ultralytics/docs/tasks/detect.md

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.

!!! tip "Tip"

YOLOv8 _detection_ models have no suffix and are the default YOLOv8 models, i.e. `yolov8n.pt` and are pretrained on COCO.

Models{ .md-button .md-button--primary}

Train

Train YOLOv8n on the COCO128 dataset for 100 epochs at image size 640. For a full list of available arguments see the Configuration page.

!!! example ""

=== "Python"

    ```python
    from ultralytics import YOLO
    
    # Load a model
    model = YOLO("yolov8n.yaml")  # build a new model from scratch
    model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
    
    # Train the model
    model.train(data="coco128.yaml", epochs=100, imgsz=640)
    ```
=== "CLI"

    ```bash
    yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640
    ```

Val

Validate trained YOLOv8n model accuracy on the COCO128 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
    ```

Read more details of predict in our Predict 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
    
    # 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 predict or validate directly on exported models, i.e. yolo predict model=yolov8n.onnx.

Format	format Argument	Model	Metadata
PyTorch	-	yolov8n.pt	✅
TorchScript	torchscript	yolov8n.torchscript	✅
ONNX	onnx	yolov8n.onnx	✅
OpenVINO	openvino	yolov8n_openvino_model/	✅
TensorRT	engine	yolov8n.engine	✅
CoreML	coreml	yolov8n.mlmodel	✅
TF SavedModel	saved_model	yolov8n_saved_model/	✅
TF GraphDef	pb	yolov8n.pb	❌
TF Lite	tflite	yolov8n.tflite	✅
TF Edge TPU	edgetpu	yolov8n_edgetpu.tflite	✅
TF.js	tfjs	yolov8n_web_model/	✅
PaddlePaddle	paddle	yolov8n_paddle_model/	✅