Train your first YOLO model·Train·Lesson 6/10

Lessonbeginner

Train Your First Model

One Python call, sensible defaults, and a checkpoint at the end.

Now we train. Thanks to transfer learning from the pretrained weights, there's a real chance your first run beats the pretrained baseline by a wide margin — your dataset is narrower, the classes are familiar, and the Ultralytics YOLO default configuration is well-tuned. Don't overthink the config the first time. Run, look at the curves, then iterate.

Outcome

Train a model on your dataset, end-to-end, and save the best.pt checkpoint.

Fast Track

If you already know your way around, here's the short version.

model = YOLO('yolo26n.pt').
model.train(data='my_dataset/data.yaml', epochs=100, imgsz=640).
Watch the loss curves in the console.
Open runs/detect/train/. Your weights are in weights/best.pt.

Hands-on

A first training run

Ultralytics Platform cloud training dialog

from ultralytics import YOLO

model = YOLO("yolo26n.pt")     # start from a pretrained checkpoint

results = model.train(
    data="my_dataset/data.yaml",
    epochs=100,
    imgsz=640,
    batch=16,
    name="forklift_v1",
)

# Path to the best checkpoint
print(results.save_dir)

That writes a directory like runs/detect/forklift_v1/ with:

weights/best.pt — best validation mAP across epochs.
weights/last.pt — last epoch.
results.csv — per-epoch metrics.
A bunch of plots — confusion matrix, PR curves, F1 curves, sample predictions.

Open the directory in a file explorer and scroll the plots before running anything else.

The arguments that matter

Defaults are sane. Three knobs you'll touch first:

Arg	Default	When to change
`epochs`	100	Lower for tiny datasets to avoid overfitting; higher (200–300) for large ones
`imgsz`	640	Increase to 1024 / 1280 for small objects
`batch`	16	Batch size — match GPU memory; -1 picks the largest that fits

model.train(data="my_dataset/data.yaml", epochs=200, imgsz=1024, batch=-1)

Start from a pretrained checkpoint

Always pass yolo26n.pt (or another size) as the model — never train from scratch unless you're researching the backbone. Pretrained weights cut training time by 5–10× and reach much higher final accuracy on most datasets.

Reading the live log

The console log per epoch looks like this:

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       1/100     2.43G      1.234      1.456      0.987         42        640: 100% ...

                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)
                   all         50        102      0.612      0.534      0.561      0.342

What to watch:

box_loss, cls_loss, dfl_loss trend down — these are the components of YOLO's loss function and should fall steadily for the first many epochs. A loss that plateaus immediately means data or config trouble.
P (precision) / R (recall) trend up — and balance. Precision >> recall means the model is too cautious; recall >> precision means it's hallucinating.
mAP50 and mAP50-95 trend up — the actual quality numbers; the performance metrics guide explains them in depth.

If anything looks off in the first 5 epochs, kill the run with Ctrl-C and investigate before burning hours.

Multi-GPU

model.train(data=..., device=[0, 1, 2, 3])    # use 4 GPUs

Ultralytics YOLO uses DDP automatically and trains in mixed precision by default. Effective batch size = batch × number of GPUs. When you're ready to push past sensible defaults, the hyperparameter tuning guide covers learning rate sweeps, augmentation knobs, and the genetic optimizer.

CLI version

The same training, on the command line:

yolo detect train data=my_dataset/data.yaml model=yolo26n.pt epochs=100 imgsz=640 name=forklift_v1

CLI and Python are equivalent — pick whichever fits your workflow.

Try It

Run model.train(data=…, epochs=100). While it trains, scan the console output every few epochs. After it finishes, open runs/detect/.../results.png and look at the curves. Note where mAP plateaus.

Commit

git add -A && git commit -m "feat(model): trained first forklift detector"

Done When

You've finished the lesson when all of these are true.

Training completed without crashing.
runs/detect/<name>/weights/best.pt exists.
You've looked at the loss/mAP curves and have an opinion on whether they look healthy.

Show solution

from ultralytics import YOLO

model = YOLO("yolo26n.pt")
model.train(
    data="my_dataset/data.yaml",
    epochs=100,
    imgsz=640,
    batch=-1,           # auto-batch
    name="forklift_v1",
    patience=20,        # early-stop if mAP doesn't improve for 20 epochs
)

What's next

We have a model. Next: validate it honestly and figure out what to fix.

Get Started