331 lines
14 KiB
Markdown
331 lines
14 KiB
Markdown
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---
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comments: true
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description: Master image classification using YOLO11. Learn to train, validate, predict, and export models efficiently.
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keywords: YOLO11, image classification, AI, machine learning, pretrained models, ImageNet, model export, predict, train, validate
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model_name: yolo11n-cls
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---
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# Image Classification
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<img width="1024" src="https://github.com/ultralytics/docs/releases/download/0/image-classification-examples.avif" alt="Image classification examples">
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[Image classification](https://www.ultralytics.com/glossary/image-classification) is the simplest of the three tasks and involves classifying an entire image into one of a set of predefined classes.
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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.
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<p align="center">
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<br>
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<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/5BO0Il_YYAg"
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title="YouTube video player" frameborder="0"
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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allowfullscreen>
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</iframe>
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<br>
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<strong>Watch:</strong> Explore Ultralytics YOLO Tasks: Image Classification using Ultralytics HUB
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</p>
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!!! tip
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YOLO11 Classify models use the `-cls` suffix, i.e. `yolo11n-cls.pt` and are pretrained on [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/ImageNet.yaml).
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## [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/11)
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YOLO11 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.
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[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.
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{% include "macros/yolo-cls-perf.md" %}
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- **acc** values are model accuracies on the [ImageNet](https://www.image-net.org/) dataset validation set. <br>Reproduce by `yolo val classify data=path/to/ImageNet device=0`
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- **Speed** averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu`
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## Train
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Train YOLO11n-cls on the MNIST160 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) at image size 64. For a full list of available arguments see the [Configuration](../usage/cfg.md) page.
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!!! example
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolo11n-cls.yaml") # build a new model from YAML
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model = YOLO("yolo11n-cls.pt") # load a pretrained model (recommended for training)
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model = YOLO("yolo11n-cls.yaml").load("yolo11n-cls.pt") # build from YAML and transfer weights
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# Train the model
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results = model.train(data="mnist160", epochs=100, imgsz=64)
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```
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=== "CLI"
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```bash
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# Build a new model from YAML and start training from scratch
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yolo classify train data=mnist160 model=yolo11n-cls.yaml epochs=100 imgsz=64
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# Start training from a pretrained *.pt model
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yolo classify train data=mnist160 model=yolo11n-cls.pt epochs=100 imgsz=64
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# Build a new model from YAML, transfer pretrained weights to it and start training
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yolo classify train data=mnist160 model=yolo11n-cls.yaml pretrained=yolo11n-cls.pt epochs=100 imgsz=64
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```
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!!! tip
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Ultralytics YOLO classification uses [`torchvision.transforms.RandomResizedCrop`](https://pytorch.org/vision/stable/generated/torchvision.transforms.RandomResizedCrop.html) for training and [`torchvision.transforms.CenterCrop`](https://pytorch.org/vision/stable/generated/torchvision.transforms.CenterCrop.html) for validation and inference.
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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.
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To preserve the full image while maintaining its proportions, consider using [`torchvision.transforms.Resize`](https://docs.pytorch.org/vision/stable/generated/torchvision.transforms.Resize.html) instead of cropping transforms.
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You can implement this by customizing your augmentation pipeline through a custom `ClassificationDataset` and `ClassificationTrainer`.
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```python
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import torch
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import torchvision.transforms as T
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from ultralytics import YOLO
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from ultralytics.data.dataset import ClassificationDataset
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from ultralytics.models.yolo.classify import ClassificationTrainer
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class CustomizedDataset(ClassificationDataset):
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"""A customized dataset class for image classification with enhanced data augmentation transforms."""
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def __init__(self, root: str, args, augment: bool = False, prefix: str = ""):
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"""Initialize a customized classification dataset with enhanced data augmentation transforms."""
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super().__init__(root, args, augment, prefix)
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# Add your custom training transforms here
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train_transforms = T.Compose(
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[
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T.Resize((args.imgsz, args.imgsz)),
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T.RandomHorizontalFlip(p=args.fliplr),
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T.RandomVerticalFlip(p=args.flipud),
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T.RandAugment(interpolation=T.InterpolationMode.BILINEAR),
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T.ColorJitter(brightness=args.hsv_v, contrast=args.hsv_v, saturation=args.hsv_s, hue=args.hsv_h),
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T.ToTensor(),
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T.Normalize(mean=torch.tensor(0), std=torch.tensor(1)),
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T.RandomErasing(p=args.erasing, inplace=True),
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]
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)
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# Add your custom validation transforms here
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val_transforms = T.Compose(
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[
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T.Resize((args.imgsz, args.imgsz)),
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T.ToTensor(),
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T.Normalize(mean=torch.tensor(0), std=torch.tensor(1)),
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]
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)
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self.torch_transforms = train_transforms if augment else val_transforms
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class CustomizedTrainer(ClassificationTrainer):
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"""A customized trainer class for YOLO classification models with enhanced dataset handling."""
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def build_dataset(self, img_path: str, mode: str = "train", batch=None):
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"""Build a customized dataset for classification training and the validation during training."""
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return CustomizedDataset(root=img_path, args=self.args, augment=mode == "train", prefix=mode)
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class CustomizedValidator(ClassificationValidator):
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"""A customized validator class for YOLO classification models with enhanced dataset handling."""
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def build_dataset(self, img_path: str, mode: str = "train"):
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"""Build a customized dataset for classification standalone validation."""
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return CustomizedDataset(root=img_path, args=self.args, augment=mode == "train", prefix=self.args.split)
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model = YOLO("yolo11n-cls.pt")
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model.train(data="imagenet1000", trainer=CustomizedTrainer, epochs=10, imgsz=224, batch=64)
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model.val(data="imagenet1000", validator=CustomizedValidator, imgsz=224, batch=64)
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```
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### Dataset format
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YOLO classification dataset format can be found in detail in the [Dataset Guide](../datasets/classify/index.md).
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## Val
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Validate trained YOLO11n-cls model [accuracy](https://www.ultralytics.com/glossary/accuracy) on the MNIST160 dataset. No arguments are needed as the `model` retains its training `data` and arguments as model attributes.
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!!! example
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolo11n-cls.pt") # load an official model
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model = YOLO("path/to/best.pt") # load a custom model
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# Validate the model
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metrics = model.val() # no arguments needed, dataset and settings remembered
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metrics.top1 # top1 accuracy
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metrics.top5 # top5 accuracy
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```
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=== "CLI"
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```bash
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yolo classify val model=yolo11n-cls.pt # val official model
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yolo classify val model=path/to/best.pt # val custom model
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```
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!!! tip
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As mentioned in the [training section](#train), 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](#train) for implementation details.
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## Predict
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Use a trained YOLO11n-cls model to run predictions on images.
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!!! example
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolo11n-cls.pt") # load an official model
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model = YOLO("path/to/best.pt") # load a custom model
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# Predict with the model
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results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
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```
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=== "CLI"
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```bash
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yolo classify predict model=yolo11n-cls.pt source='https://ultralytics.com/images/bus.jpg' # predict with official model
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yolo classify predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg' # predict with custom model
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```
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See full `predict` mode details in the [Predict](../modes/predict.md) page.
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## Export
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Export a YOLO11n-cls model to a different format like ONNX, CoreML, etc.
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!!! example
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolo11n-cls.pt") # load an official model
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model = YOLO("path/to/best.pt") # load a custom trained model
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# Export the model
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model.export(format="onnx")
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```
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=== "CLI"
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```bash
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yolo export model=yolo11n-cls.pt format=onnx # export official model
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yolo export model=path/to/best.pt format=onnx # export custom trained model
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```
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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.
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{% include "macros/export-table.md" %}
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See full `export` details in the [Export](../modes/export.md) page.
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## FAQ
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### What is the purpose of YOLO11 in image classification?
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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.
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### How do I train a YOLO11 model for image classification?
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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:
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!!! example
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolo11n-cls.pt") # load a pretrained model (recommended for training)
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# Train the model
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results = model.train(data="mnist160", epochs=100, imgsz=64)
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```
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=== "CLI"
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```bash
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yolo classify train data=mnist160 model=yolo11n-cls.pt epochs=100 imgsz=64
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```
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For more configuration options, visit the [Configuration](../usage/cfg.md) page.
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### Where can I find pretrained YOLO11 classification models?
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Pretrained YOLO11 classification models can be found in the [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/11) section. Models like `yolo11n-cls.pt`, `yolo11s-cls.pt`, `yolo11m-cls.pt`, etc., are pretrained on the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/ImageNet.yaml) dataset and can be easily downloaded and used for various image classification tasks.
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### How can I export a trained YOLO11 model to different formats?
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You can export a trained YOLO11 model to various formats using Python or CLI commands. For instance, to export a model to ONNX format:
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!!! example
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolo11n-cls.pt") # load the trained model
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# Export the model to ONNX
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model.export(format="onnx")
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```
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=== "CLI"
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```bash
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yolo export model=yolo11n-cls.pt format=onnx # export the trained model to ONNX format
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```
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For detailed export options, refer to the [Export](../modes/export.md) page.
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### How do I validate a trained YOLO11 classification model?
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To validate a trained model's accuracy on a dataset like MNIST160, you can use the following Python or CLI commands:
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!!! example
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolo11n-cls.pt") # load the trained model
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# Validate the model
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metrics = model.val() # no arguments needed, uses the dataset and settings from training
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metrics.top1 # top1 accuracy
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metrics.top5 # top5 accuracy
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```
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=== "CLI"
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```bash
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yolo classify val model=yolo11n-cls.pt # validate the trained model
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```
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For more information, visit the [Validate](#val) section.
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