ultralytics/docs/tasks/classify.md

Image classification is the simplest of the three tasks and involves classifying an entire image into one of a set of predefined classes.

The output of an image classifier is a single class label and a confidence score. Image classification is useful when you need to know only what class an image belongs to and don't need to know where objects of that class are located or what their exact shape is.

!!! tip "Tip"

YOLOv8 _classification_ models use the `-cls` suffix, i.e. `yolov8n-cls.pt` and are pretrained on ImageNet.

Models{.md-button .md-button--primary}

Train

Train YOLOv8n-cls on the MNIST160 dataset for 100 epochs at image size 64. For a full list of available arguments see the Configuration page.

!!! example ""

=== "Python"

    ```python
    from ultralytics import YOLO
    
    # Load a model
    model = YOLO('yolov8n-cls.yaml')  # build a new model from scratch
    model = YOLO('yolov8n-cls.pt')  # load a pretrained model (recommended for training)
    
    # Train the model
    model.train(data='mnist160', epochs=100, imgsz=64)
    ```
=== "CLI"

    ```bash
    yolo classify train data=mnist160 model=yolov8n-cls.pt epochs=100 imgsz=64
    ```

Val

Validate trained YOLOv8n-cls model accuracy on the MNIST160 dataset. No argument need to passed as the model retains it's training data and arguments as model attributes.

!!! example ""

=== "Python"

    ```python
    from ultralytics import YOLO
    
    # Load a model
    model = YOLO('yolov8n-cls.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.top1   # top1 accuracy
    metrics.top5   # top5 accuracy
    ```
=== "CLI"

    ```bash
    yolo classify val model=yolov8n-cls.pt  # val official model
    yolo classify val model=path/to/best.pt  # val custom model
    ```

Predict

Use a trained YOLOv8n-cls model to run predictions on images.

!!! example ""

=== "Python"

    ```python
    from ultralytics import YOLO
    
    # Load a model
    model = YOLO('yolov8n-cls.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 classify predict model=yolov8n-cls.pt source='https://ultralytics.com/images/bus.jpg'  # predict with official model
    yolo classify predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg'  # predict with custom model
    ```

Read more details of predict in our Predict page.

Export

Export a YOLOv8n-cls model to a different format like ONNX, CoreML, etc.

!!! example ""

=== "Python"

    ```python
    from ultralytics import YOLO
    
    # Load a model
    model = YOLO('yolov8n-cls.pt')  # load an official model
    model = YOLO('path/to/best.pt')  # load a custom trained
    
    # Export the model
    model.export(format='onnx')
    ```
=== "CLI"

    ```bash
    yolo export model=yolov8n-cls.pt format=onnx  # export official model
    yolo export model=path/to/best.pt format=onnx  # export custom trained model
    ```

Available YOLOv8-cls export formats are in the table below. You can predict or validate directly on exported models, i.e. yolo predict model=yolov8n-cls.onnx.

Format	format Argument	Model	Metadata
PyTorch	-	yolov8n-cls.pt	✅
TorchScript	torchscript	yolov8n-cls.torchscript	✅
ONNX	onnx	yolov8n-cls.onnx	✅
OpenVINO	openvino	yolov8n-cls_openvino_model/	✅
TensorRT	engine	yolov8n-cls.engine	✅
CoreML	coreml	yolov8n-cls.mlmodel	✅
TF SavedModel	saved_model	yolov8n-cls_saved_model/	✅
TF GraphDef	pb	yolov8n-cls.pb	❌
TF Lite	tflite	yolov8n-cls.tflite	✅
TF Edge TPU	edgetpu	yolov8n-cls_edgetpu.tflite	✅
TF.js	tfjs	yolov8n-cls_web_model/	✅
PaddlePaddle	paddle	yolov8n-cls_paddle_model/	✅