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YOLO11 Tasks, Modes, Usage, Macros and Solutions Updates (#16593)

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docs/en/tasks/classify.md
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@ -26,16 +26,16 @@ The output of an image classifier is a single class label and a confidence score
!!! 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).
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).
## [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/v8)
## [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/11)
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.
YOLO11 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>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) at 640 |
|----------------------------------------------------------------------------------------------|-----------------------|------------------|------------------|--------------------------------|-------------------------------------|--------------------|--------------------------|
| -------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ |
| [YOLO11n-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-cls.pt) | 224 | 70.0 | 89.4 | 5.0 ± 0.3 | 1.1 ± 0.0 | 1.6 | 3.3 |
| [YOLO11s-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-cls.pt) | 224 | 75.4 | 92.7 | 7.9 ± 0.2 | 1.3 ± 0.0 | 5.5 | 12.1 |
| [YOLO11m-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-cls.pt) | 224 | 77.3 | 93.9 | 17.2 ± 0.4 | 2.0 ± 0.0 | 10.4 | 39.3 |
@ -47,7 +47,7 @@ YOLOv8 pretrained Classify models are shown here. Detect, Segment and Pose model
## Train
Train YOLOv8n-cls on the MNIST160 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) at image size 64. For a full list of available arguments see the [Configuration](../usage/cfg.md) page.
Train YOLO11n-cls on the MNIST160 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) at image size 64. For a full list of available arguments see the [Configuration](../usage/cfg.md) page.
!!! example
@ -57,9 +57,9 @@ Train YOLOv8n-cls on the MNIST160 dataset for 100 [epochs](https://www.ultralyti
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
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)
@ -69,13 +69,13 @@ Train YOLOv8n-cls on the MNIST160 dataset for 100 [epochs](https://www.ultralyti
```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
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=yolov8n-cls.pt epochs=100 imgsz=64
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=yolov8n-cls.yaml pretrained=yolov8n-cls.pt epochs=100 imgsz=64
yolo classify train data=mnist160 model=yolo11n-cls.yaml pretrained=yolo11n-cls.pt epochs=100 imgsz=64
```
### Dataset format
@ -84,7 +84,7 @@ YOLO classification dataset format can be found in detail in the [Dataset Guide]
## Val
Validate trained YOLOv8n-cls model [accuracy](https://www.ultralytics.com/glossary/accuracy) on the MNIST160 dataset. No arguments are needed as the `model` retains its training `data` and arguments as model attributes.
Validate trained YOLO11n-cls model [accuracy](https://www.ultralytics.com/glossary/accuracy) on the MNIST160 dataset. No arguments are needed as the `model` retains its training `data` and arguments as model attributes.
!!! example
@ -94,7 +94,7 @@ Validate trained YOLOv8n-cls model [accuracy](https://www.ultralytics.com/glossa
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-cls.pt") # load an official model
model = YOLO("yolo11n-cls.pt") # load an official model
model = YOLO("path/to/best.pt") # load a custom model
# Validate the model
@ -106,13 +106,13 @@ Validate trained YOLOv8n-cls model [accuracy](https://www.ultralytics.com/glossa
=== "CLI"
```bash
yolo classify val model=yolov8n-cls.pt # val official model
yolo classify val model=yolo11n-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.
Use a trained YOLO11n-cls model to run predictions on images.
!!! example
@ -122,7 +122,7 @@ Use a trained YOLOv8n-cls model to run predictions on images.
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-cls.pt") # load an official model
model = YOLO("yolo11n-cls.pt") # load an official model
model = YOLO("path/to/best.pt") # load a custom model
# Predict with the model
@ -132,7 +132,7 @@ Use a trained YOLOv8n-cls model to run predictions on images.
=== "CLI"
```bash
yolo classify predict model=yolov8n-cls.pt source='https://ultralytics.com/images/bus.jpg' # predict with official model
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
```
@ -140,7 +140,7 @@ See full `predict` mode details in the [Predict](../modes/predict.md) page.
## Export
Export a YOLOv8n-cls model to a different format like ONNX, CoreML, etc.
Export a YOLO11n-cls model to a different format like ONNX, CoreML, etc.
!!! example
@ -150,7 +150,7 @@ Export a YOLOv8n-cls model to a different format like ONNX, CoreML, etc.
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-cls.pt") # load an official model
model = YOLO("yolo11n-cls.pt") # load an official model
model = YOLO("path/to/best.pt") # load a custom trained model
# Export the model
@ -160,11 +160,11 @@ Export a YOLOv8n-cls model to a different format like ONNX, CoreML, etc.
=== "CLI"
```bash
yolo export model=yolov8n-cls.pt format=onnx # export official model
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 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.
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" %}
@ -172,13 +172,13 @@ See full `export` details in the [Export](../modes/export.md) page.
## FAQ
### What is the purpose of YOLOv8 in image classification?
### What is the purpose of YOLO11 in image classification?
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.
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.
### How do I train a YOLOv8 model for image classification?
### How do I train a YOLO11 model for image classification?
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:
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
@ -188,7 +188,7 @@ To train a YOLOv8 model, you can use either Python or CLI commands. For example,
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-cls.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n-cls.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="mnist160", epochs=100, imgsz=64)
@ -197,18 +197,18 @@ To train a YOLOv8 model, you can use either Python or CLI commands. For example,
=== "CLI"
```bash
yolo classify train data=mnist160 model=yolov8n-cls.pt epochs=100 imgsz=64
yolo classify train data=mnist160 model=yolo11n-cls.pt epochs=100 imgsz=64
```
For more configuration options, visit the [Configuration](../usage/cfg.md) page.
### Where can I find pretrained YOLOv8 classification models?
### Where can I find pretrained YOLO11 classification models?
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.
Pretrained YOLO11 classification models can be found in the [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/11) section. Models like `yolo11n-cls.pt`, `yolo11s-cls.pt`, `yolo11m-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.
### How can I export a trained YOLOv8 model to different formats?
### How can I export a trained YOLO11 model to different formats?
You can export a trained YOLOv8 model to various formats using Python or CLI commands. For instance, to export a model to ONNX format:
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
@ -218,7 +218,7 @@ You can export a trained YOLOv8 model to various formats using Python or CLI com
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-cls.pt") # load the trained model
model = YOLO("yolo11n-cls.pt") # load the trained model
# Export the model to ONNX
model.export(format="onnx")
@ -227,12 +227,12 @@ You can export a trained YOLOv8 model to various formats using Python or CLI com
=== "CLI"
```bash
yolo export model=yolov8n-cls.pt format=onnx # export the trained model to ONNX format
yolo export model=yolo11n-cls.pt format=onnx # export the trained model to ONNX format
```
For detailed export options, refer to the [Export](../modes/export.md) page.
### How do I validate a trained YOLOv8 classification model?
### How do I validate a trained YOLO11 classification model?
To validate a trained model's accuracy on a dataset like MNIST160, you can use the following Python or CLI commands:
@ -244,7 +244,7 @@ To validate a trained model's accuracy on a dataset like MNIST160, you can use t
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-cls.pt") # load the trained 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
@ -255,7 +255,7 @@ To validate a trained model's accuracy on a dataset like MNIST160, you can use t
=== "CLI"
```bash
yolo classify val model=yolov8n-cls.pt # validate the trained model
yolo classify val model=yolo11n-cls.pt # validate the trained model
```
For more information, visit the [Validate](#val) section.