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](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/v8){ .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](../usage/cfg.md) 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 ``` ## 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](https://docs.ultralytics.com/modes/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](https://pytorch.org/) | - | `yolov8n-cls.pt` | ✅ | | [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n-cls.torchscript` | ✅ | | [ONNX](https://onnx.ai/) | `onnx` | `yolov8n-cls.onnx` | ✅ | | [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n-cls_openvino_model/` | ✅ | | [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n-cls.engine` | ✅ | | [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n-cls.mlmodel` | ✅ | | [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n-cls_saved_model/` | ✅ | | [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n-cls.pb` | ❌ | | [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n-cls.tflite` | ✅ | | [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n-cls_edgetpu.tflite` | ✅ | | [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-cls_web_model/` | ✅ | | [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-cls_paddle_model/` | ✅ |