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docs/en/models/index.md
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@ -17,7 +17,7 @@ Here are some of the key models supported:
3. **[YOLOv5](yolov5.md)**: An improved version of the YOLO architecture by Ultralytics, offering better performance and speed trade-offs compared to previous versions.
4. **[YOLOv6](yolov6.md)**: Released by [Meituan](https://about.meituan.com/) in 2022, and in use in many of the company's autonomous delivery robots.
5. **[YOLOv7](yolov7.md)**: Updated YOLO models released in 2022 by the authors of YOLOv4.
6. **[YOLOv8](yolov8.md) NEW 🚀**: The latest version of the YOLO family, featuring enhanced capabilities such as instance segmentation, pose/keypoints estimation, and classification.
6. **[YOLOv8](yolov8.md) NEW 🚀**: The latest version of the YOLO family, featuring enhanced capabilities such as [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation), pose/keypoints estimation, and classification.
7. **[YOLOv9](yolov9.md)**: An experimental model trained on the Ultralytics [YOLOv5](yolov5.md) codebase implementing Programmable Gradient Information (PGI).
8. **[YOLOv10](yolov10.md)**: By Tsinghua University, featuring NMS-free training and efficiency-accuracy driven architecture, delivering state-of-the-art performance and latency.
9. **[Segment Anything Model (SAM)](sam.md)**: Meta's original Segment Anything Model (SAM).
@ -25,7 +25,7 @@ Here are some of the key models supported:
11. **[Mobile Segment Anything Model (MobileSAM)](mobile-sam.md)**: MobileSAM for mobile applications, by Kyung Hee University.
12. **[Fast Segment Anything Model (FastSAM)](fast-sam.md)**: FastSAM by Image & Video Analysis Group, Institute of Automation, Chinese Academy of Sciences.
13. **[YOLO-NAS](yolo-nas.md)**: YOLO Neural Architecture Search (NAS) Models.
14. **[Realtime Detection Transformers (RT-DETR)](rtdetr.md)**: Baidu's PaddlePaddle Realtime Detection Transformer (RT-DETR) models.
14. **[Realtime Detection Transformers (RT-DETR)](rtdetr.md)**: Baidu's PaddlePaddle Realtime Detection [Transformer](https://www.ultralytics.com/glossary/transformer) (RT-DETR) models.
15. **[YOLO-World](yolo-world.md)**: Real-time Open Vocabulary Object Detection models from Tencent AI Lab.
<p align="center">
@ -43,13 +43,13 @@ Here are some of the key models supported:
This example provides simple YOLO training and inference examples. For full documentation on these and other [modes](../modes/index.md) see the [Predict](../modes/predict.md), [Train](../modes/train.md), [Val](../modes/val.md) and [Export](../modes/export.md) docs pages.
Note the below example is for YOLOv8 [Detect](../tasks/detect.md) models for object detection. For additional supported tasks see the [Segment](../tasks/segment.md), [Classify](../tasks/classify.md) and [Pose](../tasks/pose.md) docs.
Note the below example is for YOLOv8 [Detect](../tasks/detect.md) models for [object detection](https://www.ultralytics.com/glossary/object-detection). For additional supported tasks see the [Segment](../tasks/segment.md), [Classify](../tasks/classify.md) and [Pose](../tasks/pose.md) docs.
!!! example
=== "Python"
PyTorch pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLO()`, `SAM()`, `NAS()` and `RTDETR()` classes to create a model instance in Python:
[PyTorch](https://www.ultralytics.com/glossary/pytorch) pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLO()`, `SAM()`, `NAS()` and `RTDETR()` classes to create a model instance in Python:
```python
from ultralytics import YOLO
@ -101,7 +101,7 @@ For detailed steps, consult our [Contributing Guide](../help/contributing.md).
### What are the key advantages of using Ultralytics YOLOv8 for object detection?
Ultralytics YOLOv8 offers enhanced capabilities such as real-time object detection, instance segmentation, pose estimation, and classification. Its optimized architecture ensures high-speed performance without sacrificing accuracy, making it ideal for a variety of applications. YOLOv8 also includes built-in compatibility with popular datasets and models, as detailed on the [YOLOv8 documentation page](../models/yolov8.md).
Ultralytics YOLOv8 offers enhanced capabilities such as real-time object detection, instance segmentation, pose estimation, and classification. Its optimized architecture ensures high-speed performance without sacrificing [accuracy](https://www.ultralytics.com/glossary/accuracy), making it ideal for a variety of applications. YOLOv8 also includes built-in compatibility with popular datasets and models, as detailed on the [YOLOv8 documentation page](../models/yolov8.md).
### How can I train a YOLOv8 model on custom data?
@ -133,7 +133,7 @@ For more detailed instructions, visit the [Train](../modes/train.md) documentati
Ultralytics supports a comprehensive range of YOLO (You Only Look Once) versions from YOLOv3 to YOLOv10, along with models like NAS, SAM, and RT-DETR. Each version is optimized for various tasks such as detection, segmentation, and classification. For detailed information on each model, refer to the [Models Supported by Ultralytics](../models/index.md) documentation.
### Why should I use Ultralytics HUB for machine learning projects?
### Why should I use Ultralytics HUB for [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) projects?
Ultralytics HUB provides a no-code, end-to-end platform for training, deploying, and managing YOLO models. It simplifies complex workflows, enabling users to focus on model performance and application. The HUB also offers cloud training capabilities, comprehensive dataset management, and user-friendly interfaces. Learn more about it on the [Ultralytics HUB](../hub/index.md) documentation page.