All the YOLOv8-World weights have been directly migrated from the official [YOLO-World](https://github.com/AILab-CVC/YOLO-World) repository, highlighting their excellent contributions.

=== "Detection (COCO)"

    | Model Type      | mAP  | mAP50 | mAP75 |
    | --------------- | ---- | ----- | ----- |
    | yolov8s-world   | 37.4 | 52.0  | 40.6  |
    | yolov8s-worldv2 | 37.7 | 52.2  | 41.0  |
    | yolov8m-world   | 42.0 | 57.0  | 45.6  |
    | yolov8m-worldv2 | 43.0 | 58.4  | 46.8  |
    | yolov8l-world   | 45.7 | 61.3  | 49.8  |
    | yolov8l-worldv2 | 45.8 | 61.3  | 49.8  |
    | yolov8x-world   | 47.0 | 63.0  | 51.2  |
    | yolov8x-worldv2 | 47.1 | 62.8  | 51.4  |

We strongly recommend to use `yolov8-worldv2` model for custom training, because it supports deterministic training and also easy to export other formats i.e onnx/tensorrt.

=== "Python"

    [PyTorch](https://www.ultralytics.com/glossary/pytorch) pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLOWorld()` class to create a model instance in python:

    ```python
    from ultralytics import YOLOWorld

    # Load a pretrained YOLOv8s-worldv2 model
    model = YOLOWorld("yolov8s-worldv2.pt")

    # Train the model on the COCO8 example dataset for 100 epochs
    results = model.train(data="coco8.yaml", epochs=100, imgsz=640)

    # Run inference with the YOLO-World model on the 'bus.jpg' image
    results = model("path/to/bus.jpg")
    ```

=== "CLI"

    ```bash
    # Load a pretrained YOLOv8s-worldv2 model and train it on the COCO8 example dataset for 100 epochs
    yolo train model=yolov8s-worldv2.yaml data=coco8.yaml epochs=100 imgsz=640
    ```

=== "Python"

    ```python
    from ultralytics import YOLOWorld

    # Initialize a YOLO-World model
    model = YOLOWorld("yolov8s-world.pt")  # or select yolov8m/l-world.pt for different sizes

    # Execute inference with the YOLOv8s-world model on the specified image
    results = model.predict("path/to/image.jpg")

    # Show results
    results[0].show()
    ```

=== "CLI"

    ```bash
    # Perform object detection using a YOLO-World model
    yolo predict model=yolov8s-world.pt source=path/to/image.jpg imgsz=640
    ```

=== "Python"

    ```python
    from ultralytics import YOLO

    # Create a YOLO-World model
    model = YOLO("yolov8s-world.pt")  # or select yolov8m/l-world.pt for different sizes

    # Conduct model validation on the COCO8 example dataset
    metrics = model.val(data="coco8.yaml")
    ```

=== "CLI"

    ```bash
    # Validate a YOLO-World model on the COCO8 dataset with a specified image size
    yolo val model=yolov8s-world.pt data=coco8.yaml imgsz=640
    ```

=== "Python"

    ```python
    from ultralytics import YOLO

    # Create a YOLO-World model
    model = YOLO("yolov8s-world.pt")  # or select yolov8m/l-world.pt for different sizes

    # Track with a YOLO-World model on a video
    results = model.track(source="path/to/video.mp4")
    ```

=== "CLI"

    ```bash
    # Track with a YOLO-World model on the video with a specified image size
    yolo track model=yolov8s-world.pt imgsz=640 source="path/to/video.mp4"
    ```

The YOLO-World models provided by Ultralytics come pre-configured with [COCO dataset](../datasets/detect/coco.md) categories as part of their offline vocabulary, enhancing efficiency for immediate application. This integration allows the YOLOv8-World models to directly recognize and predict the 80 standard categories defined in the COCO dataset without requiring additional setup or customization.

=== "Custom Inference Prompts"

    ```python
    from ultralytics import YOLO

    # Initialize a YOLO-World model
    model = YOLO("yolov8s-world.pt")  # or choose yolov8m/l-world.pt

    # Define custom classes
    model.set_classes(["person", "bus"])

    # Execute prediction for specified categories on an image
    results = model.predict("path/to/image.jpg")

    # Show results
    results[0].show()
    ```

Some users have found that appending an empty string `""` as a background class can improve detection performance in certain scenarios. This behavior appears to be scenario-dependent and the exact mechanism is not fully understood:

```python
model.set_classes(["person", "bus", ""])
```

=== "Persisting Models with Custom Vocabulary"

    First load a YOLO-World model, set custom classes for it and save it:

    ```python
    from ultralytics import YOLO

    # Initialize a YOLO-World model
    model = YOLO("yolov8s-world.pt")  # or select yolov8m/l-world.pt

    # Define custom classes
    model.set_classes(["person", "bus"])

    # Save the model with the defined offline vocabulary
    model.save("custom_yolov8s.pt")
    ```

    After saving, the custom_yolov8s.pt model behaves like any other pretrained YOLOv8 model but with a key difference: it is now optimized to detect only the classes you have defined. This customization can significantly improve detection performance and efficiency for your specific application scenarios.

    ```python
    from ultralytics import YOLO

    # Load your custom model
    model = YOLO("custom_yolov8s.pt")

    # Run inference to detect your custom classes
    results = model.predict("path/to/image.jpg")

    # Show results
    results[0].show()
    ```

`WorldTrainerFromScratch` is highly customized to allow training yolo-world models on both detection datasets and grounding datasets simultaneously. More details please checkout [ultralytics.model.yolo.world.train_world.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/world/train_world.py).

=== "Python"

    ```python
    from ultralytics import YOLOWorld
    from ultralytics.models.yolo.world.train_world import WorldTrainerFromScratch

    # Option 1: Use Python dictionary
    data = dict(
        train=dict(
            yolo_data=["Objects365.yaml"],
            grounding_data=[
                dict(
                    img_path="flickr30k/images",
                    json_file="flickr30k/final_flickr_separateGT_train.json",
                ),
                dict(
                    img_path="GQA/images",
                    json_file="GQA/final_mixed_train_no_coco.json",
                ),
            ],
        ),
        val=dict(yolo_data=["lvis.yaml"]),
    )

    # Option 2: Use YAML file (yolo_world_data.yaml)
    # train:
    #   yolo_data:
    #     - Objects365.yaml
    #   grounding_data:
    #     - img_path: flickr/full_images/
    #       json_file: flickr/annotations/final_flickr_separateGT_train_segm.json
    #     - img_path: mixed_grounding/gqa/images
    #       json_file: mixed_grounding/annotations/final_mixed_train_no_coco_segm.json
    # val:
    #   yolo_data:
    #     - lvis.yaml

    model = YOLOWorld("yolov8s-worldv2.yaml")
    model.train(
        data=data,  # or data="yolo_world_data.yaml" if using YAML file
        batch=128,
        epochs=100,
        trainer=WorldTrainerFromScratch,
    )
    ```

=== "BibTeX"

    ```bibtex
    @article{cheng2024yolow,
    title={YOLO-World: Real-Time Open-Vocabulary Object Detection},
    author={Cheng, Tianheng and Song, Lin and Ge, Yixiao and Liu, Wenyu and Wang, Xinggang and Shan, Ying},
    journal={arXiv preprint arXiv:2401.17270},
    year={2024}
    }
    ```

from ultralytics import YOLOWorld

# Initialize a YOLO-World model
model = YOLOWorld("yolov8s-world.pt")

# Define custom classes
model.set_classes(["person", "bus"])

# Execute prediction on an image
results = model.predict("path/to/image.jpg")

# Show results
results[0].show()

from ultralytics import YOLOWorld

# Load a pretrained YOLOv8s-worldv2 model
model = YOLOWorld("yolov8s-worldv2.pt")

# Train the model on the COCO8 dataset for 100 epochs
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)

yolo train model=yolov8s-worldv2.yaml data=coco8.yaml epochs=100 imgsz=640

from ultralytics import YOLOWorld
from ultralytics.models.yolo.world.train_world import WorldTrainerFromScratch

data = {
    "train": {
        "yolo_data": ["Objects365.yaml"],
        "grounding_data": [
            {
                "img_path": "flickr30k/images",
                "json_file": "flickr30k/final_flickr_separateGT_train.json",
            },
            {
                "img_path": "GQA/images",
                "json_file": "GQA/final_mixed_train_no_coco.json",
            },
        ],
    },
    "val": {"yolo_data": ["lvis.yaml"]},
}

model = YOLOWorld("yolov8s-worldv2.yaml")
model.train(data=data, batch=128, epochs=100, trainer=WorldTrainerFromScratch)