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171 lines
9.3 KiB
171 lines
9.3 KiB
Image classification is the simplest of the three tasks and involves classifying an entire image into one of a set of
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predefined classes.
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<img width="1024" src="https://user-images.githubusercontent.com/26833433/212094133-6bb8c21c-3d47-41df-a512-81c5931054ae.png">
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The output of an image classifier is a single class label and a confidence score. Image
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classification is useful when you need to know only what class an image belongs to and don't need to know where objects
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of that class are located or what their exact shape is.
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!!! tip "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/datasets/ImageNet.yaml).
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## [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/v8)
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YOLOv8 pretrained Classify models are shown here. Detect, Segment and Pose models are pretrained on
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the [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco.yaml) dataset, while Classify
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models are pretrained on
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the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/ImageNet.yaml) dataset.
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[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest
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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/v0.0.0/yolov8n-cls.pt) | 224 | 66.6 | 87.0 | 12.9 | 0.31 | 2.7 | 4.3 |
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| [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 |
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| [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 |
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| [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 |
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| [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 |
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- **acc** values are model accuracies on the [ImageNet](https://www.image-net.org/) dataset validation set.
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<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/)
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instance.
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<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
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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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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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## Val
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Validate trained YOLOv8n-cls model accuracy on the MNIST160 dataset. No argument need to passed as the `model` retains
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it's 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](https://docs.ultralytics.com/modes/predict/) 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
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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 predict or validate directly on exported models,
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i.e. `yolo predict model=yolov8n-cls.onnx`. Usage examples are shown for your model after export completes.
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| Format | `format` Argument | Model | Metadata |
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|--------------------------------------------------------------------|-------------------|-------------------------------|----------|
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| [PyTorch](https://pytorch.org/) | - | `yolov8n-cls.pt` | ✅ |
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| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n-cls.torchscript` | ✅ |
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| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n-cls.onnx` | ✅ |
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| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n-cls_openvino_model/` | ✅ |
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| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n-cls.engine` | ✅ |
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| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n-cls.mlmodel` | ✅ |
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| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n-cls_saved_model/` | ✅ |
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| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n-cls.pb` | ❌ |
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| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n-cls.tflite` | ✅ |
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| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n-cls_edgetpu.tflite` | ✅ |
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| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-cls_web_model/` | ✅ |
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| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-cls_paddle_model/` | ✅ |
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See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page.
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