14 KiB

Raw Blame History

comments	description	keywords
true	Master image classification using YOLOv8. Learn to train, validate, predict, and export models efficiently.	YOLOv8, image classification, AI, machine learning, pretrained models, ImageNet, model export, predict, train, validate

Image Classification

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.

Watch: Explore Ultralytics YOLO Tasks: Image Classification using Ultralytics HUB

!!! 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/cfg/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	69.0	88.3	12.9	0.31	2.7	4.3
YOLOv8s-cls	224	73.8	91.7	23.4	0.35	6.4	13.5
YOLOv8m-cls	224	76.8	93.5	85.4	0.62	17.0	42.7
YOLOv8l-cls	224	76.8	93.5	163.0	0.87	37.5	99.7
YOLOv8x-cls	224	79.0	94.6	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
    results = 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

YOLO classification dataset format can be found in detail in the Dataset Guide.

Val

Validate trained YOLOv8n-cls model accuracy on the MNIST160 dataset. No argument need to passed as the model retains its 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 model

    # 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 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=yolov8n-cls.onnx. Usage examples are shown for your model after export completes.

Format	`format` Argument	Model	Metadata	Arguments
PyTorch	-	`yolov8n-cls.pt`	✅	-
TorchScript	`torchscript`	`yolov8n-cls.torchscript`	✅	`imgsz`, `optimize`, `batch`
ONNX	`onnx`	`yolov8n-cls.onnx`	✅	`imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch`
OpenVINO	`openvino`	`yolov8n-cls_openvino_model/`	✅	`imgsz`, `half`, `int8`, `batch`
TensorRT	`engine`	`yolov8n-cls.engine`	✅	`imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `int8`, `batch`
CoreML	`coreml`	`yolov8n-cls.mlpackage`	✅	`imgsz`, `half`, `int8`, `nms`, `batch`
TF SavedModel	`saved_model`	`yolov8n-cls_saved_model/`	✅	`imgsz`, `keras`, `int8`, `batch`
TF GraphDef	`pb`	`yolov8n-cls.pb`	❌	`imgsz`, `batch`
TF Lite	`tflite`	`yolov8n-cls.tflite`	✅	`imgsz`, `half`, `int8`, `batch`
TF Edge TPU	`edgetpu`	`yolov8n-cls_edgetpu.tflite`	✅	`imgsz`
TF.js	`tfjs`	`yolov8n-cls_web_model/`	✅	`imgsz`, `half`, `int8`, `batch`
PaddlePaddle	`paddle`	`yolov8n-cls_paddle_model/`	✅	`imgsz`, `batch`
NCNN	`ncnn`	`yolov8n-cls_ncnn_model/`	✅	`imgsz`, `half`, `batch`

See full export details in the Export page.

FAQ

What is the purpose of YOLOv8 in image classification?

YOLOv8 models, such as yolov8n-cls.pt, are designed for efficient image classification. They assign a single class label to an entire image along with a confidence score. This is particularly useful for applications where knowing the specific class of an image is sufficient, rather than identifying the location or shape of objects within the image.

How do I train a YOLOv8 model for image classification?

To train a YOLOv8 model, you can use either Python or CLI commands. For example, to train a yolov8n-cls model on the MNIST160 dataset for 100 epochs at an image size of 64:

!!! Example

=== "Python"

    ```python
    from ultralytics import YOLO

    # Load a model
    model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)

    # Train the model
    results = model.train(data="mnist160", epochs=100, imgsz=64)
    ```

=== "CLI"

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

For more configuration options, visit the Configuration page.

Where can I find pretrained YOLOv8 classification models?

Pretrained YOLOv8 classification models can be found in the Models section. Models like yolov8n-cls.pt, yolov8s-cls.pt, yolov8m-cls.pt, etc., are pretrained on the ImageNet dataset and can be easily downloaded and used for various image classification tasks.

How can I export a trained YOLOv8 model to different formats?

You can export a trained YOLOv8 model to various formats using Python or CLI commands. For instance, to export a model to ONNX format:

!!! Example

=== "Python"

    ```python
    from ultralytics import YOLO

    # Load a model
    model = YOLO("yolov8n-cls.pt")  # load the trained model

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

=== "CLI"

    ```bash
    yolo export model=yolov8n-cls.pt format=onnx  # export the trained model to ONNX format
    ```

For detailed export options, refer to the Export page.

How do I validate a trained YOLOv8 classification model?

To validate a trained model's accuracy on a dataset like MNIST160, you can use the following Python or CLI commands:

!!! Example

=== "Python"

    ```python
    from ultralytics import YOLO

    # Load a model
    model = YOLO("yolov8n-cls.pt")  # load the trained model

    # Validate the model
    metrics = model.val()  # no arguments needed, uses the dataset and settings from training
    metrics.top1  # top1 accuracy
    metrics.top5  # top5 accuracy
    ```

=== "CLI"

    ```bash
    yolo classify val model=yolov8n-cls.pt  # validate the trained model
    ```

For more information, visit the Validate section.

14 KiB Raw Blame History

Image Classification

Models

Train

Dataset format

Val

Predict

Export

FAQ

What is the purpose of YOLOv8 in image classification?

How do I train a YOLOv8 model for image classification?

Where can I find pretrained YOLOv8 classification models?

How can I export a trained YOLOv8 model to different formats?

How do I validate a trained YOLOv8 classification model?

14 KiB

Raw Blame History