ultralytics/docs/en/usage/python.md

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true	Boost your Python projects with object detection, segmentation and classification using YOLOv8. Explore how to load, train, validate, predict, export, track and benchmark models with ease.	YOLOv8, Ultralytics, Python, object detection, segmentation, classification, model training, validation, prediction, model export, benchmark, real-time tracking

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!

Watch: Mastering Ultralytics YOLOv8: Python

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 'coco128.yaml' dataset for 3 epochs
results = model.train(data='coco128.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

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='coco128.yaml', epochs=5)
    ```

=== "Resume"

    ```python
    model = YOLO("last.pt")
    results = model.train(resume=True)
    ```

Train Examples{ .md-button }

Val

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

      model = YOLO('yolov8n.yaml')
      model.train(data='coco128.yaml', epochs=5)
      model.val()  # It'll automatically evaluate the data you trained.
    ```

=== "Val independently"

    ```python
      from ultralytics import YOLO

      model = YOLO("model.pt")
      # It'll use the data YAML file in model.pt if you don't set data.
      model.val()
      # or you can set the data you want to val
      model.val(data='coco128.yaml')
    ```

Val Examples{ .md-button }

Predict

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
    from ultralytics import YOLO
    from PIL import Image
    import cv2

    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{ .md-button }

Export

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{ .md-button }

Track

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{ .md-button }

Benchmark

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{ .md-button }

Explorer

Explorer API can be used to explore datasets with advanced semantic, vector-similarity and SQL search among other features. It also 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='coco128.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='coco128.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{ .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 DetectionTrainer, DetectionValidator, DetectionPredictor

    # 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{ .md-button }