Fix YOLO11 YouTube link (#16542)
Signed-off-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
This commit is contained in:
Glenn Jocher
GitHub
parent
6e43d1e1e5
commit
ccee8533c1
10 changed files with 95 additions and 95 deletions
|
|
@ -8,26 +8,26 @@ keywords: YOLO11, state-of-the-art object detection, YOLO series, Ultralytics, c
|
|||
|
||||
## Overview
|
||||
|
||||
YOLO11 is the latest iteration in the Ultralytics YOLO series of real-time object detectors, redefining what's possible with cutting-edge accuracy, speed, and efficiency. Building upon the impressive advancements of previous YOLO versions, YOLO11 introduces significant improvements in architecture and training methods, making it a versatile choice for a wide range of computer vision tasks.
|
||||
YOLO11 is the latest iteration in the [Ultralytics](https://www.ultralytics.com) YOLO series of real-time object detectors, redefining what's possible with cutting-edge [accuracy](https://www.ultralytics.com/glossary/accuracy), speed, and efficiency. Building upon the impressive advancements of previous YOLO versions, YOLO11 introduces significant improvements in architecture and training methods, making it a versatile choice for a wide range of [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) tasks.
|
||||
|
||||
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/live/rfI5vOo3-_A?si=pRdMeLLus0ryYZP7"
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/rfI5vOo3-_A?si=uLCEBVVXwAHiOYqq&start=5500"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> Ultralytics YOLO11 Announcement at #YV24
|
||||
<strong>Watch:</strong> Ultralytics YOLO11 Announcement at [YOLO Vision 2024](https://www.ultralytics.com/events/yolovision)
|
||||
</p>
|
||||
|
||||
## Key Features
|
||||
|
||||
- **Enhanced Feature Extraction:** YOLO11 employs an improved backbone and neck architecture, which enhances [feature extraction](https://www.ultralytics.com/glossary/feature-extraction) capabilities for more precise object detection and complex task performance.
|
||||
- **Optimized for Efficiency and Speed:** YOLO11 introduces refined architectural designs and optimized training pipelines, delivering faster processing speeds and maintaining an optimal balance between accuracy and performance.
|
||||
- **Greater Accuracy with Fewer Parameters:** With advancements in model design, YOLO11m achieves a higher mean Average Precision (mAP) on the COCO dataset while using 22% fewer parameters than YOLOv8m, making it computationally efficient without compromising accuracy.
|
||||
- **Greater Accuracy with Fewer Parameters:** With advancements in model design, YOLO11m achieves a higher [mean Average Precision](https://www.ultralytics.com/glossary/mean-average-precision-map) (mAP) on the COCO dataset while using 22% fewer parameters than YOLOv8m, making it computationally efficient without compromising accuracy.
|
||||
- **Adaptability Across Environments:** YOLO11 can be seamlessly deployed across various environments, including edge devices, cloud platforms, and systems supporting NVIDIA GPUs, ensuring maximum flexibility.
|
||||
- **Broad Range of Supported Tasks:** Whether it's object detection, instance segmentation, image classification, pose estimation, or oriented object detection (OBB), YOLO11 is designed to cater to a diverse set of computer vision challenges.
|
||||
|
||||
|
|
@ -53,7 +53,7 @@ This table provides an overview of the YOLO11 model variants, showcasing their a
|
|||
|
||||
See [Detection Docs](../tasks/detect.md) for usage examples with these models trained on [COCO](../datasets/detect/coco.md), which include 80 pre-trained classes.
|
||||
|
||||
| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>Tesla T4 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) |
|
||||
| ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | --------------------------------------- | ------------------ | ----------------- |
|
||||
| [YOLO11n](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt) | 640 | 39.5 | 56.12 ± 0.82 ms | 1.55 ± 0.01 ms | 2.6 | 6.5 |
|
||||
| [YOLO11s](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt) | 640 | 47.0 | 90.01 ± 1.17 ms | 2.46 ± 0.00 ms | 9.4 | 21.5 |
|
||||
|
|
@ -65,7 +65,7 @@ This table provides an overview of the YOLO11 model variants, showcasing their a
|
|||
|
||||
See [Segmentation Docs](../tasks/segment.md) for usage examples with these models trained on [COCO](../datasets/segment/coco.md), which include 80 pre-trained classes.
|
||||
|
||||
| Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>Tesla T4 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
|
||||
| Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<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) |
|
||||
| -------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | --------------------------------------- | ------------------ | ----------------- |
|
||||
| [YOLO11n-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-seg.pt) | 640 | 38.9 | 32.0 | 65.90 ± 1.14 ms | 1.84 ± 0.00 ms | 2.9 | 10.4 |
|
||||
| [YOLO11s-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-seg.pt) | 640 | 46.6 | 37.8 | 117.56 ± 4.89 ms | 2.94 ± 0.01 ms | 10.1 | 35.5 |
|
||||
|
|
@ -77,7 +77,7 @@ This table provides an overview of the YOLO11 model variants, showcasing their a
|
|||
|
||||
See [Classification Docs](../tasks/classify.md) for usage examples with these models trained on [ImageNet](../datasets/classify/imagenet.md), which include 1000 pre-trained classes.
|
||||
|
||||
| Model | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>Tesla T4 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) at 640 |
|
||||
| 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 | 5.03 ± 0.32 ms | 1.10 ± 0.01 ms | 1.6 | 3.3 |
|
||||
| [YOLO11s-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-cls.pt) | 224 | 75.4 | 92.7 | 7.89 ± 0.18 ms | 1.34 ± 0.01 ms | 5.5 | 12.1 |
|
||||
|
|
@ -89,7 +89,7 @@ This table provides an overview of the YOLO11 model variants, showcasing their a
|
|||
|
||||
See [Pose Estimation Docs](../tasks/pose.md) for usage examples with these models trained on [COCO](../datasets/pose/coco.md), which include 1 pre-trained class, 'person'.
|
||||
|
||||
| Model | size<br><sup>(pixels) | mAP<sup>pose<br>50-95 | mAP<sup>pose<br>50 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>Tesla T4 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
|
||||
| Model | size<br><sup>(pixels) | mAP<sup>pose<br>50-95 | mAP<sup>pose<br>50 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
|
||||
| ---------------------------------------------------------------------------------------------- | --------------------- | --------------------- | ------------------ | ------------------------------ | --------------------------------------- | ------------------ | ----------------- |
|
||||
| [YOLO11n-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-pose.pt) | 640 | 50.0 | 81.0 | 52.40 ± 0.51 ms | 1.72 ± 0.01 ms | 2.9 | 7.6 |
|
||||
| [YOLO11s-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-pose.pt) | 640 | 58.9 | 86.3 | 90.54 ± 0.59 ms | 2.57 ± 0.00 ms | 9.9 | 23.2 |
|
||||
|
|
@ -101,7 +101,7 @@ This table provides an overview of the YOLO11 model variants, showcasing their a
|
|||
|
||||
See [Oriented Detection Docs](../tasks/obb.md) for usage examples with these models trained on [DOTAv1](../datasets/obb/dota-v2.md#dota-v10), which include 15 pre-trained classes.
|
||||
|
||||
| Model | size<br><sup>(pixels) | mAP<sup>test<br>50 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>Tesla T4 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
|
||||
| Model | size<br><sup>(pixels) | mAP<sup>test<br>50 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
|
||||
| -------------------------------------------------------------------------------------------- | --------------------- | ------------------ | ------------------------------ | --------------------------------------- | ------------------ | ----------------- |
|
||||
| [YOLO11n-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-obb.pt) | 1024 | 78.4 | 117.56 ± 0.80 ms | 4.43 ± 0.01 ms | 2.7 | 17.2 |
|
||||
| [YOLO11s-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-obb.pt) | 1024 | 79.5 | 219.41 ± 4.00 ms | 5.13 ± 0.02 ms | 9.7 | 57.5 |
|
||||
|
|
@ -113,7 +113,7 @@ This table provides an overview of the YOLO11 model variants, showcasing their a
|
|||
|
||||
This section provides simple YOLO11 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 that the example below is for YOLO11 [Detect](../tasks/detect.md) models for object detection. For additional supported tasks, see the [Segment](../tasks/segment.md), [Classify](../tasks/classify.md), [OBB](../tasks/obb.md), and [Pose](../tasks/pose.md) docs.
|
||||
Note that the example below is for YOLO11 [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), [OBB](../tasks/obb.md), and [Pose](../tasks/pose.md) docs.
|
||||
|
||||
!!! example
|
||||
|
||||
|
|
@ -176,9 +176,9 @@ Ultralytics YOLO11 introduces several significant advancements over its predeces
|
|||
|
||||
- **Enhanced Feature Extraction:** YOLO11 employs an improved backbone and neck architecture, enhancing [feature extraction](https://www.ultralytics.com/glossary/feature-extraction) capabilities for more precise object detection.
|
||||
- **Optimized Efficiency and Speed:** Refined architectural designs and optimized training pipelines deliver faster processing speeds while maintaining a balance between accuracy and performance.
|
||||
- **Greater Accuracy with Fewer Parameters:** YOLO11m achieves higher mean Average Precision (mAP) on the COCO dataset with 22% fewer parameters than YOLOv8m, making it computationally efficient without compromising accuracy.
|
||||
- **Greater Accuracy with Fewer Parameters:** YOLO11m achieves higher mean Average [Precision](https://www.ultralytics.com/glossary/precision) (mAP) on the COCO dataset with 22% fewer parameters than YOLOv8m, making it computationally efficient without compromising accuracy.
|
||||
- **Adaptability Across Environments:** YOLO11 can be deployed across various environments, including edge devices, cloud platforms, and systems supporting NVIDIA GPUs.
|
||||
- **Broad Range of Supported Tasks:** YOLO11 supports diverse computer vision tasks such as object detection, instance segmentation, image classification, pose estimation, and oriented object detection (OBB).
|
||||
- **Broad Range of Supported Tasks:** YOLO11 supports diverse computer vision tasks such as object detection, [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation), image classification, pose estimation, and oriented object detection (OBB).
|
||||
|
||||
### How do I train a YOLO11 model for object detection?
|
||||
|
||||
|
|
@ -213,7 +213,7 @@ YOLO11 models are versatile and support a wide range of computer vision tasks, i
|
|||
|
||||
- **Object Detection:** Identifying and locating objects within an image.
|
||||
- **Instance Segmentation:** Detecting objects and delineating their boundaries.
|
||||
- **Image Classification:** Categorizing images into predefined classes.
|
||||
- **[Image Classification](https://www.ultralytics.com/glossary/image-classification):** Categorizing images into predefined classes.
|
||||
- **Pose Estimation:** Detecting and tracking keypoints on human bodies.
|
||||
- **Oriented Object Detection (OBB):** Detecting objects with rotation for higher precision.
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue