Resume and Iterate

Resume an interrupted run

If a run was killed (power cut, disconnect, OOM at hour 8), resume training instead of starting over. Two options:

yolo train resume=True model=runs/detect/forklift_v1/weights/last.pt

from ultralytics import YOLO

model = YOLO("runs/detect/forklift_v1/weights/last.pt")
model.train(resume=True)

Resume picks up the exact optimizer state, learning-rate schedule, and epoch counter. It will not reset the schedule — it continues from where it stopped.

Iterate from your best.pt

Most "next round" experiments aren't resumes — they're fresh runs that warm-start from your last good model. This is plain transfer learning inside your own project:

from ultralytics import YOLO

model = YOLO("runs/detect/forklift_v1/weights/best.pt")    # warm start

model.train(
    data="my_dataset/data.yaml",         # now with extra pallets
    epochs=50,                            # shorter — already pretrained on your data
    imgsz=640,
    name="forklift_v2_more_pallets",
    lr0=0.001,                            # lower LR — fine-tune, not retrain
)

Lower learning rate is the key knob: you want to nudge the model, not reset it. lr0=0.001 (vs the default 0.01) is a sane fine-tune starting point — for anything more systematic, the hyperparameter tuning guide covers sweeps and the genetic optimizer. Augmentation knobs are summarized in the data augmentation glossary.

Naming runs

Let your future self thank you: name every run after what changed.

model.train(name="v2_added_pallets", ...)

A consistent naming scheme means you can compare results.csv across runs trivially. For richer experiment tracking, plug in the Comet or Weights & Biases integrations — both auto-log losses, metrics, and sample predictions per run.

Compare runs side-by-side

Ultralytics YOLO writes a results.csv per run. A small helper to compare:

import pandas as pd
import glob

rows = []
for path in sorted(glob.glob("runs/detect/*/results.csv")):
    df = pd.read_csv(path)
    rows.append({
        "run": path.split("/")[-2],
        "epochs": len(df),
        "best_mAP50_95": df["metrics/mAP50-95(B)"].max(),
        "final_box_loss": df["train/box_loss"].iloc[-1],
    })
print(pd.DataFrame(rows).to_string(index=False))

When to start completely fresh

Sometimes the dataset has changed enough — added a class, relabeled half the data — that warming from your old best.pt actually hurts. The old weights know "this background is forklift territory" with old labels; new labels disagree. In that case start from the pretrained YOLO again:

model = YOLO("yolo26n.pt")       # back to vanilla
model.train(data="my_dataset/data.yaml", epochs=100, name="v3_relabeled")