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18
docs/en/datasets/detect/african-wildlife.md
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@ -1,7 +1,7 @@
---
comments: true
description: Explore our African Wildlife Dataset featuring images of buffalo, elephant, rhino, and zebra for training computer vision models. Ideal for research and conservation.
keywords: African Wildlife Dataset, South African animals, object detection, computer vision, YOLOv8, wildlife research, conservation, dataset
keywords: African Wildlife Dataset, South African animals, object detection, computer vision, YOLO11, wildlife research, conservation, dataset
---
# African Wildlife Dataset
@ -16,7 +16,7 @@ This dataset showcases four common animal classes typically found in South Afric
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> African Wildlife Animals Detection using Ultralytics YOLOv8
<strong>Watch:</strong> African Wildlife Animals Detection using Ultralytics YOLO11
</p>
## Dataset Structure
@ -43,7 +43,7 @@ A YAML (Yet Another Markup Language) file defines the dataset configuration, inc
## Usage
To train a YOLOv8n model on the African wildlife dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the provided code samples. For a comprehensive list of available parameters, refer to the model's [Training](../../modes/train.md) page.
To train a YOLO11n model on the African wildlife dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the provided code samples. For a comprehensive list of available parameters, refer to the model's [Training](../../modes/train.md) page.
!!! example "Train Example"
@ -53,7 +53,7 @@ To train a YOLOv8n model on the African wildlife dataset for 100 [epochs](https:
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="african-wildlife.yaml", epochs=100, imgsz=640)
@ -63,7 +63,7 @@ To train a YOLOv8n model on the African wildlife dataset for 100 [epochs](https:
```bash
# Start training from a pretrained *.pt model
yolo detect train data=african-wildlife.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=african-wildlife.yaml model=yolo11n.pt epochs=100 imgsz=640
```
!!! example "Inference Example"
@ -107,9 +107,9 @@ The dataset has been released available under the [AGPL-3.0 License](https://git
The African Wildlife Dataset includes images of four common animal species found in South African nature reserves: buffalo, elephant, rhino, and zebra. It is a valuable resource for training computer vision algorithms in object detection and animal identification. The dataset supports various tasks like object tracking, research, and conservation efforts. For more information on its structure and applications, refer to the [Dataset Structure](#dataset-structure) section and [Applications](#applications) of the dataset.
### How do I train a YOLOv8 model using the African Wildlife Dataset?
### How do I train a YOLO11 model using the African Wildlife Dataset?
You can train a YOLOv8 model on the African Wildlife Dataset by using the `african-wildlife.yaml` configuration file. Below is an example of how to train the YOLOv8n model for 100 epochs with an image size of 640:
You can train a YOLO11 model on the African Wildlife Dataset by using the `african-wildlife.yaml` configuration file. Below is an example of how to train the YOLO11n model for 100 epochs with an image size of 640:
!!! example
@ -119,7 +119,7 @@ You can train a YOLOv8 model on the African Wildlife Dataset by using the `afric
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="african-wildlife.yaml", epochs=100, imgsz=640)
@ -129,7 +129,7 @@ You can train a YOLOv8 model on the African Wildlife Dataset by using the `afric
```bash
# Start training from a pretrained *.pt model
yolo detect train data=african-wildlife.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=african-wildlife.yaml model=yolo11n.pt epochs=100 imgsz=640
```
For additional training parameters and options, refer to the [Training](../../modes/train.md) documentation.

12
docs/en/datasets/detect/argoverse.md
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@ -43,7 +43,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
## Usage
To train a YOLOv8n model on the Argoverse dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
To train a YOLO11n model on the Argoverse dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
!!! example "Train Example"
@ -53,7 +53,7 @@ To train a YOLOv8n model on the Argoverse dataset for 100 [epochs](https://www.u
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="Argoverse.yaml", epochs=100, imgsz=640)
@ -63,7 +63,7 @@ To train a YOLOv8n model on the Argoverse dataset for 100 [epochs](https://www.u
```bash
# Start training from a pretrained *.pt model
yolo detect train data=Argoverse.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=Argoverse.yaml model=yolo11n.pt epochs=100 imgsz=640
```
## Sample Data and Annotations
@ -104,7 +104,7 @@ The [Argoverse](https://www.argoverse.org/) dataset, developed by Argo AI, suppo
### How can I train an Ultralytics YOLO model using the Argoverse dataset?
To train a YOLOv8 model with the Argoverse dataset, use the provided YAML configuration file and the following code:
To train a YOLO11 model with the Argoverse dataset, use the provided YAML configuration file and the following code:
!!! example "Train Example"
@ -114,7 +114,7 @@ To train a YOLOv8 model with the Argoverse dataset, use the provided YAML config
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="Argoverse.yaml", epochs=100, imgsz=640)
@ -125,7 +125,7 @@ To train a YOLOv8 model with the Argoverse dataset, use the provided YAML config
```bash
# Start training from a pretrained *.pt model
yolo detect train data=Argoverse.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=Argoverse.yaml model=yolo11n.pt epochs=100 imgsz=640
```
For a detailed explanation of the arguments, refer to the model [Training](../../modes/train.md) page.

18
docs/en/datasets/detect/brain-tumor.md
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@ -42,7 +42,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
## Usage
To train a YOLOv8n model on the brain tumor dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, utilize the provided code snippets. For a detailed list of available arguments, consult the model's [Training](../../modes/train.md) page.
To train a YOLO11n model on the brain tumor dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, utilize the provided code snippets. For a detailed list of available arguments, consult the model's [Training](../../modes/train.md) page.
!!! example "Train Example"
@ -52,7 +52,7 @@ To train a YOLOv8n model on the brain tumor dataset for 100 [epochs](https://www
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="brain-tumor.yaml", epochs=100, imgsz=640)
@ -62,7 +62,7 @@ To train a YOLOv8n model on the brain tumor dataset for 100 [epochs](https://www
```bash
# Start training from a pretrained *.pt model
yolo detect train data=brain-tumor.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=brain-tumor.yaml model=yolo11n.pt epochs=100 imgsz=640
```
!!! example "Inference Example"
@ -106,9 +106,9 @@ The dataset has been released available under the [AGPL-3.0 License](https://git
The brain tumor dataset is divided into two subsets: the **training set** consists of 893 images with corresponding annotations, while the **testing set** comprises 223 images with paired annotations. This structured division aids in developing robust and accurate computer vision models for detecting brain tumors. For more information on the dataset structure, visit the [Dataset Structure](#dataset-structure) section.
### How can I train a YOLOv8 model on the brain tumor dataset using Ultralytics?
### How can I train a YOLO11 model on the brain tumor dataset using Ultralytics?
You can train a YOLOv8 model on the brain tumor dataset for 100 epochs with an image size of 640px using both Python and CLI methods. Below are the examples for both:
You can train a YOLO11 model on the brain tumor dataset for 100 epochs with an image size of 640px using both Python and CLI methods. Below are the examples for both:
!!! example "Train Example"
@ -118,7 +118,7 @@ You can train a YOLOv8 model on the brain tumor dataset for 100 epochs with an i
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="brain-tumor.yaml", epochs=100, imgsz=640)
@ -129,7 +129,7 @@ You can train a YOLOv8 model on the brain tumor dataset for 100 epochs with an i
```bash
# Start training from a pretrained *.pt model
yolo detect train data=brain-tumor.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=brain-tumor.yaml model=yolo11n.pt epochs=100 imgsz=640
```
For a detailed list of available arguments, refer to the [Training](../../modes/train.md) page.
@ -138,9 +138,9 @@ For a detailed list of available arguments, refer to the [Training](../../modes/
Using the brain tumor dataset in AI projects enables early diagnosis and treatment planning for brain tumors. It helps in automating brain tumor identification through computer vision, facilitating accurate and timely medical interventions, and supporting personalized treatment strategies. This application holds significant potential in improving patient outcomes and medical efficiencies.
### How do I perform inference using a fine-tuned YOLOv8 model on the brain tumor dataset?
### How do I perform inference using a fine-tuned YOLO11 model on the brain tumor dataset?
Inference using a fine-tuned YOLOv8 model can be performed with either Python or CLI approaches. Here are the examples:
Inference using a fine-tuned YOLO11 model can be performed with either Python or CLI approaches. Here are the examples:
!!! example "Inference Example"

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docs/en/datasets/detect/coco.md
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@ -21,13 +21,13 @@ The [COCO](https://cocodataset.org/#home) (Common Objects in Context) dataset is
## COCO Pretrained Models
| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
| ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt) | 640 | 37.3 | 80.4 | 0.99 | 3.2 | 8.7 |
| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s.pt) | 640 | 44.9 | 128.4 | 1.20 | 11.2 | 28.6 |
| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m.pt) | 640 | 50.2 | 234.7 | 1.83 | 25.9 | 78.9 |
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l.pt) | 640 | 52.9 | 375.2 | 2.39 | 43.7 | 165.2 |
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x.pt) | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 |
| [YOLO11n](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt) | 640 | 39.5 | 56.1 ± 0.8 | 1.5 ± 0.0 | 2.6 | 6.5 |
| [YOLO11s](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt) | 640 | 47.0 | 90.0 ± 1.2 | 2.5 ± 0.0 | 9.4 | 21.5 |
| [YOLO11m](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m.pt) | 640 | 51.5 | 183.2 ± 2.0 | 4.7 ± 0.1 | 20.1 | 68.0 |
| [YOLO11l](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l.pt) | 640 | 53.4 | 238.6 ± 1.4 | 6.2 ± 0.1 | 25.3 | 86.9 |
| [YOLO11x](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x.pt) | 640 | 54.7 | 462.8 ± 6.7 | 11.3 ± 0.2 | 56.9 | 194.9 |
## Key Features
@ -60,7 +60,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
## Usage
To train a YOLOv8n model on the COCO dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
To train a YOLO11n model on the COCO dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
!!! example "Train Example"
@ -70,7 +70,7 @@ To train a YOLOv8n model on the COCO dataset for 100 [epochs](https://www.ultral
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="coco.yaml", epochs=100, imgsz=640)
@ -80,7 +80,7 @@ To train a YOLOv8n model on the COCO dataset for 100 [epochs](https://www.ultral
```bash
# Start training from a pretrained *.pt model
yolo detect train data=coco.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=coco.yaml model=yolo11n.pt epochs=100 imgsz=640
```
## Sample Images and Annotations
@ -122,7 +122,7 @@ The [COCO dataset](https://cocodataset.org/#home) (Common Objects in Context) is
### How can I train a YOLO model using the COCO dataset?
To train a YOLOv8 model using the COCO dataset, you can use the following code snippets:
To train a YOLO11 model using the COCO dataset, you can use the following code snippets:
!!! example "Train Example"
@ -132,7 +132,7 @@ To train a YOLOv8 model using the COCO dataset, you can use the following code s
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="coco.yaml", epochs=100, imgsz=640)
@ -142,7 +142,7 @@ To train a YOLOv8 model using the COCO dataset, you can use the following code s
```bash
# Start training from a pretrained *.pt model
yolo detect train data=coco.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=coco.yaml model=yolo11n.pt epochs=100 imgsz=640
```
Refer to the [Training page](../../modes/train.md) for more details on available arguments.
@ -156,13 +156,13 @@ The COCO dataset includes:
- Standardized evaluation metrics for object detection (mAP) and segmentation (mean Average Recall, mAR).
- **Mosaicing** technique in training batches to enhance model generalization across various object sizes and contexts.
### Where can I find pretrained YOLOv8 models trained on the COCO dataset?
### Where can I find pretrained YOLO11 models trained on the COCO dataset?
Pretrained YOLOv8 models on the COCO dataset can be downloaded from the links provided in the documentation. Examples include:
Pretrained YOLO11 models on the COCO dataset can be downloaded from the links provided in the documentation. Examples include:
- [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt)
- [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s.pt)
- [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m.pt)
- [YOLO11n](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt)
- [YOLO11s](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt)
- [YOLO11m](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m.pt)
These models vary in size, mAP, and inference speed, providing options for different performance and resource requirements.

24
docs/en/datasets/detect/coco8.md
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@ -1,7 +1,7 @@
---
comments: true
description: Explore the Ultralytics COCO8 dataset, a versatile and manageable set of 8 images perfect for testing object detection models and training pipelines.
keywords: COCO8, Ultralytics, dataset, object detection, YOLOv8, training, validation, machine learning, computer vision
keywords: COCO8, Ultralytics, dataset, object detection, YOLO11, training, validation, machine learning, computer vision
---
# COCO8 Dataset
@ -21,7 +21,7 @@ keywords: COCO8, Ultralytics, dataset, object detection, YOLOv8, training, valid
<strong>Watch:</strong> Ultralytics COCO Dataset Overview
</p>
This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com/) and [YOLOv8](https://github.com/ultralytics/ultralytics).
This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com/) and [YOLO11](https://github.com/ultralytics/ultralytics).
## Dataset YAML
@ -35,7 +35,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
## Usage
To train a YOLOv8n model on the COCO8 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
To train a YOLO11n model on the COCO8 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
!!! example "Train Example"
@ -45,7 +45,7 @@ To train a YOLOv8n model on the COCO8 dataset for 100 [epochs](https://www.ultra
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
@ -55,7 +55,7 @@ To train a YOLOv8n model on the COCO8 dataset for 100 [epochs](https://www.ultra
```bash
# Start training from a pretrained *.pt model
yolo detect train data=coco8.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=coco8.yaml model=yolo11n.pt epochs=100 imgsz=640
```
## Sample Images and Annotations
@ -95,9 +95,9 @@ We would like to acknowledge the COCO Consortium for creating and maintaining th
The Ultralytics COCO8 dataset is a compact yet versatile object detection dataset consisting of the first 8 images from the COCO train 2017 set, with 4 images for training and 4 for validation. It is designed for testing and debugging object detection models and experimentation with new detection approaches. Despite its small size, COCO8 offers enough diversity to act as a sanity check for your training pipelines before deploying larger datasets. For more details, view the [COCO8 dataset](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8.yaml).
### How do I train a YOLOv8 model using the COCO8 dataset?
### How do I train a YOLO11 model using the COCO8 dataset?
To train a YOLOv8 model using the COCO8 dataset, you can employ either Python or CLI commands. Here's how you can start:
To train a YOLO11 model using the COCO8 dataset, you can employ either Python or CLI commands. Here's how you can start:
!!! example "Train Example"
@ -107,7 +107,7 @@ To train a YOLOv8 model using the COCO8 dataset, you can employ either Python or
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
@ -117,19 +117,19 @@ To train a YOLOv8 model using the COCO8 dataset, you can employ either Python or
```bash
# Start training from a pretrained *.pt model
yolo detect train data=coco8.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=coco8.yaml model=yolo11n.pt epochs=100 imgsz=640
```
For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
### Why should I use Ultralytics HUB for managing my COCO8 training?
Ultralytics HUB is an all-in-one web tool designed to simplify the training and deployment of YOLO models, including the Ultralytics YOLOv8 models on the COCO8 dataset. It offers cloud training, real-time tracking, and seamless dataset management. HUB allows you to start training with a single click and avoids the complexities of manual setups. Discover more about [Ultralytics HUB](https://hub.ultralytics.com/) and its benefits.
Ultralytics HUB is an all-in-one web tool designed to simplify the training and deployment of YOLO models, including the Ultralytics YOLO11 models on the COCO8 dataset. It offers cloud training, real-time tracking, and seamless dataset management. HUB allows you to start training with a single click and avoids the complexities of manual setups. Discover more about [Ultralytics HUB](https://hub.ultralytics.com/) and its benefits.
### What are the benefits of using mosaic augmentation in training with the COCO8 dataset?
Mosaic augmentation, demonstrated in the COCO8 dataset, combines multiple images into a single image during training. This technique increases the variety of objects and scenes in each training batch, improving the model's ability to generalize across different object sizes, aspect ratios, and contexts. This results in a more robust object detection model. For more details, refer to the [training guide](#usage).
### How can I validate my YOLOv8 model trained on the COCO8 dataset?
### How can I validate my YOLO11 model trained on the COCO8 dataset?
Validation of your YOLOv8 model trained on the COCO8 dataset can be performed using the model's validation commands. You can invoke the validation mode via CLI or Python script to evaluate the model's performance using precise metrics. For detailed instructions, visit the [Validation](../../modes/val.md) page.
Validation of your YOLO11 model trained on the COCO8 dataset can be performed using the model's validation commands. You can invoke the validation mode via CLI or Python script to evaluate the model's performance using precise metrics. For detailed instructions, visit the [Validation](../../modes/val.md) page.

14
docs/en/datasets/detect/globalwheat2020.md
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@ -38,7 +38,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
## Usage
To train a YOLOv8n model on the Global Wheat Head Dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
To train a YOLO11n model on the Global Wheat Head Dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
!!! example "Train Example"
@ -48,7 +48,7 @@ To train a YOLOv8n model on the Global Wheat Head Dataset for 100 [epochs](https
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="GlobalWheat2020.yaml", epochs=100, imgsz=640)
@ -58,7 +58,7 @@ To train a YOLOv8n model on the Global Wheat Head Dataset for 100 [epochs](https
```bash
# Start training from a pretrained *.pt model
yolo detect train data=GlobalWheat2020.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=GlobalWheat2020.yaml model=yolo11n.pt epochs=100 imgsz=640
```
## Sample Data and Annotations
@ -96,9 +96,9 @@ We would like to acknowledge the researchers and institutions that contributed t
The Global Wheat Head Dataset is primarily used for developing and training deep learning models aimed at wheat head detection. This is crucial for applications in wheat phenotyping and crop management, allowing for more accurate estimations of wheat head density, size, and overall crop yield potential. Accurate detection methods help in assessing crop health and maturity, essential for efficient crop management.
### How do I train a YOLOv8n model on the Global Wheat Head Dataset?
### How do I train a YOLO11n model on the Global Wheat Head Dataset?
To train a YOLOv8n model on the Global Wheat Head Dataset, you can use the following code snippets. Make sure you have the `GlobalWheat2020.yaml` configuration file specifying dataset paths and classes:
To train a YOLO11n model on the Global Wheat Head Dataset, you can use the following code snippets. Make sure you have the `GlobalWheat2020.yaml` configuration file specifying dataset paths and classes:
!!! example "Train Example"
@ -108,7 +108,7 @@ To train a YOLOv8n model on the Global Wheat Head Dataset, you can use the follo
from ultralytics import YOLO
# Load a pre-trained model (recommended for training)
model = YOLO("yolov8n.pt")
model = YOLO("yolo11n.pt")
# Train the model
results = model.train(data="GlobalWheat2020.yaml", epochs=100, imgsz=640)
@ -118,7 +118,7 @@ To train a YOLOv8n model on the Global Wheat Head Dataset, you can use the follo
```bash
# Start training from a pretrained *.pt model
yolo detect train data=GlobalWheat2020.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=GlobalWheat2020.yaml model=yolo11n.pt epochs=100 imgsz=640
```
For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.

16
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@ -56,7 +56,7 @@ Here's how you can use these formats to train your model:
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
@ -66,7 +66,7 @@ Here's how you can use these formats to train your model:
```bash
# Start training from a pretrained *.pt model
yolo detect train data=coco8.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=coco8.yaml model=yolo11n.pt epochs=100 imgsz=640
```
## Supported Datasets
@ -158,11 +158,11 @@ Ultralytics YOLO supports a wide range of datasets, including:
- [Objects365](objects365.md)
- [OpenImagesV7](open-images-v7.md)
Each dataset page provides detailed information on the structure and usage tailored for efficient YOLOv8 training. Explore the full list in the [Supported Datasets](#supported-datasets) section.
Each dataset page provides detailed information on the structure and usage tailored for efficient YOLO11 training. Explore the full list in the [Supported Datasets](#supported-datasets) section.
### How do I start training a YOLOv8 model using my dataset?
### How do I start training a YOLO11 model using my dataset?
To start training a YOLOv8 model, ensure your dataset is formatted correctly and the paths are defined in a YAML file. Use the following script to begin training:
To start training a YOLO11 model, ensure your dataset is formatted correctly and the paths are defined in a YAML file. Use the following script to begin training:
!!! example
@ -171,18 +171,18 @@ To start training a YOLOv8 model, ensure your dataset is formatted correctly and
```python
from ultralytics import YOLO
model = YOLO("yolov8n.pt") # Load a pretrained model
model = YOLO("yolo11n.pt") # Load a pretrained model
results = model.train(data="path/to/your_dataset.yaml", epochs=100, imgsz=640)
```
=== "CLI"
```bash
yolo detect train data=path/to/your_dataset.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=path/to/your_dataset.yaml model=yolo11n.pt epochs=100 imgsz=640
```
Refer to the [Usage](#usage) section for more details on utilizing different modes, including CLI commands.
### Where can I find practical examples of using Ultralytics YOLO for object detection?
Ultralytics provides numerous examples and practical guides for using YOLOv8 in diverse applications. For a comprehensive overview, visit the [Ultralytics Blog](https://www.ultralytics.com/blog) where you can find case studies, detailed tutorials, and community stories showcasing object detection, segmentation, and more with YOLOv8. For specific examples, check the [Usage](../../modes/predict.md) section in the documentation.
Ultralytics provides numerous examples and practical guides for using YOLO11 in diverse applications. For a comprehensive overview, visit the [Ultralytics Blog](https://www.ultralytics.com/blog) where you can find case studies, detailed tutorials, and community stories showcasing object detection, segmentation, and more with YOLO11. For specific examples, check the [Usage](../../modes/predict.md) section in the documentation.

16
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@ -56,7 +56,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
## Usage
To train a YOLOv8n model on the LVIS dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
To train a YOLO11n model on the LVIS dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
!!! example "Train Example"
@ -66,7 +66,7 @@ To train a YOLOv8n model on the LVIS dataset for 100 [epochs](https://www.ultral
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="lvis.yaml", epochs=100, imgsz=640)
@ -76,7 +76,7 @@ To train a YOLOv8n model on the LVIS dataset for 100 [epochs](https://www.ultral
```bash
# Start training from a pretrained *.pt model
yolo detect train data=lvis.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=lvis.yaml model=yolo11n.pt epochs=100 imgsz=640
```
## Sample Images and Annotations
@ -114,9 +114,9 @@ We would like to acknowledge the LVIS Consortium for creating and maintaining th
The [LVIS dataset](https://www.lvisdataset.org/) is a large-scale dataset with fine-grained vocabulary-level annotations developed by Facebook AI Research (FAIR). It is primarily used for object detection and instance segmentation, featuring over 1203 object categories and 2 million instance annotations. Researchers and practitioners use it to train and benchmark models like Ultralytics YOLO for advanced computer vision tasks. The dataset's extensive size and diversity make it an essential resource for pushing the boundaries of model performance in detection and segmentation.
### How can I train a YOLOv8n model using the LVIS dataset?
### How can I train a YOLO11n model using the LVIS dataset?
To train a YOLOv8n model on the LVIS dataset for 100 epochs with an image size of 640, follow the example below. This process utilizes Ultralytics' framework, which offers comprehensive training features.
To train a YOLO11n model on the LVIS dataset for 100 epochs with an image size of 640, follow the example below. This process utilizes Ultralytics' framework, which offers comprehensive training features.
!!! example "Train Example"
@ -126,7 +126,7 @@ To train a YOLOv8n model on the LVIS dataset for 100 epochs with an image size o
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="lvis.yaml", epochs=100, imgsz=640)
@ -137,7 +137,7 @@ To train a YOLOv8n model on the LVIS dataset for 100 epochs with an image size o
```bash
# Start training from a pretrained *.pt model
yolo detect train data=lvis.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=lvis.yaml model=yolo11n.pt epochs=100 imgsz=640
```
For detailed training configurations, refer to the [Training](../../modes/train.md) documentation.
@ -148,7 +148,7 @@ The images in the LVIS dataset are the same as those in the [COCO dataset](./coc
### Why should I use Ultralytics YOLO for training on the LVIS dataset?
Ultralytics YOLO models, including the latest YOLOv8, are optimized for real-time object detection with state-of-the-art [accuracy](https://www.ultralytics.com/glossary/accuracy) and speed. They support a wide range of annotations, such as the fine-grained ones provided by the LVIS dataset, making them ideal for advanced computer vision applications. Moreover, Ultralytics offers seamless integration with various [training](../../modes/train.md), [validation](../../modes/val.md), and [prediction](../../modes/predict.md) modes, ensuring efficient model development and deployment.
Ultralytics YOLO models, including the latest YOLO11, are optimized for real-time object detection with state-of-the-art [accuracy](https://www.ultralytics.com/glossary/accuracy) and speed. They support a wide range of annotations, such as the fine-grained ones provided by the LVIS dataset, making them ideal for advanced computer vision applications. Moreover, Ultralytics offers seamless integration with various [training](../../modes/train.md), [validation](../../modes/val.md), and [prediction](../../modes/predict.md) modes, ensuring efficient model development and deployment.
### Can I see some sample annotations from the LVIS dataset?

16
docs/en/datasets/detect/objects365.md
View file

@ -1,7 +1,7 @@
---
comments: true
description: Explore the Objects365 Dataset with 2M images and 30M bounding boxes across 365 categories. Enhance your object detection models with diverse, high-quality data.
keywords: Objects365 dataset, object detection, machine learning, deep learning, computer vision, annotated images, bounding boxes, YOLOv8, high-resolution images, dataset configuration
keywords: Objects365 dataset, object detection, machine learning, deep learning, computer vision, annotated images, bounding boxes, YOLO11, high-resolution images, dataset configuration
---
# Objects365 Dataset
@ -38,7 +38,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
## Usage
To train a YOLOv8n model on the Objects365 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
To train a YOLO11n model on the Objects365 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
!!! example "Train Example"
@ -48,7 +48,7 @@ To train a YOLOv8n model on the Objects365 dataset for 100 [epochs](https://www.
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="Objects365.yaml", epochs=100, imgsz=640)
@ -58,7 +58,7 @@ To train a YOLOv8n model on the Objects365 dataset for 100 [epochs](https://www.
```bash
# Start training from a pretrained *.pt model
yolo detect train data=Objects365.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=Objects365.yaml model=yolo11n.pt epochs=100 imgsz=640
```
## Sample Data and Annotations
@ -97,9 +97,9 @@ We would like to acknowledge the team of researchers who created and maintain th
The [Objects365 dataset](https://www.objects365.org/) is designed for object detection tasks in [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) and computer vision. It provides a large-scale, high-quality dataset with 2 million annotated images and 30 million bounding boxes across 365 categories. Leveraging such a diverse dataset helps improve the performance and generalization of object detection models, making it invaluable for research and development in the field.
### How can I train a YOLOv8 model on the Objects365 dataset?
### How can I train a YOLO11 model on the Objects365 dataset?
To train a YOLOv8n model using the Objects365 dataset for 100 epochs with an image size of 640, follow these instructions:
To train a YOLO11n model using the Objects365 dataset for 100 epochs with an image size of 640, follow these instructions:
!!! example "Train Example"
@ -109,7 +109,7 @@ To train a YOLOv8n model using the Objects365 dataset for 100 epochs with an ima
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="Objects365.yaml", epochs=100, imgsz=640)
@ -119,7 +119,7 @@ To train a YOLOv8n model using the Objects365 dataset for 100 epochs with an ima
```bash
# Start training from a pretrained *.pt model
yolo detect train data=Objects365.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=Objects365.yaml model=yolo11n.pt epochs=100 imgsz=640
```
Refer to the [Training](../../modes/train.md) page for a comprehensive list of available arguments.

26
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@ -1,7 +1,7 @@
---
comments: true
description: Explore the comprehensive Open Images V7 dataset by Google. Learn about its annotations, applications, and use YOLOv8 pretrained models for computer vision tasks.
keywords: Open Images V7, Google dataset, computer vision, YOLOv8 models, object detection, image segmentation, visual relationships, AI research, Ultralytics
description: Explore the comprehensive Open Images V7 dataset by Google. Learn about its annotations, applications, and use YOLO11 pretrained models for computer vision tasks.
keywords: Open Images V7, Google dataset, computer vision, YOLO11 models, object detection, image segmentation, visual relationships, AI research, Ultralytics
---
# Open Images V7 Dataset
@ -69,7 +69,7 @@ Typically, datasets come with a YAML (Yet Another Markup Language) file that del
## Usage
To train a YOLOv8n model on the Open Images V7 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
To train a YOLO11n model on the Open Images V7 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
!!! warning
@ -87,8 +87,8 @@ To train a YOLOv8n model on the Open Images V7 dataset for 100 [epochs](https://
```python
from ultralytics import YOLO
# Load a COCO-pretrained YOLOv8n model
model = YOLO("yolov8n.pt")
# Load a COCO-pretrained YOLO11n model
model = YOLO("yolo11n.pt")
# Train the model on the Open Images V7 dataset
results = model.train(data="open-images-v7.yaml", epochs=100, imgsz=640)
@ -97,8 +97,8 @@ To train a YOLOv8n model on the Open Images V7 dataset for 100 [epochs](https://
=== "CLI"
```bash
# Train a COCO-pretrained YOLOv8n model on the Open Images V7 dataset
yolo detect train data=open-images-v7.yaml model=yolov8n.pt epochs=100 imgsz=640
# Train a COCO-pretrained YOLO11n model on the Open Images V7 dataset
yolo detect train data=open-images-v7.yaml model=yolo11n.pt epochs=100 imgsz=640
```
## Sample Data and Annotations
@ -136,9 +136,9 @@ A heartfelt acknowledgment goes out to the Google AI team for creating and maint
Open Images V7 is an extensive and versatile dataset created by Google, designed to advance research in computer vision. It includes image-level labels, object bounding boxes, object segmentation masks, visual relationships, and localized narratives, making it ideal for various computer vision tasks such as object detection, segmentation, and relationship detection.
### How do I train a YOLOv8 model on the Open Images V7 dataset?
### How do I train a YOLO11 model on the Open Images V7 dataset?
To train a YOLOv8 model on the Open Images V7 dataset, you can use both Python and CLI commands. Here's an example of training the YOLOv8n model for 100 epochs with an image size of 640:
To train a YOLO11 model on the Open Images V7 dataset, you can use both Python and CLI commands. Here's an example of training the YOLO11n model for 100 epochs with an image size of 640:
!!! example "Train Example"
@ -147,8 +147,8 @@ To train a YOLOv8 model on the Open Images V7 dataset, you can use both Python a
```python
from ultralytics import YOLO
# Load a COCO-pretrained YOLOv8n model
model = YOLO("yolov8n.pt")
# Load a COCO-pretrained YOLO11n model
model = YOLO("yolo11n.pt")
# Train the model on the Open Images V7 dataset
results = model.train(data="open-images-v7.yaml", epochs=100, imgsz=640)
@ -158,8 +158,8 @@ To train a YOLOv8 model on the Open Images V7 dataset, you can use both Python a
=== "CLI"
```bash
# Train a COCO-pretrained YOLOv8n model on the Open Images V7 dataset
yolo detect train data=open-images-v7.yaml model=yolov8n.pt epochs=100 imgsz=640
# Train a COCO-pretrained YOLO11n model on the Open Images V7 dataset
yolo detect train data=open-images-v7.yaml model=yolo11n.pt epochs=100 imgsz=640
```
For more details on arguments and settings, refer to the [Training](../../modes/train.md) page.

4
docs/en/datasets/detect/roboflow-100.md
View file

@ -67,7 +67,7 @@ Dataset benchmarking evaluates machine learning model performance on specific da
if path.exists():
# Fix YAML file and run training
benchmark.fix_yaml(str(path))
os.system(f"yolo detect train data={path} model=yolov8s.pt epochs=1 batch=16")
os.system(f"yolo detect train data={path} model=yolo11s.pt epochs=1 batch=16")
# Run validation and evaluate
os.system(f"yolo detect val data={path} model=runs/detect/train/weights/best.pt > {val_log_file} 2>&1")
@ -165,7 +165,7 @@ To use the Roboflow 100 dataset for benchmarking, you can implement the RF100Ben
if path.exists():
# Fix YAML file and run training
benchmark.fix_yaml(str(path))
os.system(f"yolo detect train data={path} model=yolov8s.pt epochs=1 batch=16")
os.system(f"yolo detect train data={path} model=yolo11n.pt epochs=1 batch=16")
# Run validation and evaluate
os.system(f"yolo detect val data={path} model=runs/detect/train/weights/best.pt > {val_log_file} 2>&1")

16
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@ -1,7 +1,7 @@
---
comments: true
description: Discover the Signature Detection Dataset for training models to identify and verify human signatures in various documents. Perfect for document verification and fraud prevention.
keywords: Signature Detection Dataset, document verification, fraud detection, computer vision, YOLOv8, Ultralytics, annotated signatures, training dataset
keywords: Signature Detection Dataset, document verification, fraud detection, computer vision, YOLO11, Ultralytics, annotated signatures, training dataset
---
# Signature Detection Dataset
@ -31,7 +31,7 @@ A YAML (Yet Another Markup Language) file defines the dataset configuration, inc
## Usage
To train a YOLOv8n model on the signature detection dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the provided code samples. For a comprehensive list of available parameters, refer to the model's [Training](../../modes/train.md) page.
To train a YOLO11n model on the signature detection dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the provided code samples. For a comprehensive list of available parameters, refer to the model's [Training](../../modes/train.md) page.
!!! example "Train Example"
@ -41,7 +41,7 @@ To train a YOLOv8n model on the signature detection dataset for 100 [epochs](htt
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="signature.yaml", epochs=100, imgsz=640)
@ -51,7 +51,7 @@ To train a YOLOv8n model on the signature detection dataset for 100 [epochs](htt
```bash
# Start training from a pretrained *.pt model
yolo detect train data=signature.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=signature.yaml model=yolo11n.pt epochs=100 imgsz=640
```
!!! example "Inference Example"
@ -95,9 +95,9 @@ The dataset has been released available under the [AGPL-3.0 License](https://git
The Signature Detection Dataset is a collection of annotated images aimed at detecting human signatures within various document types. It can be applied in computer vision tasks such as [object detection](https://www.ultralytics.com/glossary/object-detection) and tracking, primarily for document verification, fraud detection, and archival research. This dataset helps train models to recognize signatures in different contexts, making it valuable for both research and practical applications.
### How do I train a YOLOv8n model on the Signature Detection Dataset?
### How do I train a YOLO11n model on the Signature Detection Dataset?
To train a YOLOv8n model on the Signature Detection Dataset, follow these steps:
To train a YOLO11n model on the Signature Detection Dataset, follow these steps:
1. Download the `signature.yaml` dataset configuration file from [signature.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/signature.yaml).
2. Use the following Python script or CLI command to start training:
@ -110,7 +110,7 @@ To train a YOLOv8n model on the Signature Detection Dataset, follow these steps:
from ultralytics import YOLO
# Load a pretrained model
model = YOLO("yolov8n.pt")
model = YOLO("yolo11n.pt")
# Train the model
results = model.train(data="signature.yaml", epochs=100, imgsz=640)
@ -119,7 +119,7 @@ To train a YOLOv8n model on the Signature Detection Dataset, follow these steps:
=== "CLI"
```bash
yolo detect train data=signature.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=signature.yaml model=yolo11n.pt epochs=100 imgsz=640
```
For more details, refer to the [Training](../../modes/train.md) page.

14
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@ -51,7 +51,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
## Usage
To train a YOLOv8n model on the SKU-110K dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
To train a YOLO11n model on the SKU-110K dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
!!! example "Train Example"
@ -61,7 +61,7 @@ To train a YOLOv8n model on the SKU-110K dataset for 100 [epochs](https://www.ul
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="SKU-110K.yaml", epochs=100, imgsz=640)
@ -71,7 +71,7 @@ To train a YOLOv8n model on the SKU-110K dataset for 100 [epochs](https://www.ul
```bash
# Start training from a pretrained *.pt model
yolo detect train data=SKU-110K.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=SKU-110K.yaml model=yolo11n.pt epochs=100 imgsz=640
```
## Sample Data and Annotations
@ -109,9 +109,9 @@ We would like to acknowledge Eran Goldman et al. for creating and maintaining th
The SKU-110k dataset consists of densely packed retail shelf images designed to aid research in object detection tasks. Developed by Eran Goldman et al., it includes over 110,000 unique SKU categories. Its importance lies in its ability to challenge state-of-the-art object detectors with diverse object appearances and close proximity, making it an invaluable resource for researchers and practitioners in computer vision. Learn more about the dataset's structure and applications in our [SKU-110k Dataset](#sku-110k-dataset) section.
### How do I train a YOLOv8 model using the SKU-110k dataset?
### How do I train a YOLO11 model using the SKU-110k dataset?
Training a YOLOv8 model on the SKU-110k dataset is straightforward. Here's an example to train a YOLOv8n model for 100 epochs with an image size of 640:
Training a YOLO11 model on the SKU-110k dataset is straightforward. Here's an example to train a YOLO11n model for 100 epochs with an image size of 640:
!!! example "Train Example"
@ -121,7 +121,7 @@ Training a YOLOv8 model on the SKU-110k dataset is straightforward. Here's an ex
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="SKU-110K.yaml", epochs=100, imgsz=640)
@ -132,7 +132,7 @@ Training a YOLOv8 model on the SKU-110k dataset is straightforward. Here's an ex
```bash
# Start training from a pretrained *.pt model
yolo detect train data=SKU-110K.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=SKU-110K.yaml model=yolo11n.pt epochs=100 imgsz=640
```
For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.

14
docs/en/datasets/detect/visdrone.md
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@ -47,7 +47,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
## Usage
To train a YOLOv8n model on the VisDrone dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
To train a YOLO11n model on the VisDrone dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
!!! example "Train Example"
@ -57,7 +57,7 @@ To train a YOLOv8n model on the VisDrone dataset for 100 [epochs](https://www.ul
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="VisDrone.yaml", epochs=100, imgsz=640)
@ -67,7 +67,7 @@ To train a YOLOv8n model on the VisDrone dataset for 100 [epochs](https://www.ul
```bash
# Start training from a pretrained *.pt model
yolo detect train data=VisDrone.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=VisDrone.yaml model=yolo11n.pt epochs=100 imgsz=640
```
## Sample Data and Annotations
@ -113,9 +113,9 @@ The [VisDrone Dataset](https://github.com/VisDrone/VisDrone-Dataset) is a large-
- **Diversity**: Collected across 14 cities, in urban and rural settings, under different weather and lighting conditions.
- **Tasks**: Split into five main tasks—object detection in images and videos, single-object and multi-object tracking, and crowd counting.
### How can I use the VisDrone Dataset to train a YOLOv8 model with Ultralytics?
### How can I use the VisDrone Dataset to train a YOLO11 model with Ultralytics?
To train a YOLOv8 model on the VisDrone dataset for 100 epochs with an image size of 640, you can follow these steps:
To train a YOLO11 model on the VisDrone dataset for 100 epochs with an image size of 640, you can follow these steps:
!!! example "Train Example"
@ -125,7 +125,7 @@ To train a YOLOv8 model on the VisDrone dataset for 100 epochs with an image siz
from ultralytics import YOLO
# Load a pretrained model
model = YOLO("yolov8n.pt")
model = YOLO("yolo11n.pt")
# Train the model
results = model.train(data="VisDrone.yaml", epochs=100, imgsz=640)
@ -135,7 +135,7 @@ To train a YOLOv8 model on the VisDrone dataset for 100 epochs with an image siz
```bash
# Start training from a pretrained *.pt model
yolo detect train data=VisDrone.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=VisDrone.yaml model=yolo11n.pt epochs=100 imgsz=640
```
For additional configuration options, please refer to the model [Training](../../modes/train.md) page.

14
docs/en/datasets/detect/voc.md
View file

@ -39,7 +39,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
## Usage
To train a YOLOv8n model on the VOC dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
To train a YOLO11n model on the VOC dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
!!! example "Train Example"
@ -49,7 +49,7 @@ To train a YOLOv8n model on the VOC dataset for 100 [epochs](https://www.ultraly
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="VOC.yaml", epochs=100, imgsz=640)
@ -59,7 +59,7 @@ To train a YOLOv8n model on the VOC dataset for 100 [epochs](https://www.ultraly
```bash
# Start training from a pretrained *.pt model
yolo detect train data=VOC.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=VOC.yaml model=yolo11n.pt epochs=100 imgsz=640
```
## Sample Images and Annotations
@ -99,9 +99,9 @@ We would like to acknowledge the PASCAL VOC Consortium for creating and maintain
The [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) (Visual Object Classes) dataset is a renowned benchmark for [object detection](https://www.ultralytics.com/glossary/object-detection), segmentation, and classification in computer vision. It includes comprehensive annotations like bounding boxes, class labels, and segmentation masks across 20 different object categories. Researchers use it widely to evaluate the performance of models like Faster R-CNN, YOLO, and Mask R-CNN due to its standardized evaluation metrics such as mean Average Precision (mAP).
### How do I train a YOLOv8 model using the VOC dataset?
### How do I train a YOLO11 model using the VOC dataset?
To train a YOLOv8 model with the VOC dataset, you need the dataset configuration in a YAML file. Here's an example to start training a YOLOv8n model for 100 epochs with an image size of 640:
To train a YOLO11 model with the VOC dataset, you need the dataset configuration in a YAML file. Here's an example to start training a YOLO11n model for 100 epochs with an image size of 640:
!!! example "Train Example"
@ -111,7 +111,7 @@ To train a YOLOv8 model with the VOC dataset, you need the dataset configuration
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="VOC.yaml", epochs=100, imgsz=640)
@ -121,7 +121,7 @@ To train a YOLOv8 model with the VOC dataset, you need the dataset configuration
```bash
# Start training from a pretrained *.pt model
yolo detect train data=VOC.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=VOC.yaml model=yolo11n.pt epochs=100 imgsz=640
```
### What are the primary challenges included in the VOC dataset?

8
docs/en/datasets/detect/xview.md
View file

@ -52,7 +52,7 @@ To train a model on the xView dataset for 100 [epochs](https://www.ultralytics.c
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="xView.yaml", epochs=100, imgsz=640)
@ -62,7 +62,7 @@ To train a model on the xView dataset for 100 [epochs](https://www.ultralytics.c
```bash
# Start training from a pretrained *.pt model
yolo detect train data=xView.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=xView.yaml model=yolo11n.pt epochs=100 imgsz=640
```
## Sample Data and Annotations
@ -114,7 +114,7 @@ To train a model on the xView dataset using Ultralytics YOLO, follow these steps
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="xView.yaml", epochs=100, imgsz=640)
@ -125,7 +125,7 @@ To train a model on the xView dataset using Ultralytics YOLO, follow these steps
```bash
# Start training from a pretrained *.pt model
yolo detect train data=xView.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=xView.yaml model=yolo11n.pt epochs=100 imgsz=640
```
For detailed arguments and settings, refer to the model [Training](../../modes/train.md) page.