You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
181 lines
11 KiB
181 lines
11 KiB
--- |
|
comments: true |
|
description: Learn about YOLOv8 Classify models for image classification. Get detailed information on List of Pretrained Models & how to Train, Validate, Predict & Export models. |
|
keywords: Ultralytics, YOLOv8, Image Classification, Pretrained Models, YOLOv8n-cls, Training, Validation, Prediction, Model Export |
|
--- |
|
|
|
Image classification is the simplest of the three tasks and involves classifying an entire image into one of a set of |
|
predefined classes. |
|
|
|
<img width="1024" src="https://user-images.githubusercontent.com/26833433/243418606-adf35c62-2e11-405d-84c6-b84e7d013804.png"> |
|
|
|
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. |
|
|
|
!!! tip "Tip" |
|
|
|
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). |
|
|
|
## [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/v8) |
|
|
|
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. |
|
|
|
[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. |
|
|
|
| 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 | |
|
|----------------------------------------------------------------------------------------------|-----------------------|------------------|------------------|--------------------------------|-------------------------------------|--------------------|--------------------------| |
|
| [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 | |
|
| [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 | |
|
| [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 | |
|
| [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 | |
|
| [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 | |
|
|
|
- **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` |
|
- **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` |
|
|
|
## Train |
|
|
|
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. |
|
|
|
!!! example "" |
|
|
|
=== "Python" |
|
|
|
```python |
|
from ultralytics import YOLO |
|
|
|
# Load a model |
|
model = YOLO('yolov8n-cls.yaml') # build a new model from YAML |
|
model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training) |
|
model = YOLO('yolov8n-cls.yaml').load('yolov8n-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=yolov8n-cls.yaml epochs=100 imgsz=64 |
|
|
|
# Start training from a pretrained *.pt model |
|
yolo classify train data=mnist160 model=yolov8n-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=yolov8n-cls.yaml pretrained=yolov8n-cls.pt epochs=100 imgsz=64 |
|
``` |
|
|
|
### Dataset format |
|
|
|
YOLO classification dataset format can be found in detail in the [Dataset Guide](../datasets/classify/index.md). |
|
|
|
## Val |
|
|
|
Validate trained YOLOv8n-cls model accuracy on the MNIST160 dataset. No argument need to passed as the `model` retains |
|
it's training `data` and arguments as model attributes. |
|
|
|
!!! example "" |
|
|
|
=== "Python" |
|
|
|
```python |
|
from ultralytics import YOLO |
|
|
|
# Load a model |
|
model = YOLO('yolov8n-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=yolov8n-cls.pt # val official model |
|
yolo classify val model=path/to/best.pt # val custom model |
|
``` |
|
|
|
## Predict |
|
|
|
Use a trained YOLOv8n-cls model to run predictions on images. |
|
|
|
!!! example "" |
|
|
|
=== "Python" |
|
|
|
```python |
|
from ultralytics import YOLO |
|
|
|
# Load a model |
|
model = YOLO('yolov8n-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=yolov8n-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](https://docs.ultralytics.com/modes/predict/) page. |
|
|
|
## Export |
|
|
|
Export a YOLOv8n-cls model to a different format like ONNX, CoreML, etc. |
|
|
|
!!! example "" |
|
|
|
=== "Python" |
|
|
|
```python |
|
from ultralytics import YOLO |
|
|
|
# Load a model |
|
model = YOLO('yolov8n-cls.pt') # load an official model |
|
model = YOLO('path/to/best.pt') # load a custom trained |
|
|
|
# Export the model |
|
model.export(format='onnx') |
|
``` |
|
=== "CLI" |
|
|
|
```bash |
|
yolo export model=yolov8n-cls.pt format=onnx # export official model |
|
yolo export model=path/to/best.pt format=onnx # export custom trained model |
|
``` |
|
|
|
Available YOLOv8-cls export formats are in the table below. 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. |
|
|
|
| Format | `format` Argument | Model | Metadata | Arguments | |
|
|--------------------------------------------------------------------|-------------------|-------------------------------|----------|-----------------------------------------------------| |
|
| [PyTorch](https://pytorch.org/) | - | `yolov8n-cls.pt` | ✅ | - | |
|
| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n-cls.torchscript` | ✅ | `imgsz`, `optimize` | |
|
| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n-cls.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset` | |
|
| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n-cls_openvino_model/` | ✅ | `imgsz`, `half` | |
|
| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n-cls.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` | |
|
| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n-cls.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms` | |
|
| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n-cls_saved_model/` | ✅ | `imgsz`, `keras` | |
|
| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n-cls.pb` | ❌ | `imgsz` | |
|
| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n-cls.tflite` | ✅ | `imgsz`, `half`, `int8` | |
|
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n-cls_edgetpu.tflite` | ✅ | `imgsz` | |
|
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-cls_web_model/` | ✅ | `imgsz` | |
|
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-cls_paddle_model/` | ✅ | `imgsz` | |
|
| [ncnn](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n-cls_ncnn_model/` | ✅ | `imgsz`, `half` | |
|
|
|
See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page.
|
|
|