| --- |
| 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 goes a step further than object detection and involves identifying individual objects in an image |
| and segmenting them from the rest of the image. |
|
|
| <img width="1024" src="https://user-images.githubusercontent.com/26833433/243418644-7df320b8-098d-47f1-85c5-26604d761286.png"> |
|
|
| 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. |
|
|
| !!! 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/v0.0.0/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/v0.0.0/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/v0.0.0/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/v0.0.0/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/v0.0.0/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](http://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=coco128-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='coco128-seg.yaml', epochs=100, imgsz=640) |
| ``` |
| === "CLI" |
| |
| ```bash |
| # Build a new model from YAML and start training from scratch |
| yolo segment train data=coco128-seg.yaml model=yolov8n-seg.yaml epochs=100 imgsz=640 |
| |
| # Start training from a pretrained *.pt model |
| yolo segment train data=coco128-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=coco128-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 tool](https://github.com/ultralytics/JSON2YOLO) by Ultralytics. |
|
|
| ## Val |
|
|
| Validate trained YOLOv8n-seg model accuracy on the COCO128-seg 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-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 |
| |
| # 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 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](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n-seg.torchscript` | ✅ | `imgsz`, `optimize` | |
| | [ONNX](https://onnx.ai/) | `onnx` | `yolov8n-seg.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset` | |
| | [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n-seg_openvino_model/` | ✅ | `imgsz`, `half` | |
| | [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n-seg.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` | |
| | [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n-seg.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms` | |
| | [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n-seg_saved_model/` | ✅ | `imgsz`, `keras` | |
| | [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n-seg.pb` | ❌ | `imgsz` | |
| | [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n-seg.tflite` | ✅ | `imgsz`, `half`, `int8` | |
| | [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n-seg_edgetpu.tflite` | ✅ | `imgsz` | |
| | [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-seg_web_model/` | ✅ | `imgsz` | |
| | [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-seg_paddle_model/` | ✅ | `imgsz` | |
| | [ncnn](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n-seg_ncnn_model/` | ✅ | `imgsz`, `half` | |
|
|
| See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page. |
|
|
