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---

comments: true
description: Learn to integrate YOLOv8 in Python for object detection, segmentation, and classification. Load, train models, and make predictions easily with our comprehensive guide.
keywords: YOLOv8, Python, object detection, segmentation, classification, machine learning, AI, pretrained models, train models, make predictions
---


# Python Usage

Welcome to the YOLOv8 Python Usage documentation! This guide is designed to help you seamlessly integrate YOLOv8 into your Python projects for object detection, segmentation, and classification. Here, you'll learn how to load and use pretrained models, train new models, and perform predictions on images. The easy-to-use Python interface is a valuable resource for anyone looking to incorporate YOLOv8 into their Python projects, allowing you to quickly implement advanced object detection capabilities. Let's get started!

<p align="center">
  <br>
  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/GsXGnb-A4Kc?start=58"

    title="YouTube video player" frameborder="0"

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    allowfullscreen>
  </iframe>
  <br>
  <strong>Watch:</strong> Mastering Ultralytics YOLOv8: Python
</p>

For example, users can load a model, train it, evaluate its performance on a validation set, and even export it to ONNX format with just a few lines of code.

!!! Example "Python"

    ```python

    from ultralytics import YOLO


    # Create a new YOLO model from scratch

    model = YOLO("yolov8n.yaml")


    # Load a pretrained YOLO model (recommended for training)

    model = YOLO("yolov8n.pt")


    # Train the model using the 'coco8.yaml' dataset for 3 epochs

    results = model.train(data="coco8.yaml", epochs=3)


    # Evaluate the model's performance on the validation set

    results = model.val()


    # Perform object detection on an image using the model

    results = model("https://ultralytics.com/images/bus.jpg")


    # Export the model to ONNX format

    success = model.export(format="onnx")

    ```


## [Train](../modes/train.md)

Train mode is used for training a YOLOv8 model on a custom dataset. In this mode, the model is trained using the specified dataset and hyperparameters. The training process involves optimizing the model's parameters so that it can accurately predict the classes and locations of objects in an image.

!!! Example "Train"

    === "From pretrained(recommended)"


        ```python

        from ultralytics import YOLO


        model = YOLO("yolov8n.pt")  # pass any model type

        results = model.train(epochs=5)

        ```


    === "From scratch"


        ```python

        from ultralytics import YOLO


        model = YOLO("yolov8n.yaml")

        results = model.train(data="coco8.yaml", epochs=5)

        ```


    === "Resume"


        ```python

        model = YOLO("last.pt")

        results = model.train(resume=True)

        ```


[Train Examples](../modes/train.md){ .md-button }

## [Val](../modes/val.md)

Val mode is used for validating a YOLOv8 model after it has been trained. In this mode, the model is evaluated on a validation set to measure its accuracy and generalization performance. This mode can be used to tune the hyperparameters of the model to improve its performance.

!!! Example "Val"

    === "Val after training"


        ```python

        from ultralytics import YOLO


        # Load a YOLOv8 model

        model = YOLO("yolov8n.yaml")


        # Train the model

        model.train(data="coco8.yaml", epochs=5)


        # Validate on training data

        model.val()

        ```


    === "Val on another dataset"


        ```python

        from ultralytics import YOLO


        # Load a YOLOv8 model

        model = YOLO("yolov8n.yaml")


        # Train the model

        model.train(data="coco8.yaml", epochs=5)


        # Validate on separate data

        model.val(data="path/to/separate/data.yaml")

        ```


[Val Examples](../modes/val.md){ .md-button }

## [Predict](../modes/predict.md)

Predict mode is used for making predictions using a trained YOLOv8 model on new images or videos. In this mode, the model is loaded from a checkpoint file, and the user can provide images or videos to perform inference. The model predicts the classes and locations of objects in the input images or videos.

!!! Example "Predict"

    === "From source"


        ```python

        import cv2

        from PIL import Image


        from ultralytics import YOLO


        model = YOLO("model.pt")

        # accepts all formats - image/dir/Path/URL/video/PIL/ndarray. 0 for webcam

        results = model.predict(source="0")

        results = model.predict(source="folder", show=True)  # Display preds. Accepts all YOLO predict arguments


        # from PIL

        im1 = Image.open("bus.jpg")

        results = model.predict(source=im1, save=True)  # save plotted images


        # from ndarray

        im2 = cv2.imread("bus.jpg")

        results = model.predict(source=im2, save=True, save_txt=True)  # save predictions as labels


        # from list of PIL/ndarray

        results = model.predict(source=[im1, im2])

        ```


    === "Results usage"


        ```python

        # results would be a list of Results object including all the predictions by default

        # but be careful as it could occupy a lot memory when there're many images,

        # especially the task is segmentation.

        # 1. return as a list

        results = model.predict(source="folder")


        # results would be a generator which is more friendly to memory by setting stream=True

        # 2. return as a generator

        results = model.predict(source=0, stream=True)


        for result in results:

            # Detection

            result.boxes.xyxy  # box with xyxy format, (N, 4)

            result.boxes.xywh  # box with xywh format, (N, 4)

            result.boxes.xyxyn  # box with xyxy format but normalized, (N, 4)

            result.boxes.xywhn  # box with xywh format but normalized, (N, 4)

            result.boxes.conf  # confidence score, (N, 1)

            result.boxes.cls  # cls, (N, 1)


            # Segmentation

            result.masks.data  # masks, (N, H, W)

            result.masks.xy  # x,y segments (pixels), List[segment] * N

            result.masks.xyn  # x,y segments (normalized), List[segment] * N


            # Classification

            result.probs  # cls prob, (num_class, )


        # Each result is composed of torch.Tensor by default,

        # in which you can easily use following functionality:

        result = result.cuda()

        result = result.cpu()

        result = result.to("cpu")

        result = result.numpy()

        ```


[Predict Examples](../modes/predict.md){ .md-button }

## [Export](../modes/export.md)

Export mode is used for exporting a YOLOv8 model to a format that can be used for deployment. In this mode, the model is converted to a format that can be used by other software applications or hardware devices. This mode is useful when deploying the model to production environments.

!!! Example "Export"

    === "Export to ONNX"


        Export an official YOLOv8n model to ONNX with dynamic batch-size and image-size.

        ```python

        from ultralytics import YOLO


        model = YOLO("yolov8n.pt")

        model.export(format="onnx", dynamic=True)

        ```


    === "Export to TensorRT"


        Export an official YOLOv8n model to TensorRT on `device=0` for acceleration on CUDA devices.

        ```python

        from ultralytics import YOLO


        model = YOLO("yolov8n.pt")

        model.export(format="onnx", device=0)

        ```


[Export Examples](../modes/export.md){ .md-button }

## [Track](../modes/track.md)

Track mode is used for tracking objects in real-time using a YOLOv8 model. In this mode, the model is loaded from a checkpoint file, and the user can provide a live video stream to perform real-time object tracking. This mode is useful for applications such as surveillance systems or self-driving cars.

!!! Example "Track"

    === "Python"


        ```python

        from ultralytics import YOLO


        # Load a model

        model = YOLO("yolov8n.pt")  # load an official detection model

        model = YOLO("yolov8n-seg.pt")  # load an official segmentation model

        model = YOLO("path/to/best.pt")  # load a custom model


        # Track with the model

        results = model.track(source="https://youtu.be/LNwODJXcvt4", show=True)

        results = model.track(source="https://youtu.be/LNwODJXcvt4", show=True, tracker="bytetrack.yaml")

        ```


[Track Examples](../modes/track.md){ .md-button }

## [Benchmark](../modes/benchmark.md)

Benchmark mode is used to profile the speed and accuracy of various export formats for YOLOv8. The benchmarks provide information on the size of the exported format, its `mAP50-95` metrics (for object detection and segmentation) or `accuracy_top5` metrics (for classification), and the inference time in milliseconds per image across various export formats like ONNX, OpenVINO, TensorRT and others. This information can help users choose the optimal export format for their specific use case based on their requirements for speed and accuracy.

!!! Example "Benchmark"

    === "Python"


        Benchmark an official YOLOv8n model across all export formats.

        ```python

        from ultralytics.utils.benchmarks import benchmark


        # Benchmark

        benchmark(model="yolov8n.pt", data="coco8.yaml", imgsz=640, half=False, device=0)

        ```


[Benchmark Examples](../modes/benchmark.md){ .md-button }

## Explorer

Explorer API can be used to explore datasets with advanced semantic, vector-similarity and SQL search among other features. It also enabled searching for images based on their content using natural language by utilizing the power of LLMs. The Explorer API allows you to write your own dataset exploration notebooks or scripts to get insights into your datasets.

!!! Example "Semantic Search Using Explorer"

    === "Using Images"


        ```python

        from ultralytics import Explorer


        # create an Explorer object

        exp = Explorer(data="coco8.yaml", model="yolov8n.pt")

        exp.create_embeddings_table()


        similar = exp.get_similar(img="https://ultralytics.com/images/bus.jpg", limit=10)

        print(similar.head())


        # Search using multiple indices

        similar = exp.get_similar(

            img=["https://ultralytics.com/images/bus.jpg", "https://ultralytics.com/images/bus.jpg"], limit=10

        )

        print(similar.head())

        ```


    === "Using Dataset Indices"


        ```python

        from ultralytics import Explorer


        # create an Explorer object

        exp = Explorer(data="coco8.yaml", model="yolov8n.pt")

        exp.create_embeddings_table()


        similar = exp.get_similar(idx=1, limit=10)

        print(similar.head())


        # Search using multiple indices

        similar = exp.get_similar(idx=[1, 10], limit=10)

        print(similar.head())

        ```


[Explorer](../datasets/explorer/index.md){ .md-button }

## Using Trainers

`YOLO` model class is a high-level wrapper on the Trainer classes. Each YOLO task has its own trainer that inherits from `BaseTrainer`.

!!! Tip "Detection Trainer Example"

        ```python

        from ultralytics.models.yolo import DetectionPredictor, DetectionTrainer, DetectionValidator


        # trainer

        trainer = DetectionTrainer(overrides={})

        trainer.train()

        trained_model = trainer.best


        # Validator

        val = DetectionValidator(args=...)

        val(model=trained_model)


        # predictor

        pred = DetectionPredictor(overrides={})

        pred(source=SOURCE, model=trained_model)


        # resume from last weight

        overrides["resume"] = trainer.last

        trainer = detect.DetectionTrainer(overrides=overrides)

        ```


You can easily customize Trainers to support custom tasks or explore R&D ideas. Learn more about Customizing `Trainers`, `Validators` and `Predictors` to suit your project needs in the Customization Section.

[Customization tutorials](engine.md){ .md-button }

## FAQ

### How can I integrate YOLOv8 into my Python project for object detection?

Integrating Ultralytics YOLOv8 into your Python projects is simple. You can load a pre-trained model or train a new model from scratch. Here's how to get started:

```python

from ultralytics import YOLO



# Load a pretrained YOLO model

model = YOLO("yolov8n.pt")



# Perform object detection on an image

results = model("https://ultralytics.com/images/bus.jpg")



# Visualize the results

for result in results:

    result.show()

```

See more detailed examples in our [Predict Mode](../modes/predict.md) section.

### What are the different modes available in YOLOv8?

Ultralytics YOLOv8 provides various modes to cater to different machine learning workflows. These include:

- **[Train](../modes/train.md)**: Train a model using custom datasets.
- **[Val](../modes/val.md)**: Validate model performance on a validation set.
- **[Predict](../modes/predict.md)**: Make predictions on new images or video streams.
- **[Export](../modes/export.md)**: Export models to various formats like ONNX, TensorRT.
- **[Track](../modes/track.md)**: Real-time object tracking in video streams.
- **[Benchmark](../modes/benchmark.md)**: Benchmark model performance across different configurations.

Each mode is designed to provide comprehensive functionalities for different stages of model development and deployment.

### How do I train a custom YOLOv8 model using my dataset?

To train a custom YOLOv8 model, you need to specify your dataset and other hyperparameters. Here's a quick example:

```python

from ultralytics import YOLO



# Load the YOLO model

model = YOLO("yolov8n.yaml")



# Train the model with custom dataset

model.train(data="path/to/your/dataset.yaml", epochs=10)

```

For more details on training and hyperlinks to example usage, visit our [Train Mode](../modes/train.md) page.

### How do I export YOLOv8 models for deployment?

Exporting YOLOv8 models in a format suitable for deployment is straightforward with the `export` function. For example, you can export a model to ONNX format:

```python

from ultralytics import YOLO



# Load the YOLO model

model = YOLO("yolov8n.pt")



# Export the model to ONNX format

model.export(format="onnx")

```

For various export options, refer to the [Export Mode](../modes/export.md) documentation.

### Can I validate my YOLOv8 model on different datasets?

Yes, validating YOLOv8 models on different datasets is possible. After training, you can use the validation mode to evaluate the performance:

```python

from ultralytics import YOLO



# Load a YOLOv8 model

model = YOLO("yolov8n.yaml")



# Train the model

model.train(data="coco8.yaml", epochs=5)



# Validate the model on a different dataset

model.val(data="path/to/separate/data.yaml")

```

Check the [Val Mode](../modes/val.md) page for detailed examples and usage.