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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.

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

Tip

YOLO11 Classify models use the `-cls` suffix, i.e. `yolo11n-cls.pt` and are pretrained on ImageNet.

## Models

YOLO11 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 T4 TensorRT10 (ms) | params (M) | FLOPs (B) at 224
--- | --- | --- | --- | --- | --- | --- | ---
YOLO11n-cls | 224 | 70.0 | 89.4 | 5.0 ± 0.3 | 1.1 ± 0.0 | 1.6 | 0.5
YOLO11s-cls | 224 | 75.4 | 92.7 | 7.9 ± 0.2 | 1.3 ± 0.0 | 5.5 | 1.6
YOLO11m-cls | 224 | 77.3 | 93.9 | 17.2 ± 0.4 | 2.0 ± 0.0 | 10.4 | 5.0
YOLO11l-cls | 224 | 78.3 | 94.3 | 23.2 ± 0.3 | 2.8 ± 0.0 | 12.9 | 6.2
YOLO11x-cls | 224 | 79.5 | 94.9 | 41.4 ± 0.9 | 3.8 ± 0.0 | 28.4 | 13.7

- 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 YOLO11n-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 CLI

    from ultralytics import YOLO

    # Load a model
    model = YOLO("yolo11n-cls.yaml")  # build a new model from YAML
    model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)
    model = YOLO("yolo11n-cls.yaml").load("yolo11n-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=yolo11n-cls.yaml epochs=100 imgsz=64

    # Start training from a pretrained *.pt model
    yolo classify train data=mnist160 model=yolo11n-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=yolo11n-cls.yaml pretrained=yolo11n-cls.pt epochs=100 imgsz=64

Tip

Ultralytics YOLO classification uses `torchvision.transforms.RandomResizedCrop` for training and `torchvision.transforms.CenterCrop` for validation and inference. These cropping-based transforms assume square inputs and may inadvertently crop out important regions from images with extreme aspect ratios, potentially causing loss of critical visual information during training. To preserve the full image while maintaining its proportions, consider using `torchvision.transforms.Resize` instead of cropping transforms.

You can implement this by customizing your augmentation pipeline through a custom `ClassificationDataset` and `ClassificationTrainer`.

    import torch
    import torchvision.transforms as T

    from ultralytics import YOLO
    from ultralytics.data.dataset import ClassificationDataset
    from ultralytics.models.yolo.classify import ClassificationTrainer, ClassificationValidator

    class CustomizedDataset(ClassificationDataset):
        """A customized dataset class for image classification with enhanced data augmentation transforms."""

        def __init__(self, root: str, args, augment: bool = False, prefix: str = ""):
            """Initialize a customized classification dataset with enhanced data augmentation transforms."""
            super().__init__(root, args, augment, prefix)

            # Add your custom training transforms here
            train_transforms = T.Compose(
                [
                    T.Resize((args.imgsz, args.imgsz)),
                    T.RandomHorizontalFlip(p=args.fliplr),
                    T.RandomVerticalFlip(p=args.flipud),
                    T.RandAugment(interpolation=T.InterpolationMode.BILINEAR),
                    T.ColorJitter(brightness=args.hsv_v, contrast=args.hsv_v, saturation=args.hsv_s, hue=args.hsv_h),
                    T.ToTensor(),
                    T.Normalize(mean=torch.tensor(0), std=torch.tensor(1)),
                    T.RandomErasing(p=args.erasing, inplace=True),
                ]
            )

            # Add your custom validation transforms here
            val_transforms = T.Compose(
                [
                    T.Resize((args.imgsz, args.imgsz)),
                    T.ToTensor(),
                    T.Normalize(mean=torch.tensor(0), std=torch.tensor(1)),
                ]
            )
            self.torch_transforms = train_transforms if augment else val_transforms

    class CustomizedTrainer(ClassificationTrainer):
        """A customized trainer class for YOLO classification models with enhanced dataset handling."""

        def build_dataset(self, img_path: str, mode: str = "train", batch=None):
            """Build a customized dataset for classification training and the validation during training."""
            return CustomizedDataset(root=img_path, args=self.args, augment=mode == "train", prefix=mode)

    class CustomizedValidator(ClassificationValidator):
        """A customized validator class for YOLO classification models with enhanced dataset handling."""

        def build_dataset(self, img_path: str, mode: str = "train"):
            """Build a customized dataset for classification standalone validation."""
            return CustomizedDataset(root=img_path, args=self.args, augment=mode == "train", prefix=self.args.split)

    model = YOLO("yolo11n-cls.pt")
    model.train(data="imagenet1000", trainer=CustomizedTrainer, epochs=10, imgsz=224, batch=64)
    model.val(data="imagenet1000", validator=CustomizedValidator, imgsz=224, batch=64)

### Dataset format

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

## Val

Validate trained YOLO11n-cls model accuracy on the MNIST160 dataset. No arguments are needed as the `model` retains its training `data` and arguments as model attributes.

Example

Python CLI

    from ultralytics import YOLO

    # Load a model
    model = YOLO("yolo11n-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=yolo11n-cls.pt  # val official model
    yolo classify val model=path/to/best.pt # val custom model

Tip

As mentioned in the training section, you can handle extreme aspect ratios during training by using a custom `ClassificationTrainer`. You need to apply the same approach for consistent validation results by implementing a custom `ClassificationValidator` when calling the `val()` method. Refer to the complete code example in the training section for implementation details.

## Predict

Use a trained YOLO11n-cls model to run predictions on images.

Example

Python CLI

    from ultralytics import YOLO

    # Load a model
    model = YOLO("yolo11n-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=yolo11n-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 YOLO11n-cls model to a different format like ONNX, CoreML, etc.

Example

Python CLI

    from ultralytics import YOLO

    # Load a model
    model = YOLO("yolo11n-cls.pt")  # load an official model
    model = YOLO("path/to/best.pt")  # load a custom trained model

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

    yolo export model=yolo11n-cls.pt format=onnx  # export official model
    yolo export model=path/to/best.pt format=onnx # export custom trained model

Available YOLO11-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=yolo11n-cls.onnx`. Usage examples are shown for your model after export completes.

Format | `format` Argument | Model | Metadata | Arguments
--- | --- | --- | --- | ---
PyTorch | - | `yolo11n-cls.pt` | ✅ | -
TorchScript | `torchscript` | `yolo11n-cls.torchscript` | ✅ | `imgsz`, `half`, `dynamic`, `optimize`, `nms` , `batch`, `device`
ONNX | `onnx` | `yolo11n-cls.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `nms` , `batch`, `device`
OpenVINO | `openvino` | `yolo11n-cls_openvino_model/` | ✅ | `imgsz`, `half`, `dynamic`, `int8`, `nms` , `batch`, `data`, `fraction`, `device`
TensorRT | `engine` | `yolo11n-cls.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `int8`, `nms` , `batch`, `data`, `fraction`, `device`
CoreML | `coreml` | `yolo11n-cls.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms` , `batch`, `device`
TF SavedModel | `saved_model` | `yolo11n-cls_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `nms` , `batch`, `device`
TF GraphDef | `pb` | `yolo11n-cls.pb` | ❌ | `imgsz`, `batch`, `device`
TF Lite | `tflite` | `yolo11n-cls.tflite` | ✅ | `imgsz`, `half`, `int8`, `nms` , `batch`, `data`, `fraction`, `device`
TF Edge TPU | `edgetpu` | `yolo11n-cls_edgetpu.tflite` | ✅ | `imgsz`, `device`
TF.js | `tfjs` | `yolo11n-cls_web_model/` | ✅ | `imgsz`, `half`, `int8`, `nms` , `batch`, `device`
PaddlePaddle | `paddle` | `yolo11n-cls_paddle_model/` | ✅ | `imgsz`, `batch`, `device`
MNN | `mnn` | `yolo11n-cls.mnn` | ✅ | `imgsz`, `batch`, `int8`, `half`, `device`
NCNN | `ncnn` | `yolo11n-cls_ncnn_model/` | ✅ | `imgsz`, `half`, `batch`, `device`
IMX500 | `imx` | `yolo11n-cls_imx_model/` | ✅ | `imgsz`, `int8`, `data`, `fraction`, `device`
RKNN | `rknn` | `yolo11n-cls_rknn_model/` | ✅ | `imgsz`, `batch`, `name`, `device`

See full `export` details in the Export page.

## FAQ

### What is the purpose of YOLO11 in image classification?

YOLO11 models, such as `yolo11n-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 YOLO11 model for image classification?

To train a YOLO11 model, you can use either Python or CLI commands. For example, to train a `yolo11n-cls` model on the MNIST160 dataset for 100 epochs at an image size of 64:

Example

Python CLI

    from ultralytics import YOLO

    # Load a model
    model = YOLO("yolo11n-cls.pt")  # load a pretrained model (recommended for training)

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

    yolo classify train data=mnist160 model=yolo11n-cls.pt epochs=100 imgsz=64

For more configuration options, visit the Configuration page.

### Where can I find pretrained YOLO11 classification models?

Pretrained YOLO11 classification models can be found in the Models section. Models like `yolo11n-cls.pt`, `yolo11s-cls.pt`, `yolo11m-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 YOLO11 model to different formats?

You can export a trained YOLO11 model to various formats using Python or CLI commands. For instance, to export a model to ONNX format:

Example

Python CLI

    from ultralytics import YOLO

    # Load a model
    model = YOLO("yolo11n-cls.pt")  # load the trained model

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

    yolo export model=yolo11n-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 YOLO11 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 CLI

    from ultralytics import YOLO

    # Load a model
    model = YOLO("yolo11n-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

    yolo classify val model=yolo11n-cls.pt # validate the trained model

For more information, visit the Validate section.

📅 Created 1 year ago ✏️ Updated 25 days ago

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