Connect to our Discord channel
| comments | description | keywords | model_name |
|---|---|---|---|
| true | Master image classification using YOLO11. Learn to train, validate, predict, and export models efficiently. | YOLO11, image classification, AI, machine learning, pretrained models, ImageNet, model export, predict, train, validate | yolo11n-cls |
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
Models
Annotations
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).
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.
{% include "macros/yolo-cls-perf.md" %}
yolo val classify data=path/to/ImageNet device=0yolo val classify data=path/to/ImageNet batch=1 device=0|cpuTrain 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"
```python
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)
```
=== "CLI"
```bash
# 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`](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.
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`](https://docs.pytorch.org/vision/stable/generated/torchvision.transforms.Resize.html) instead of cropping transforms.
You can implement this by customizing your augmentation pipeline through a custom `ClassificationDataset` and `ClassificationTrainer`.
```python
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)
```
YOLO classification dataset format can be found in detail in the Dataset Guide.
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"
```python
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
```
=== "CLI"
```bash
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](#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.
Use a trained YOLO11n-cls model to run predictions on images.
!!! example
=== "Python"
```python
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
```
=== "CLI"
```bash
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 a YOLO11n-cls model to a different format like ONNX, CoreML, etc.
!!! example
=== "Python"
```python
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")
```
=== "CLI"
```bash
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.
{% include "macros/export-table.md" %}
See full export details in the Export page.
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.
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"
```python
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)
```
=== "CLI"
```bash
yolo classify train data=mnist160 model=yolo11n-cls.pt epochs=100 imgsz=64
```
For more configuration options, visit the Configuration page.
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.
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"
```python
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")
```
=== "CLI"
```bash
yolo export model=yolo11n-cls.pt format=onnx # export the trained model to ONNX format
```
For detailed export options, refer to the Export page.
To validate a trained model's accuracy on a dataset like MNIST160, you can use the following Python or CLI commands:
!!! example
=== "Python"
```python
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
```
=== "CLI"
```bash
yolo classify val model=yolo11n-cls.pt # validate the trained model
```
For more information, visit the Validate section.
Press p or to see the previous file or, n or to see the next file
Are you sure you want to delete this access key?
Are you sure you want to delete this access key?
Are you sure you want to delete this access key?
Are you sure you want to delete this access key?