YOLOv8-16bit/docs/tasks/classify.md

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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 Classify models use the `-cls` suffix, i.e. `yolov8n-cls.pt` and are pretrained on [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/ImageNet.yaml).

Models

YOLOv8 pretrained Classify 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)	acc top1	acc top5	Speed CPU ONNX (ms)	Speed A100 TensorRT (ms)	params (M)	FLOPs (B) at 640
YOLOv8n-cls	224	66.6	87.0	12.9	0.31	2.7	4.3
YOLOv8s-cls	224	72.3	91.1	23.4	0.35	6.4	13.5
YOLOv8m-cls	224	76.4	93.2	85.4	0.62	17.0	42.7
YOLOv8l-cls	224	78.0	94.1	163.0	0.87	37.5	99.7
YOLOv8x-cls	224	78.4	94.3	232.0	1.01	57.4	154.8

• acc values are model accuracies on the ImageNet dataset validation set.
  Reproduce by yolo val classify data=path/to/ImageNet device=0
• Speed averaged over ImageNet val images using an Amazon EC2 P4d instance.
  Reproduce by yolo val classify data=path/to/ImageNet batch=1 device=0|cpu

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 YAML
    model = YOLO('yolov8n-cls.pt')  # load a pretrained model (recommended for training)
    model = YOLO('yolov8n-cls.yaml').load('yolov8n-cls.pt')  # build from YAML and transfer weights
    
    # Train the model
    model.train(data='mnist160', epochs=100, imgsz=64)
    ```

=== "CLI"

    ```bash
    # Build a new model from YAML and start training from scratch
    yolo classify train data=mnist160 model=yolov8n-cls.yaml epochs=100 imgsz=64

    # Start training from a pretrained *.pt model
    yolo classify train data=mnist160 model=yolov8n-cls.pt epochs=100 imgsz=64

    # Build a new model from YAML, transfer pretrained weights to it and start training
    yolo classify train data=mnist160 model=yolov8n-cls.yaml pretrained=yolov8n-cls.pt epochs=100 imgsz=64
    ```

Dataset format

The YOLO classification dataset format is same as the torchvision format. Each class of images has its own folder and you have to simply pass the path of the dataset folder, i.e, yolo classify train data="path/to/dataset"

dataset/
├── train/
├──── class1/
├──── class2/
├──── class3/
├──── ...
├── val/
├──── class1/
├──── class2/
├──── class3/
├──── ...

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
    ```

See full predict mode details in the 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. Usage examples are shown for your model after export completes.

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/	✅

See full export details in the Export page.