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Docs improvements and redirect fixes (#16287)

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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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docs/en/datasets/index.md
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@ -123,7 +123,7 @@ Contributing a new dataset involves several steps to ensure that it aligns well
5. **Create a `data.yaml` File**: In your dataset's root directory, create a `data.yaml` file that describes the dataset, classes, and other necessary information.
6. **Optimize Images (Optional)**: If you want to reduce the size of the dataset for more efficient processing, you can optimize the images using the code below. This is not required, but recommended for smaller dataset sizes and faster download speeds.
7. **Zip Dataset**: Compress the entire dataset folder into a zip file.
8. **Document and PR**: Create a documentation page describing your dataset and how it fits into the existing framework. After that, submit a Pull Request (PR). Refer to [Ultralytics Contribution Guidelines](https://docs.ultralytics.com/help/contributing) for more details on how to submit a PR.
8. **Document and PR**: Create a documentation page describing your dataset and how it fits into the existing framework. After that, submit a Pull Request (PR). Refer to [Ultralytics Contribution Guidelines](https://docs.ultralytics.com/help/contributing/) for more details on how to submit a PR.
### Example Code to Optimize and Zip a Dataset
@ -175,7 +175,7 @@ Contributing a new dataset involves several steps:
5. **Create a `data.yaml` File**: Include dataset descriptions, classes, and other relevant information.
6. **Optimize Images (Optional)**: Reduce dataset size for efficiency.
7. **Zip Dataset**: Compress the dataset into a zip file.
8. **Document and PR**: Describe your dataset and submit a Pull Request following [Ultralytics Contribution Guidelines](https://docs.ultralytics.com/help/contributing).
8. **Document and PR**: Describe your dataset and submit a Pull Request following [Ultralytics Contribution Guidelines](https://docs.ultralytics.com/help/contributing/).
Visit [Contribute New Datasets](#contribute-new-datasets) for a comprehensive guide.

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docs/en/datasets/segment/coco8-seg.md
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@ -113,7 +113,7 @@ For a thorough explanation of available arguments and configuration options, you
### Why is the COCO8-Seg dataset important for model development and debugging?
The **COCO8-Seg dataset** is ideal for its manageability and diversity within a small size. It consists of only 8 images, providing a quick way to test and debug segmentation models or new detection approaches without the overhead of larger datasets. This makes it an efficient tool for sanity checks and pipeline error identification before committing to extensive training on large datasets. Learn more about dataset formats [here](https://docs.ultralytics.com/datasets/segment).
The **COCO8-Seg dataset** is ideal for its manageability and diversity within a small size. It consists of only 8 images, providing a quick way to test and debug segmentation models or new detection approaches without the overhead of larger datasets. This makes it an efficient tool for sanity checks and pipeline error identification before committing to extensive training on large datasets. Learn more about dataset formats [here](https://docs.ultralytics.com/datasets/segment/).
### Where can I find the YAML configuration file for the COCO8-Seg dataset?

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docs/en/datasets/segment/package-seg.md
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@ -136,7 +136,7 @@ This structure ensures a balanced dataset for thorough model training, validatio
### Why should I use Ultralytics YOLOv8 with the Package Segmentation Dataset?
Ultralytics YOLOv8 provides state-of-the-art accuracy and speed for real-time object detection and segmentation tasks. Using it with the Package Segmentation Dataset allows you to leverage YOLOv8's capabilities for precise package segmentation. This combination is especially beneficial for industries like logistics and warehouse automation, where accurate package identification is critical. For more information, check out our [page on YOLOv8 segmentation](https://docs.ultralytics.com/models/yolov8).
Ultralytics YOLOv8 provides state-of-the-art accuracy and speed for real-time object detection and segmentation tasks. Using it with the Package Segmentation Dataset allows you to leverage YOLOv8's capabilities for precise package segmentation. This combination is especially beneficial for industries like logistics and warehouse automation, where accurate package identification is critical. For more information, check out our [page on YOLOv8 segmentation](https://docs.ultralytics.com/models/yolov8/).
### How can I access and use the package-seg.yaml file for the Package Segmentation Dataset?