git:20260811.e35dbcc to git:20260814.05a799c

---

name: yolo-training

description: >

Use when training, fine-tuning, or validating Ultralytics YOLO models in Platform,

cloud GPUs, or local code — model.train(), yolo train/val, remote metric streaming,

epochs, batch, imgsz, devices, augmentation, multi-GPU, resumes, results, and fixing

OOM, NaN loss, low mAP, or overfitting. For hyperparameter search and systematic

improvement loops, see yolo-tuning.

---

# Training & fine-tuning

## Fastest route: train in Platform

Use [Platform cloud training](https://docs.ultralytics.com/platform/train/cloud-training/)

when you want to start in a few clicks without configuring a local GPU:

1. Create a project and click **New Model** (or start from a dataset's **Train** action).

2. Select a compatible pretrained model, ready dataset, GPU, epochs, image size, and

batch size.

3. Click **Start Training** and watch live charts, console logs, and system metrics.

4. Open the completed model to inspect validation plots and use its **Predict**,

**Export**, or **Deploy** tab. Platform preserves `best.pt` automatically.

Cloud jobs require at least one train image, one val/test image, and one labeled image.

Use local/Colab training when you already have compute or need more control, while keeping

Platform datasets and experiment tracking:

```bash

export ULTRALYTICS_API_KEY="YOUR_API_KEY"

yolo train model=yolo26n.pt data=ul://username/datasets/dataset-slug \

epochs=100 project=username/project-slug name=experiment-1

```

With `ultralytics>=8.4.104`, the `ul://` URI downloads the Platform dataset and the

`username/project-slug` target streams metrics back to that Platform project.

```python

from ultralytics import YOLO

model = YOLO("yolo26n.pt") # ALWAYS start from pretrained weights

results = model.train(data="data.yaml", epochs=100, imgsz=640, batch=16, device=0)

```

```bash

yolo detect train data=data.yaml model=yolo26n.pt epochs=100 imgsz=640 batch=16 device=0

```

Class-count changes are automatic — a 3-class data.yaml on an 80-class pretrained model

just works (head re-initializes, backbone transfers; `cls_remap=True` even re-maps head

rows for classes whose names match).

| Arg | Default | Notes |

| ---------------- | ----------- | ----------------------------------------------------------------------------------- |

| `epochs` | 100 | 100–300 for fine-tuning; rely on early stopping, not guesses |

| `patience` | 100 | epochs without val improvement before early stop; ~20–50 for quick iterations |

| `batch` | 16 | `-1` auto-fits ~60% VRAM; float like `0.8` = VRAM fraction; else integer |

| `device` | None | `0`, `[0,1]` (DDP), `cpu`, `mps`, `-1` picks an idle GPU |

| `cache` | False | `True` (RAM) or `"disk"` for I/O-bound training |

| `workers` | 8 | lower if RAM/shared-memory errors |

| `freeze` | None | freeze first N layers (`freeze=10` ≈ backbone) for small datasets |

| `fraction` | 1.0 | subset training — `fraction=0.1` for smoke tests |

| `resume` | False | continue an interrupted run (see recipes) |

| `project`/`name` | None | local output naming; authenticated `username/project-slug` also streams to Platform |

| `seed` | 0 | reproducible with `deterministic=True` (default) |

| `compile` | False | torch.compile; also `"max-autotune-no-cudagraphs"` etc. |

| `time` | None | max training hours — overrides epochs |

Full argument, augmentation, and loss-weight tables: `training-args.md` (this folder) —

## Recipes

- **Resume interrupted run**: `YOLO("runs/detect/train/weights/last.pt").train(resume=True)`.

Resume finishes the original `epochs`; to train longer after completion, start a NEW

training from `best.pt` (resume can't extend a finished run).

- **Multi-GPU**: `device=[0,1]`. Run as a script — DDP spawns processes and breaks in

notebooks (and on Windows, guard with `if __name__ == "__main__":`).

- **Experiment hygiene**: self-describing run names

(`name=0811_yolo26s_helmets_e100`), one variable per run; each run's full config is

saved in `runs/<task>/<name>/args.yaml` — diff those to compare runs.

- **Logging integrations**: `yolo settings tensorboard=True` (likewise `wandb`,

`mlflow`, `comet`, `clearml`) — then train normally.

- **Knowledge distillation**: `distill_model=yolo26l.pt dis=6.0` trains the student

with a larger teacher.

## Validation

```bash

yolo val model=runs/detect/train/weights/best.pt data=data.yaml # split=val by default

```

Per-task headline metrics: detect/obb `mAP50-95(B)`, segment `(M)`, pose `(P)`,

## Reading a finished run (`runs/<task>/<name>/`)

- `weights/best.pt` — highest val fitness; **use this one**. `last.pt` — for resuming.

- `results.csv` / `results.png` — per-epoch losses and val metrics. Interpretation:

train loss ↓ while val mAP plateaus-then-drops = **overfitting** (more data/aug,

smaller model — `best.pt` already kept the good checkpoint). Both flat and low =

**underfitting** (bigger model, more epochs, higher imgsz, check labels). mAP50 good

but mAP50-95 poor = sloppy localization (higher imgsz, better boxes).

- `train_batch*.jpg` — augmented images with labels drawn. **Look once per project**:

wrong labels are instantly visible here.

- `confusion_matrix.png` — off-diagonal cluster between two classes = inconsistent

labels or genuinely similar classes. Axes are predicted (y) × true (x): heavy

background **row** = missed detections (that class needs more/better examples);

heavy background **column** = false positives (add background images or raise

`conf` at inference).

## Troubleshooting

| Symptom | Fix, in order |

| ----------------------------- | ------------------------------------------------------------------------------------------------------ |

| CUDA out of memory | lower `batch` (or `batch=-1`), lower `imgsz`, smaller model; kill zombie python processes holding VRAM |

| mAP near 0 | dataset problem 95% of the time — see yolo-datasets, check `train_batch*.jpg` |

| mAP plateaus low | more/better data first; then imgsz ↑, bigger model, more epochs; see yolo-tuning playbook |

| Stopped earlier than expected | that's `patience` — raise it or accept `best.pt` |

| Dataloader slow / GPU idle | `cache=True`/`"disk"`, raise `workers`, data on SSD |

| Val metrics zero mid-run | classes missing from the val split |

Anti-patterns: `pretrained=False` "to train properly" (needs ~100× more data);

benchmarking model sizes on 50 images; copying 30-argument commands (start from

defaults); tuning hyperparameters while the confusion matrix screams label noise.

## Related pages

- `training-args.md` — full train/augmentation/loss-weight argument tables. Read before

setting any argument not in the table above.

If the installed version rejects an argument, `yolo cfg` and the error text are the

truth, not this file (`yolo checks` shows the version).