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# 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 YOLO Model.

Tip

YOLO11 Detect models are the default YOLO11 models, i.e. `yolo11n.pt` and are pretrained on COCO.

## Models

YOLO11 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 T4 TensorRT10 (ms) | params (M) | FLOPs (B)
--- | --- | --- | --- | --- | --- | ---
YOLO11n | 640 | 39.5 | 56.1 ± 0.8 | 1.5 ± 0.0 | 2.6 | 6.5
YOLO11s | 640 | 47.0 | 90.0 ± 1.2 | 2.5 ± 0.0 | 9.4 | 21.5
YOLO11m | 640 | 51.5 | 183.2 ± 2.0 | 4.7 ± 0.1 | 20.1 | 68.0
YOLO11l | 640 | 53.4 | 238.6 ± 1.4 | 6.2 ± 0.1 | 25.3 | 86.9
YOLO11x | 640 | 54.7 | 462.8 ± 6.7 | 11.3 ± 0.2 | 56.9 | 194.9

- mAPval 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=coco.yaml batch=1 device=0|cpu`

## Train

Train YOLO11n on the COCO8 dataset for 100 epochs at image size 640. For a full list of available arguments see the Configuration page.

Example

Python CLI

    from ultralytics import YOLO

    # Load a model
    model = YOLO("yolo11n.yaml")  # build a new model from YAML
    model = YOLO("yolo11n.pt")  # load a pretrained model (recommended for training)
    model = YOLO("yolo11n.yaml").load("yolo11n.pt")  # build from YAML and transfer weights

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

    # Build a new model from YAML and start training from scratch
    yolo detect train data=coco8.yaml model=yolo11n.yaml epochs=100 imgsz=640

    # Start training from a pretrained *.pt model
    yolo detect train data=coco8.yaml model=yolo11n.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=yolo11n.yaml pretrained=yolo11n.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 YOLO11n model accuracy on the COCO8 dataset. No arguments are needed as the `model` retains its training `data` and arguments as model attributes.

Example

Python CLI

    from ultralytics import YOLO

    # Load a model
    model = YOLO("yolo11n.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

    yolo detect val model=yolo11n.pt      # val official model
    yolo detect val model=path/to/best.pt # val custom model

## Predict

Use a trained YOLO11n model to run predictions on images.

Example

Python CLI

    from ultralytics import YOLO

    # Load a model
    model = YOLO("yolo11n.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

    # Access the results
    for result in results:
        xywh = result.boxes.xywh  # center-x, center-y, width, height
        xywhn = result.boxes.xywhn  # normalized
        xyxy = result.boxes.xyxy  # top-left-x, top-left-y, bottom-right-x, bottom-right-y
        xyxyn = result.boxes.xyxyn  # normalized
        names = [result.names[cls.item()] for cls in result.boxes.cls.int()]  # class name of each box
        confs = result.boxes.conf  # confidence score of each box

    yolo detect predict model=yolo11n.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 YOLO11n model to a different format like ONNX, CoreML, etc.

Example

Python CLI

    from ultralytics import YOLO

    # Load a model
    model = YOLO("yolo11n.pt")  # load an official model
    model = YOLO("path/to/best.pt")  # load a custom trained model

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

    yolo export model=yolo11n.pt format=onnx      # export official model
    yolo export model=path/to/best.pt format=onnx # export custom trained model

Available YOLO11 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=yolo11n.onnx`. Usage examples are shown for your model after export completes.

Format | `format` Argument | Model | Metadata | Arguments
--- | --- | --- | --- | ---
PyTorch | - | `yolo11n.pt` | ✅ | -
TorchScript | `torchscript` | `yolo11n.torchscript` | ✅ | `imgsz`, `half`, `dynamic`, `optimize`, `nms` , `batch`, `device`
ONNX | `onnx` | `yolo11n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `nms` , `batch`, `device`
OpenVINO | `openvino` | `yolo11n_openvino_model/` | ✅ | `imgsz`, `half`, `dynamic`, `int8`, `nms` , `batch`, `data`, `fraction`, `device`
TensorRT | `engine` | `yolo11n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `int8`, `nms` , `batch`, `data`, `fraction`, `device`
CoreML | `coreml` | `yolo11n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms` , `batch`, `device`
TF SavedModel | `saved_model` | `yolo11n_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `nms` , `batch`, `device`
TF GraphDef | `pb` | `yolo11n.pb` | ❌ | `imgsz`, `batch`, `device`
TF Lite | `tflite` | `yolo11n.tflite` | ✅ | `imgsz`, `half`, `int8`, `nms` , `batch`, `data`, `fraction`, `device`
TF Edge TPU | `edgetpu` | `yolo11n_edgetpu.tflite` | ✅ | `imgsz`, `device`
TF.js | `tfjs` | `yolo11n_web_model/` | ✅ | `imgsz`, `half`, `int8`, `nms` , `batch`, `device`
PaddlePaddle | `paddle` | `yolo11n_paddle_model/` | ✅ | `imgsz`, `batch`, `device`
MNN | `mnn` | `yolo11n.mnn` | ✅ | `imgsz`, `batch`, `int8`, `half`, `device`
NCNN | `ncnn` | `yolo11n_ncnn_model/` | ✅ | `imgsz`, `half`, `batch`, `device`
IMX500 | `imx` | `yolo11n_imx_model/` | ✅ | `imgsz`, `int8`, `data`, `fraction`, `device`
RKNN | `rknn` | `yolo11n_rknn_model/` | ✅ | `imgsz`, `batch`, `name`, `device`

See full `export` details in the Export page.

## FAQ

### How do I train a YOLO11 model on my custom dataset?

Training a YOLO11 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 CLI

    from ultralytics import YOLO

    # Load a pretrained model
    model = YOLO("yolo11n.pt")

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

    yolo detect train data=my_custom_dataset.yaml model=yolo11n.pt epochs=100 imgsz=640

For detailed configuration options, visit the Configuration page.

### What pretrained models are available in YOLO11?

Ultralytics YOLO11 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:

- YOLO11n
- YOLO11s
- YOLO11m
- YOLO11l
- YOLO11x

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

### How can I validate the accuracy of my trained YOLO model?

To validate the accuracy of your trained YOLO11 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 CLI

    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

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

For more validation details, visit the Val page.

### What formats can I export a YOLO11 model to?

Ultralytics YOLO11 allows exporting models to various formats such as ONNX, TensorRT, CoreML, and more to ensure compatibility across different platforms and devices.

Example

Python CLI

    from ultralytics import YOLO

    # Load the model
    model = YOLO("yolo11n.pt")

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

    yolo export model=yolo11n.pt format=onnx

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

### Why should I use Ultralytics YOLO11 for object detection?

Ultralytics YOLO11 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 YOLO11 in action.

📅 Created 1 year ago ✏️ Updated 6 months ago

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