--- comments: true description: Check YOLO class label with only one class for the whole image, using image classification. Get strategies for training and validation models. keywords: YOLOv8n-cls, image classification, pretrained models --- 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/cfg/datasets/ImageNet.yaml). ## [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/v8) YOLOv8 pretrained Classify models are shown here. Detect, Segment and Pose models are pretrained on the [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml) dataset, while Classify models are pretrained on the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/ImageNet.yaml) dataset. [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) 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](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-cls.pt) | 224 | 66.6 | 87.0 | 12.9 | 0.31 | 2.7 | 4.3 | | [YOLOv8s-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-cls.pt) | 224 | 72.3 | 91.1 | 23.4 | 0.35 | 6.4 | 13.5 | | [YOLOv8m-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-cls.pt) | 224 | 76.4 | 93.2 | 85.4 | 0.62 | 17.0 | 42.7 | | [YOLOv8l-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-cls.pt) | 224 | 78.0 | 94.1 | 163.0 | 0.87 | 37.5 | 99.7 | | [YOLOv8x-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-cls.pt) | 224 | 78.4 | 94.3 | 232.0 | 1.01 | 57.4 | 154.8 | - **acc** values are model accuracies on the [ImageNet](https://www.image-net.org/) 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](https://aws.amazon.com/ec2/instance-types/p4/) 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](../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 ``` ### Dataset format YOLO classification dataset format can be found in detail in the [Dataset Guide](../datasets/classify/index.md). ## 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](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`. Usage examples are shown for your model after export completes. | Format | `format` Argument | Model | Metadata | Arguments | |--------------------------------------------------------------------|-------------------|-------------------------------|----------|-----------------------------------------------------| | [PyTorch](https://pytorch.org/) | - | `yolov8n-cls.pt` | ✅ | - | | [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n-cls.torchscript` | ✅ | `imgsz`, `optimize` | | [ONNX](https://onnx.ai/) | `onnx` | `yolov8n-cls.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset` | | [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n-cls_openvino_model/` | ✅ | `imgsz`, `half` | | [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n-cls.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` | | [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n-cls.mlmodel` | ✅ | `imgsz`, `half`, `int8`, `nms` | | [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n-cls_saved_model/` | ✅ | `imgsz`, `keras` | | [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n-cls.pb` | ❌ | `imgsz` | | [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n-cls.tflite` | ✅ | `imgsz`, `half`, `int8` | | [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n-cls_edgetpu.tflite` | ✅ | `imgsz` | | [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-cls_web_model/` | ✅ | `imgsz` | | [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-cls_paddle_model/` | ✅ | `imgsz` | | [ncnn](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n-cls_ncnn_model/` | ✅ | `imgsz`, `half` | See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page.