ultralytics/docs/en/tasks/segment.md

---
comments: true
description: Learn how to use instance segmentation models with Ultralytics YOLO. Instructions on training, validation, image prediction, and model export.
keywords: yolov8, instance segmentation, Ultralytics, COCO dataset, image segmentation, object detection, model training, model validation, image prediction, model export
---

# Instance Segmentation

<img width="1024" src="https://user-images.githubusercontent.com/26833433/243418644-7df320b8-098d-47f1-85c5-26604d761286.png" alt="Instance segmentation examples">

Instance segmentation goes a step further than object detection and involves identifying individual objects in an image and segmenting them from the rest of the image.

The output of an instance segmentation model is a set of masks or contours that outline each object in the image, along with class labels and confidence scores for each object. Instance segmentation is useful when you need to know not only where objects are in an image, but also what their exact shape is.

<p align="center">
  <br>
  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/o4Zd-IeMlSY?si=37nusCzDTd74Obsp"
    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> Run Segmentation with Pre-Trained Ultralytics YOLOv8 Model in Python.
</p>

!!! Tip "Tip"

    YOLOv8 Segment models use the `-seg` suffix, i.e. `yolov8n-seg.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 Segment 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>box<br>50-95 | mAP<sup>mask<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-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-seg.pt) | 640                   | 36.7                 | 30.5                  | 96.1                           | 1.21                                | 3.4                | 12.6              |
| [YOLOv8s-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-seg.pt) | 640                   | 44.6                 | 36.8                  | 155.7                          | 1.47                                | 11.8               | 42.6              |
| [YOLOv8m-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-seg.pt) | 640                   | 49.9                 | 40.8                  | 317.0                          | 2.18                                | 27.3               | 110.2             |
| [YOLOv8l-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-seg.pt) | 640                   | 52.3                 | 42.6                  | 572.4                          | 2.79                                | 46.0               | 220.5             |
| [YOLOv8x-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-seg.pt) | 640                   | 53.4                 | 43.4                  | 712.1                          | 4.02                                | 71.8               | 344.1             |

- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](https://cocodataset.org) dataset. <br>Reproduce by `yolo val segment 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 segment data=coco8-seg.yaml batch=1 device=0|cpu`

## Train

Train YOLOv8n-seg on the COCO128-seg 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-seg.yaml')  # build a new model from YAML
        model = YOLO('yolov8n-seg.pt')  # load a pretrained model (recommended for training)
        model = YOLO('yolov8n-seg.yaml').load('yolov8n.pt')  # build from YAML and transfer weights

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

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

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

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

### Dataset format

YOLO segmentation dataset format can be found in detail in the [Dataset Guide](../datasets/segment/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-seg model accuracy on the COCO128-seg dataset. No argument need to passed as the `model`
retains its training `data` and arguments as model attributes.

!!! Example

    === "Python"

        ```python
        from ultralytics import YOLO

        # Load a model
        model = YOLO('yolov8n-seg.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(B)
        metrics.box.map50  # map50(B)
        metrics.box.map75  # map75(B)
        metrics.box.maps   # a list contains map50-95(B) of each category
        metrics.seg.map    # map50-95(M)
        metrics.seg.map50  # map50(M)
        metrics.seg.map75  # map75(M)
        metrics.seg.maps   # a list contains map50-95(M) of each category
        ```
    === "CLI"

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

## Predict

Use a trained YOLOv8n-seg model to run predictions on images.

!!! Example

    === "Python"

        ```python
        from ultralytics import YOLO

        # Load a model
        model = YOLO('yolov8n-seg.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 segment predict model=yolov8n-seg.pt source='https://ultralytics.com/images/bus.jpg'  # predict with official model
        yolo segment 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](https://docs.ultralytics.com/modes/predict/) page.

## Export

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

!!! Example

    === "Python"

        ```python
        from ultralytics import YOLO

        # Load a model
        model = YOLO('yolov8n-seg.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-seg.pt format=onnx  # export official model
        yolo export model=path/to/best.pt format=onnx  # export custom trained model
        ```

Available YOLOv8-seg 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-seg.onnx`. Usage examples are shown for your model after export completes.

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

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