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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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docs/en/datasets/classify/imagenet.md
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@ -21,7 +21,7 @@ The ImageNet dataset is organized using the WordNet hierarchy. Each node in the
## ImageNet Large Scale Visual Recognition Challenge (ILSVRC)
The annual [ImageNet Large Scale Visual Recognition Challenge (ILSVRC)](http://image-net.org/challenges/LSVRC/) has been an important event in the field of computer vision. It has provided a platform for researchers and developers to evaluate their algorithms and models on a large-scale dataset with standardized evaluation metrics. The ILSVRC has led to significant advancements in the development of deep learning models for image classification, object detection, and other computer vision tasks.
The annual [ImageNet Large Scale Visual Recognition Challenge (ILSVRC)](https://image-net.org/challenges/LSVRC/) has been an important event in the field of computer vision. It has provided a platform for researchers and developers to evaluate their algorithms and models on a large-scale dataset with standardized evaluation metrics. The ILSVRC has led to significant advancements in the development of deep learning models for image classification, object detection, and other computer vision tasks.
## Applications

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docs/en/datasets/classify/imagenette.md
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@ -6,7 +6,7 @@ keywords: ImageNette dataset, Ultralytics, YOLO, Image classification, Machine L
# ImageNette Dataset
The [ImageNette](https://github.com/fastai/imagenette) dataset is a subset of the larger [Imagenet](http://www.image-net.org/) dataset, but it only includes 10 easily distinguishable classes. It was created to provide a quicker, easier-to-use version of Imagenet for software development and education.
The [ImageNette](https://github.com/fastai/imagenette) dataset is a subset of the larger [Imagenet](https://www.image-net.org/) dataset, but it only includes 10 easily distinguishable classes. It was created to provide a quicker, easier-to-use version of Imagenet for software development and education.
## Key Features

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docs/en/datasets/detect/globalwheat2020.md
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@ -6,7 +6,7 @@ keywords: Ultralytics, YOLO, Global Wheat Head Dataset, wheat head detection, pl
# Global Wheat Head Dataset
The [Global Wheat Head Dataset](http://www.global-wheat.com/) is a collection of images designed to support the development of accurate wheat head detection models for applications in wheat phenotyping and crop management. Wheat heads, also known as spikes, are the grain-bearing parts of the wheat plant. Accurate estimation of wheat head density and size is essential for assessing crop health, maturity, and yield potential. The dataset, created by a collaboration of nine research institutes from seven countries, covers multiple growing regions to ensure models generalize well across different environments.
The [Global Wheat Head Dataset](https://www.global-wheat.com/) is a collection of images designed to support the development of accurate wheat head detection models for applications in wheat phenotyping and crop management. Wheat heads, also known as spikes, are the grain-bearing parts of the wheat plant. Accurate estimation of wheat head density and size is essential for assessing crop health, maturity, and yield potential. The dataset, created by a collaboration of nine research institutes from seven countries, covers multiple growing regions to ensure models generalize well across different environments.
## Key Features
@ -88,4 +88,4 @@ If you use the Global Wheat Head Dataset in your research or development work, p
}
```
We would like to acknowledge the researchers and institutions that contributed to the creation and maintenance of the Global Wheat Head Dataset as a valuable resource for the plant phenotyping and crop management research community. For more information about the dataset and its creators, visit the [Global Wheat Head Dataset website](http://www.global-wheat.com/).
We would like to acknowledge the researchers and institutions that contributed to the creation and maintenance of the Global Wheat Head Dataset as a valuable resource for the plant phenotyping and crop management research community. For more information about the dataset and its creators, visit the [Global Wheat Head Dataset website](https://www.global-wheat.com/).

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docs/en/robots.txt
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@ -1,12 +1,12 @@
User-agent: *
Sitemap: http://docs.ultralytics.com/sitemap.xml
Sitemap: http://docs.ultralytics.com/ar/sitemap.xml
Sitemap: http://docs.ultralytics.com/de/sitemap.xml
Sitemap: http://docs.ultralytics.com/es/sitemap.xml
Sitemap: http://docs.ultralytics.com/fr/sitemap.xml
Sitemap: http://docs.ultralytics.com/hi/sitemap.xml
Sitemap: http://docs.ultralytics.com/ja/sitemap.xml
Sitemap: http://docs.ultralytics.com/ko/sitemap.xml
Sitemap: http://docs.ultralytics.com/pt/sitemap.xml
Sitemap: http://docs.ultralytics.com/ru/sitemap.xml
Sitemap: http://docs.ultralytics.com/zh/sitemap.xml
Sitemap: https://docs.ultralytics.com/sitemap.xml
Sitemap: https://docs.ultralytics.com/ar/sitemap.xml
Sitemap: https://docs.ultralytics.com/de/sitemap.xml
Sitemap: https://docs.ultralytics.com/es/sitemap.xml
Sitemap: https://docs.ultralytics.com/fr/sitemap.xml
Sitemap: https://docs.ultralytics.com/hi/sitemap.xml
Sitemap: https://docs.ultralytics.com/ja/sitemap.xml
Sitemap: https://docs.ultralytics.com/ko/sitemap.xml
Sitemap: https://docs.ultralytics.com/pt/sitemap.xml
Sitemap: https://docs.ultralytics.com/ru/sitemap.xml
Sitemap: https://docs.ultralytics.com/zh/sitemap.xml

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docs/en/tasks/detect.md
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@ -41,7 +41,7 @@ YOLOv8 pretrained Detect models are shown here. Detect, Segment and Pose models
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8l.pt) | 640 | 52.9 | 375.2 | 2.39 | 43.7 | 165.2 |
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8x.pt) | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 |
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset. <br>Reproduce by `yolo val detect data=coco.yaml device=0`
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](https://cocodataset.org) dataset. <br>Reproduce by `yolo val detect data=coco.yaml device=0`
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val detect data=coco128.yaml batch=1 device=0|cpu`
## Train

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docs/en/tasks/pose.md
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@ -42,7 +42,7 @@ YOLOv8 pretrained Pose models are shown here. Detect, Segment and Pose models ar
| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8x-pose.pt) | 640 | 69.2 | 90.2 | 1607.1 | 3.73 | 69.4 | 263.2 |
| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8x-pose-p6.pt) | 1280 | 71.6 | 91.2 | 4088.7 | 10.04 | 99.1 | 1066.4 |
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO Keypoints val2017](http://cocodataset.org) dataset. <br>Reproduce by `yolo val pose data=coco-pose.yaml device=0`
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO Keypoints val2017](https://cocodataset.org) dataset. <br>Reproduce by `yolo val pose data=coco-pose.yaml device=0`
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val pose data=coco8-pose.yaml batch=1 device=0|cpu`
## Train

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docs/en/tasks/segment.md
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@ -41,7 +41,7 @@ YOLOv8 pretrained Segment models are shown here. Detect, Segment and Pose models
| [YOLOv8l-seg](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8l-seg.pt) | 640 | 52.3 | 42.6 | 572.4 | 2.79 | 46.0 | 220.5 |
| [YOLOv8x-seg](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8x-seg.pt) | 640 | 53.4 | 43.4 | 712.1 | 4.02 | 71.8 | 344.1 |
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset. <br>Reproduce by `yolo val segment data=coco.yaml device=0`
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](https://cocodataset.org) dataset. <br>Reproduce by `yolo val segment data=coco.yaml device=0`
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val segment data=coco128-seg.yaml batch=1 device=0|cpu`
## Train

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docs/en/yolov5/tutorials/tips_for_best_training_results.md
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@ -60,6 +60,6 @@ Before modifying anything, **first train with default settings to establish a pe
## Further Reading
If you'd like to know more, a good place to start is Karpathy's 'Recipe for Training Neural Networks', which has great ideas for training that apply broadly across all ML domains: [http://karpathy.github.io/2019/04/25/recipe/](http://karpathy.github.io/2019/04/25/recipe/)
If you'd like to know more, a good place to start is Karpathy's 'Recipe for Training Neural Networks', which has great ideas for training that apply broadly across all ML domains: [https://karpathy.github.io/2019/04/25/recipe/](https://karpathy.github.io/2019/04/25/recipe/)
Good luck 🍀 and let us know if you have any other questions!

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docs/en/yolov5/tutorials/train_custom_data.md
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@ -77,7 +77,7 @@ Export in `YOLOv5 Pytorch` format, then copy the snippet into your training scri
### 2.1 Create `dataset.yaml`
[COCO128](https://www.kaggle.com/ultralytics/coco128) is an example small tutorial dataset composed of the first 128 images in [COCO](http://cocodataset.org/#home) train2017. These same 128 images are used for both training and validation to verify our training pipeline is capable of overfitting. [data/coco128.yaml](https://github.com/ultralytics/yolov5/blob/master/data/coco128.yaml), shown below, is the dataset config file that defines 1) the dataset root directory `path` and relative paths to `train` / `val` / `test` image directories (or *.txt files with image paths) and 2) a class `names` dictionary:
[COCO128](https://www.kaggle.com/ultralytics/coco128) is an example small tutorial dataset composed of the first 128 images in [COCO](https://cocodataset.org/) train2017. These same 128 images are used for both training and validation to verify our training pipeline is capable of overfitting. [data/coco128.yaml](https://github.com/ultralytics/yolov5/blob/master/data/coco128.yaml), shown below, is the dataset config file that defines 1) the dataset root directory `path` and relative paths to `train` / `val` / `test` image directories (or *.txt files with image paths) and 2) a class `names` dictionary:
```yaml
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]