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146 lines
6.1 KiB
146 lines
6.1 KiB
Instance segmentation goes a step further than object detection and involves identifying individual objects in an image
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and segmenting them from the rest of the image.
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<img width="1024" src="https://user-images.githubusercontent.com/26833433/212094133-6bb8c21c-3d47-41df-a512-81c5931054ae.png">
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The output of an instance segmentation model is a set of masks or
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contours that outline each object in the image, along with class labels and confidence scores for each object. Instance
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segmentation is useful when you need to know not only where objects are in an image, but also what their exact shape is.
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!!! tip "Tip"
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YOLOv8 _segmentation_ models use the `-seg` suffix, i.e. `yolov8n-seg.pt` and are pretrained on COCO.
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[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/v8/seg){.md-button .md-button--primary}
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## Train
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Train YOLOv8n-seg on the COCO128-seg dataset for 100 epochs at image size 640. For a full list of available
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arguments see the [Configuration](../usage/cfg.md) page.
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!!! example ""
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n-seg.yaml') # build a new model from scratch
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model = YOLO('yolov8n-seg.pt') # load a pretrained model (recommended for training)
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# Train the model
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model.train(data='coco128-seg.yaml', epochs=100, imgsz=640)
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```
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=== "CLI"
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```bash
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yolo segment train data=coco128-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
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```
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## Val
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Validate trained YOLOv8n-seg model accuracy on the COCO128-seg dataset. No argument need to passed as the `model`
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retains it's training `data` and arguments as model attributes.
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!!! example ""
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n-seg.pt') # load an official model
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model = YOLO('path/to/best.pt') # load a custom model
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# Validate the model
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metrics = model.val() # no arguments needed, dataset and settings remembered
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metrics.box.map # map50-95(B)
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metrics.box.map50 # map50(B)
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metrics.box.map75 # map75(B)
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metrics.box.maps # a list contains map50-95(B) of each category
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metrics.seg.map # map50-95(M)
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metrics.seg.map50 # map50(M)
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metrics.seg.map75 # map75(M)
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metrics.seg.maps # a list contains map50-95(M) of each category
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```
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=== "CLI"
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```bash
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yolo segment val model=yolov8n-seg.pt # val official model
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yolo segment val model=path/to/best.pt # val custom model
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```
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## Predict
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Use a trained YOLOv8n-seg model to run predictions on images.
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!!! example ""
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n-seg.pt') # load an official model
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model = YOLO('path/to/best.pt') # load a custom model
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# Predict with the model
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results = model('https://ultralytics.com/images/bus.jpg') # predict on an image
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```
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=== "CLI"
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```bash
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yolo segment predict model=yolov8n-seg.pt source='https://ultralytics.com/images/bus.jpg' # predict with official model
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yolo segment predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg' # predict with custom model
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```
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Read more details of `predict` in our [Predict](https://docs.ultralytics.com/modes/predict/) page.
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## Export
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Export a YOLOv8n-seg model to a different format like ONNX, CoreML, etc.
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!!! example ""
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n-seg.pt') # load an official model
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model = YOLO('path/to/best.pt') # load a custom trained
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# Export the model
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model.export(format='onnx')
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```
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=== "CLI"
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```bash
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yolo export model=yolov8n-seg.pt format=onnx # export official model
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yolo export model=path/to/best.pt format=onnx # export custom trained model
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```
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Available YOLOv8-seg export formats are in the table below. You can predict or validate directly on exported models,
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i.e. `yolo predict model=yolov8n-seg.onnx`.
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| Format | `format` Argument | Model | Metadata |
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|--------------------------------------------------------------------|-------------------|-------------------------------|----------|
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| [PyTorch](https://pytorch.org/) | - | `yolov8n-seg.pt` | ✅ |
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| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n-seg.torchscript` | ✅ |
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| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n-seg.onnx` | ✅ |
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| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n-seg_openvino_model/` | ✅ |
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| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n-seg.engine` | ✅ |
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| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n-seg.mlmodel` | ✅ |
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| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n-seg_saved_model/` | ✅ |
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| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n-seg.pb` | ❌ |
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| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n-seg.tflite` | ✅ |
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| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n-seg_edgetpu.tflite` | ✅ |
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| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-seg_web_model/` | ✅ |
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| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-seg_paddle_model/` | ✅ |
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