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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.

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.

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)

# 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 it's training data and arguments as model attributes.

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.

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

See full predict mode details in the Predict page.

Export

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

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. 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
ONNX onnx yolov8n-cls.onnx ✅ imgsz, half, dynamic, simplify, opset
OpenVINO openvino yolov8n-cls_openvino_model/ ✅ imgsz, half
TensorRT engine yolov8n-cls.engine ✅ imgsz, half, dynamic, simplify, workspace
CoreML coreml yolov8n-cls.mlpackage ✅ imgsz, half, int8, nms
TF SavedModel saved_model yolov8n-cls_saved_model/ ✅ imgsz, keras
TF GraphDef pb yolov8n-cls.pb ❌ imgsz
TF Lite tflite yolov8n-cls.tflite ✅ imgsz, half, int8
TF Edge TPU edgetpu yolov8n-cls_edgetpu.tflite ✅ imgsz
TF.js tfjs yolov8n-cls_web_model/ ✅ imgsz
PaddlePaddle paddle yolov8n-cls_paddle_model/ ✅ imgsz
ncnn ncnn yolov8n-cls_ncnn_model/ ✅ imgsz, half

See full export details in the Export page.


Created 2023-03-12, Updated 2023-09-13
Authors: glenn-jocher (22), Laughing-q (1), sergiuwaxmann (1), AyushExel (1)

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