ultralytics/docs/en/tasks/detect.md

comments	description	keywords
true	Learn about object detection with YOLOv8. Explore pretrained models, training, validation, prediction, and export details for efficient object recognition.	object detection, YOLOv8, pretrained models, training, validation, prediction, export, machine learning, computer vision

Object Detection

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.

Watch: Object Detection with Pre-trained Ultralytics YOLOv8 Model.

!!! 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

YOLOv8 pretrained Detect models are shown here. Detect, Segment and Pose models are pretrained on the COCO dataset, while Classify models are pretrained on the ImageNet dataset.

Models download automatically from the latest Ultralytics release on first use.

Model	size (pixels)	mAPval 50-95	Speed CPU ONNX (ms)	Speed A100 TensorRT (ms)	params (M)	FLOPs (B)
YOLOv8n	640	37.3	80.4	0.99	3.2	8.7
YOLOv8s	640	44.9	128.4	1.20	11.2	28.6
YOLOv8m	640	50.2	234.7	1.83	25.9	78.9
YOLOv8l	640	52.9	375.2	2.39	43.7	165.2
YOLOv8x	640	53.9	479.1	3.53	68.2	257.8

• mAP^val values are for single-model single-scale on COCO val2017 dataset.
  Reproduce by yolo val detect data=coco.yaml device=0
• Speed averaged over COCO val images using an Amazon EC2 P4d instance.
  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 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. To convert your existing dataset from other formats (like COCO etc.) to YOLO format, please use JSON2YOLO tool by Ultralytics.

Val

Validate trained YOLOv8n model accuracy on the COCO8 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.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 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	-	yolov8n.pt	✅	-
TorchScript	torchscript	yolov8n.torchscript	✅	imgsz, optimize, batch
ONNX	onnx	yolov8n.onnx	✅	imgsz, half, dynamic, simplify, opset, batch
OpenVINO	openvino	yolov8n_openvino_model/	✅	imgsz, half, int8, batch
TensorRT	engine	yolov8n.engine	✅	imgsz, half, dynamic, simplify, workspace, int8, batch
CoreML	coreml	yolov8n.mlpackage	✅	imgsz, half, int8, nms, batch
TF SavedModel	saved_model	yolov8n_saved_model/	✅	imgsz, keras, int8, batch
TF GraphDef	pb	yolov8n.pb	❌	imgsz, batch
TF Lite	tflite	yolov8n.tflite	✅	imgsz, half, int8, batch
TF Edge TPU	edgetpu	yolov8n_edgetpu.tflite	✅	imgsz
TF.js	tfjs	yolov8n_web_model/	✅	imgsz, half, int8, batch
PaddlePaddle	paddle	yolov8n_paddle_model/	✅	imgsz, batch
NCNN	ncnn	yolov8n_ncnn_model/	✅	imgsz, half, batch

See full export details in the Export page.

FAQ

How do I train a YOLOv8 model on my custom dataset?

Training a YOLOv8 model on a custom dataset involves a few steps:

1. Prepare the Dataset: Ensure your dataset is in the YOLO format. For guidance, refer to our Dataset Guide.
2. Load the Model: Use the Ultralytics YOLO library to load a pre-trained model or create a new model from a YAML file.
3. Train the Model: Execute the train method in Python or the yolo detect train command in CLI.

!!! Example

=== "Python"

    ```python
    from ultralytics import YOLO

    # Load a pretrained model
    model = YOLO("yolov8n.pt")

    # Train the model on your custom dataset
    model.train(data="my_custom_dataset.yaml", epochs=100, imgsz=640)
    ```

=== "CLI"

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

For detailed configuration options, visit the Configuration page.

What pretrained models are available in YOLOv8?

Ultralytics YOLOv8 offers various pretrained models for object detection, segmentation, and pose estimation. These models are pretrained on the COCO dataset or ImageNet for classification tasks. Here are some of the available models:

• YOLOv8n
• YOLOv8s
• YOLOv8m
• YOLOv8l
• YOLOv8x

For a detailed list and performance metrics, refer to the Models section.

How can I validate the accuracy of my trained YOLOv8 model?

To validate the accuracy of your trained YOLOv8 model, you can use the .val() method in Python or the yolo detect val command in CLI. This will provide metrics like mAP50-95, mAP50, and more.

!!! Example

=== "Python"

    ```python
    from ultralytics import YOLO

    # Load the model
    model = YOLO("path/to/best.pt")

    # Validate the model
    metrics = model.val()
    print(metrics.box.map)  # mAP50-95
    ```

=== "CLI"

    ```bash
    yolo detect val model=path/to/best.pt
    ```

For more validation details, visit the Val page.

What formats can I export a YOLOv8 model to?

Ultralytics YOLOv8 allows exporting models to various formats such as ONNX, TensorRT, CoreML, and more to ensure compatibility across different platforms and devices.

!!! Example

=== "Python"

    ```python
    from ultralytics import YOLO

    # Load the model
    model = YOLO("yolov8n.pt")

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

=== "CLI"

    ```bash
    yolo export model=yolov8n.pt format=onnx
    ```

Check the full list of supported formats and instructions on the Export page.

Why should I use Ultralytics YOLOv8 for object detection?

Ultralytics YOLOv8 is designed to offer state-of-the-art performance for object detection, segmentation, and pose estimation. Here are some key advantages:

1. Pretrained Models: Utilize models pretrained on popular datasets like COCO and ImageNet for faster development.
2. High Accuracy: Achieves impressive mAP scores, ensuring reliable object detection.
3. Speed: Optimized for real-time inference, making it ideal for applications requiring swift processing.
4. Flexibility: Export models to various formats like ONNX and TensorRT for deployment across multiple platforms.

Explore our Blog for use cases and success stories showcasing YOLOv8 in action.