---
comments: true
description: Official documentation for YOLOv8 by Ultralytics. Learn how to train, validate, predict and export models in various formats. Including detailed performance stats.
keywords: YOLOv8, Ultralytics, object detection, pretrained models, training, validation, prediction, export models, COCO, ImageNet, PyTorch, ONNX, CoreML
---

# Object Detection

<img width="1024" src="https://user-images.githubusercontent.com/26833433/243418624-5785cb93-74c9-4541-9179-d5c6782d491a.png" alt="Object detection examples">

Object detection is a task that involves identifying the location and class of objects in an image or video stream.

The output of an object detector is a set of bounding boxes that enclose the objects in the image, along with class labels and confidence scores for each box. Object detection is a good choice when you need to identify objects of interest in a scene, but don't need to know exactly where the object is or its exact shape.

<p align="center">
  <br>
  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/5ku7npMrW40?si=6HQO1dDXunV8gekh"
    title="YouTube video player" frameborder="0"
    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
    allowfullscreen>
  </iframe>
  <br>
  <strong>Watch:</strong> Object Detection with Pre-trained Ultralytics YOLOv8 Model.
</p>

!!! Tip "Tip"

    YOLOv8 Detect models are the default YOLOv8 models, i.e. `yolov8n.pt` and are pretrained on [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml).

## [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/v8)

YOLOv8 pretrained Detect models are shown here. Detect, Segment and Pose models are pretrained on the [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml) dataset, while Classify models are pretrained on the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/ImageNet.yaml) dataset.

[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.

| Model                                                                                | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
|--------------------------------------------------------------------------------------|-----------------------|----------------------|--------------------------------|-------------------------------------|--------------------|-------------------|
| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt) | 640                   | 37.3                 | 80.4                           | 0.99                                | 3.2                | 8.7               |
| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s.pt) | 640                   | 44.9                 | 128.4                          | 1.20                                | 11.2               | 28.6              |
| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m.pt) | 640                   | 50.2                 | 234.7                          | 1.83                                | 25.9               | 78.9              |
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l.pt) | 640                   | 52.9                 | 375.2                          | 2.39                                | 43.7               | 165.2             |
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x.pt) | 640                   | 53.9                 | 479.1                          | 3.53                                | 68.2               | 257.8             |

- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](https://cocodataset.org) dataset. <br>Reproduce by `yolo val detect data=coco.yaml device=0`
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val detect data=coco8.yaml batch=1 device=0|cpu`

## Train

Train YOLOv8n on the COCO8 dataset for 100 epochs at image size 640. For a full list of available arguments see the [Configuration](../usage/cfg.md) page.

!!! Example

    === "Python"

        ```python
        from ultralytics import YOLO

        # Load a model
        model = YOLO("yolov8n.yaml")  # build a new model from YAML
        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
        model = YOLO("yolov8n.yaml").load("yolov8n.pt")  # build from YAML and transfer weights

        # Train the model
        results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
        ```
    === "CLI"

        ```bash
        # Build a new model from YAML and start training from scratch
        yolo detect train data=coco8.yaml model=yolov8n.yaml epochs=100 imgsz=640

        # Start training from a pretrained *.pt model
        yolo detect train data=coco8.yaml model=yolov8n.pt epochs=100 imgsz=640

        # Build a new model from YAML, transfer pretrained weights to it and start training
        yolo detect train data=coco8.yaml model=yolov8n.yaml pretrained=yolov8n.pt epochs=100 imgsz=640
        ```

### Dataset format

YOLO detection dataset format can be found in detail in the [Dataset Guide](../datasets/detect/index.md). To convert your existing dataset from other formats (like COCO etc.) to YOLO format, please use [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) tool by Ultralytics.

## Val

Validate trained YOLOv8n model accuracy on the COCO8 dataset. No argument need to passed as the `model` retains it's training `data` and arguments as model attributes.

!!! Example

    === "Python"

        ```python
        from ultralytics import YOLO

        # Load a model
        model = YOLO("yolov8n.pt")  # load an official model
        model = YOLO("path/to/best.pt")  # load a custom model

        # Validate the model
        metrics = model.val()  # no arguments needed, dataset and settings remembered
        metrics.box.map  # map50-95
        metrics.box.map50  # map50
        metrics.box.map75  # map75
        metrics.box.maps  # a list contains map50-95 of each category
        ```
    === "CLI"

        ```bash
        yolo detect val model=yolov8n.pt  # val official model
        yolo detect val model=path/to/best.pt  # val custom model
        ```

## Predict

Use a trained YOLOv8n model to run predictions on images.

!!! Example

    === "Python"

        ```python
        from ultralytics import YOLO

        # Load a model
        model = YOLO("yolov8n.pt")  # load an official model
        model = YOLO("path/to/best.pt")  # load a custom model

        # Predict with the model
        results = model("https://ultralytics.com/images/bus.jpg")  # predict on an image
        ```
    === "CLI"

        ```bash
        yolo detect predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'  # predict with official model
        yolo detect predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg'  # predict with custom model
        ```

See full `predict` mode details in the [Predict](../modes/predict.md) page.

## Export

Export a YOLOv8n model to a different format like ONNX, CoreML, etc.

!!! Example

    === "Python"

        ```python
        from ultralytics import YOLO

        # Load a model
        model = YOLO("yolov8n.pt")  # load an official model
        model = YOLO("path/to/best.pt")  # load a custom trained model

        # Export the model
        model.export(format="onnx")
        ```
    === "CLI"

        ```bash
        yolo export model=yolov8n.pt format=onnx  # export official model
        yolo export model=path/to/best.pt format=onnx  # export custom trained model
        ```

Available YOLOv8 export formats are in the table below. You can export to any format using the `format` argument, i.e. `format='onnx'` or `format='engine'`. You can predict or validate directly on exported models, i.e. `yolo predict model=yolov8n.onnx`. Usage examples are shown for your model after export completes.

| Format                                            | `format` Argument | Model                     | Metadata | Arguments                                                            |
|---------------------------------------------------|-------------------|---------------------------|----------|----------------------------------------------------------------------|
| [PyTorch](https://pytorch.org/)                   | -                 | `yolov8n.pt`              | ✅        | -                                                                    |
| [TorchScript](../integrations/torchscript.md)     | `torchscript`     | `yolov8n.torchscript`     | ✅        | `imgsz`, `optimize`, `batch`                                         |
| [ONNX](../integrations/onnx.md)                   | `onnx`            | `yolov8n.onnx`            | ✅        | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch`             |
| [OpenVINO](../integrations/openvino.md)           | `openvino`        | `yolov8n_openvino_model/` | ✅        | `imgsz`, `half`, `int8`, `batch`                                     |
| [TensorRT](../integrations/tensorrt.md)           | `engine`          | `yolov8n.engine`          | ✅        | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `int8`, `batch` |
| [CoreML](../integrations/coreml.md)               | `coreml`          | `yolov8n.mlpackage`       | ✅        | `imgsz`, `half`, `int8`, `nms`, `batch`                              |
| [TF SavedModel](../integrations/tf-savedmodel.md) | `saved_model`     | `yolov8n_saved_model/`    | ✅        | `imgsz`, `keras`, `int8`, `batch`                                    |
| [TF GraphDef](../integrations/tf-graphdef.md)     | `pb`              | `yolov8n.pb`              | ❌        | `imgsz`, `batch`                                                     |
| [TF Lite](../integrations/tflite.md)              | `tflite`          | `yolov8n.tflite`          | ✅        | `imgsz`, `half`, `int8`, `batch`                                     |
| [TF Edge TPU](../integrations/edge-tpu.md)        | `edgetpu`         | `yolov8n_edgetpu.tflite`  | ✅        | `imgsz`, `batch`                                                     |
| [TF.js](../integrations/tfjs.md)                  | `tfjs`            | `yolov8n_web_model/`      | ✅        | `imgsz`, `half`, `int8`, `batch`                                     |
| [PaddlePaddle](../integrations/paddlepaddle.md)   | `paddle`          | `yolov8n_paddle_model/`   | ✅        | `imgsz`, `batch`                                                     |
| [NCNN](../integrations/ncnn.md)                   | `ncnn`            | `yolov8n_ncnn_model/`     | ✅        | `imgsz`, `half`, `batch`                                             |

See full `export` details in the [Export](../modes/export.md) page.
