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180 lines
10 KiB
180 lines
10 KiB
--- |
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comments: true |
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description: Check YOLO class label with only one class for the whole image, using image classification. Get strategies for training and validation models. |
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keywords: YOLOv8n-cls, image classification, pretrained models |
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--- |
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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 |
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predefined classes. |
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<img width="1024" src="https://user-images.githubusercontent.com/26833433/243418606-adf35c62-2e11-405d-84c6-b84e7d013804.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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### 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 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 | Arguments | |
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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` | ✅ | `imgsz`, `optimize` | |
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| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n-cls.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset` | |
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| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n-cls_openvino_model/` | ✅ | `imgsz`, `half` | |
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| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n-cls.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` | |
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| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n-cls.mlmodel` | ✅ | `imgsz`, `half`, `int8`, `nms` | |
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| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n-cls_saved_model/` | ✅ | `imgsz`, `keras` | |
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| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n-cls.pb` | ❌ | `imgsz` | |
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| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n-cls.tflite` | ✅ | `imgsz`, `half`, `int8` | |
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| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n-cls_edgetpu.tflite` | ✅ | `imgsz` | |
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| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-cls_web_model/` | ✅ | `imgsz` | |
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| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-cls_paddle_model/` | ✅ | `imgsz` | |
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See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page. |