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261 lines
11 KiB
261 lines
11 KiB
--- |
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comments: true |
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description: Master image classification using YOLOv8. Learn to train, validate, predict, and export models efficiently. |
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keywords: YOLOv8, image classification, AI, machine learning, pretrained models, ImageNet, model export, predict, train, validate |
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model_name: yolov8n-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 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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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). |
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## [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/v8) |
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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. |
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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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| Model | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) at 640 | |
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| -------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ | |
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| [YOLOv8n-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-cls.pt) | 224 | 69.0 | 88.3 | 12.9 | 0.31 | 2.7 | 4.3 | |
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| [YOLOv8s-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-cls.pt) | 224 | 73.8 | 91.7 | 23.4 | 0.35 | 6.4 | 13.5 | |
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| [YOLOv8m-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-cls.pt) | 224 | 76.8 | 93.5 | 85.4 | 0.62 | 17.0 | 42.7 | |
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| [YOLOv8l-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-cls.pt) | 224 | 76.8 | 93.5 | 163.0 | 0.87 | 37.5 | 99.7 | |
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| [YOLOv8x-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-cls.pt) | 224 | 79.0 | 94.6 | 232.0 | 1.01 | 57.4 | 154.8 | |
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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 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. |
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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("yolov8n-cls.yaml") # build a new model from YAML |
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model = YOLO("yolov8n-cls.pt") # load a pretrained model (recommended for training) |
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model = YOLO("yolov8n-cls.yaml").load("yolov8n-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=yolov8n-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=yolov8n-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=yolov8n-cls.yaml pretrained=yolov8n-cls.pt epochs=100 imgsz=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 YOLOv8n-cls model 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("yolov8n-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=yolov8n-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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## Predict |
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Use a trained YOLOv8n-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("yolov8n-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=yolov8n-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 YOLOv8n-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("yolov8n-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=yolov8n-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 YOLOv8-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=yolov8n-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 YOLOv8 in image classification? |
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YOLOv8 models, such as `yolov8n-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 YOLOv8 model for image classification? |
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To train a YOLOv8 model, you can use either Python or CLI commands. For example, to train a `yolov8n-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("yolov8n-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=yolov8n-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 YOLOv8 classification models? |
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Pretrained YOLOv8 classification models can be found in the [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/v8) section. Models like `yolov8n-cls.pt`, `yolov8s-cls.pt`, `yolov8m-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 YOLOv8 model to different formats? |
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You can export a trained YOLOv8 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("yolov8n-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=yolov8n-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 YOLOv8 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("yolov8n-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=yolov8n-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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