell you it’s not worth it (took 3 days to get up to 4 epochs).