Classify

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

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

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.

PythonCLI

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)

# 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

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.

PythonCLI

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

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.

PythonCLI

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

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.

PythonCLI

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')

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/`	✅