A Simple YOLO26 Tutorial for Beginners to Experts

Hello there, hope you are doing well. Today we are seeing a simple YOLO26 tutorial for every type of audience. Without further ado, let’s get to it!

What Is YOLO26?

YOLO26 (You Only Look Once 2026) is a a state of the art image machine learning based model that can be trained and implemented using the Ultrlaytics Library.

There are multiple versions of YOLO26, depending on the task at hand.

Classify simply detects and labels objects. Detect detects and forms a bounding box around an object. Segment segments out the object at hand. Track is an extension of detect but tracks an object throughout an image, and Pose shows a wireframe of an individual.

There is also OBB, Oriented Bounding Boxes, that is detect but the bounding box can be rotated and oriented to ones liking.

There are also varying sizes of models in every task, Nano (N), Small (S), Medium (M), Large (L), and eXtra large (X).

Nano and small are used primarily in test batches, medium and large are used in small applications, and extra large is used in industrial standards with large datasets or to provide the best performing model.

Get Started By Making a Dataset

So firstly, we want to gather images for our dataset and create a folder with all the images. Next we upload them to Roboflow.

If you are doing OBB, detect, or track, select Object Detection. If you are doing Classify select Classification. If you are doing Segment select Instance Segmentation. If you are doing Pose, select Keypoint Detection.

Upload your images to the new Roboflow project and annotate them using the toolbox given on the right. Explore the different tools at your disposal like simple bounding box select, polygon tool, and AI helper.

Export and Train!

Now that you are done annotating, go to the health check in the main sidebar and check the dataset health and make necessary adjustments. Afterwards, go to the versions tab and skim through the steps and make a version. Once you get a version, name it, and press export in the top right corner. Select YOLO26 or YOLO11 (both will be supported), and download the zip, unzip and set it ready.

Before we write the base Python code or CLI, first download Ultralytics!

pip install ultralytics

Once that is done check to see successful installation by putting ‘yolo’ in the terminal.

Now decide which model you are training.

If you want to do a pretrained model you want to use “.pt”, and if you want to start from scratch use “.yaml”.

Decide which size model you are going to do outlined in the introduction of this article.

And if you are doing something other than detect or track you need to have an extension to the base name:

Segment = ‘-seg’

OBB = ‘-obb’

Pose = ‘-pose’

Classify = ‘-cls’

If you are doing detect or track you do not have an extension to the base name.

Now put them together, your model should be named:

yolo26(size)(extension if you have)(.yaml/.pt)

It should look like this:

• yolo26n-obb.pt
• yolo26x-seg.yaml
• yolo26m-cls.pt

Training Python Code

from ultralytics import YOLO
model = YOLO('INSERT_MODEL_NAME')
# Train the model
results = model.train(data='PATH_TO_DATASET', epochs=CHOOSE_AND_EXPERIMENT, imgsz=640)

Choose and experiment with the amount of epochs.

You have your model inside the dataset under runs directory. You will have to go searching for it but it will be under whatever the task you are doing and under train and a number followed by that.

If you get “dataset not found” click here for a solution.

Now you can do multiple things:

• If you did track put the following code:

from ultralytics import YOLO
model = YOLO('PATH_TO_MODEL') 
# Perform tracking with the model
results = model.track('INSERT YOUTUBE LINK', show=True)

~Tracking also works on segmentation and pose, so now that you got it mastered in detect, go ahead and do the other two if you want ;)~

• You could validate your model which helps tune it slightly more for a better boost:

from ultralytics import YOLO
model = YOLO("PATH TO MODEL")
metrics = model.val()  # no arguments needed
# You can take a look at certain stats like the following
metrics.box.map  # map50-95
metrics.box.map50  # map50

• It can also make predictions on new images

from ultralytics import YOLO
# Load a model
model = YOLO("PATH TO MODEL")
# Run batched inference on a list of images
results = model(["im1.jpg", "im2.jpg"])  # return a list of Results objects
# Process results list
for result in results:
    boxes = result.boxes  # Boxes object for bounding box outputs
    masks = result.masks  # Masks object for segmentation masks outputs
    keypoints = result.keypoints  # Keypoints object for pose outputs
    probs = result.probs  # Probs object for classification outputs
    obb = result.obb  # Oriented boxes object for OBB outputs
    result.show()  # display to screen
    result.save(filename="result.jpg")  # save to disk

• You can export the model into a different format like .onnx

from ultralytics import YOLO
# Load a model
model = YOLO("PATH TO MODEL")
# Export the model
model.export(format="onnx")

• And you can benchmark your model:

from ultralytics.utils.benchmarks import benchmark
# Benchmark on GPU
benchmark(model='PATH_TO_MODEL', data='DATASET PATH', imgsz=640, half=False)

Yeah and that is about it, congrats on your new model, and the research and missions you might do with YOLO, the possibilities are endless.

Credits to the Ultralytics Docs (https://docs.ultralytics.com/) most of the code came from them, and they can help out with their awesome YouTube videos explaining everything YOLO, and troubleshooting tips.

And that is about it. If you want more articles like this, make sure to follow. Comment your thoughts if you play-tested it or really wanted to say something about the YOLO-verse or my article. Make sure to clap to increase it’s appearance in search results. I have an email subscription to get an email every week on something really intriguing the ML/AI/CS field and with sprinkles of other stuff I have been thinking about. That way you don’t forget.

And that’s about it, I will catch you on the flip flop, I’m out, see yah!