Repository: ultralytics/ultralytics

Issue:
Possible typo in `select_highest_overlaps` function doc
https://github.com/ultralytics/ultralytics/blob/2d75f72598d5ce80786be61f140673a6d1dd7e57/ultralytics/utils/tal.py#L234

The doc states:
> ...the highest **IoI** will be selected

Shouldn't this be IOU?


Discussion:
👋 Hello @deanmark, thank you for your interest in Ultralytics YOLOv8 🚀! We recommend a visit to the [Docs](https://docs.ultralytics.com) for new users where you can find many [Python](https://docs.ultralytics.com/usage/python/) and [CLI](https://docs.ultralytics.com/usage/cli/) usage examples and where many of the most common questions may already be answered.

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## Install

Pip install the `ultralytics` package including all [requirements](https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml) in a [**Python>=3.8**](https://www.python.org/) environment with [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/).

```bash
pip install ultralytics
```

## Environments

YOLOv8 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):

- **Notebooks** with free GPU: <a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"/></a> <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/ultralytics/yolov8"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)
- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)
- **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) <a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Docker Pulls"></a>

## Status

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If this badge is green, all [Ultralytics CI](https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml?query=event%3Aschedule) tests are currently passing. CI tests verify correct operation of all YOLOv8 [Modes](https://docs.ultralytics.com/modes/) and [Tasks](https://docs.ultralytics.com/tasks/) on macOS, Windows, and Ubuntu every 24 hours and on every commit.
@deanmark thank you for pointing this out! 🙌 You're absolutely right, it should indeed be IOU (Intersection over Union) rather than IoI. We appreciate your keen eye on this, and we'll update the documentation to correct this typo. Here's a quick fix for clarity:

```python
def select_highest_overlaps(...):
    """
    ...the highest IOU will be selected
    """
```

Thanks again for your contribution to making YOLOv8 even better! If you spot anything else, please don't hesitate to let us know.

Resolving patch:
diff --git a/ultralytics/utils/tal.py b/ultralytics/utils/tal.py
--- a/ultralytics/utils/tal.py
+++ b/ultralytics/utils/tal.py
@@ -231,7 +231,7 @@ def select_candidates_in_gts(xy_centers, gt_bboxes, eps=1e-9):
     @staticmethod
     def select_highest_overlaps(mask_pos, overlaps, n_max_boxes):
         """
-        If an anchor box is assigned to multiple gts, the one with the highest IoI will be selected.
+        If an anchor box is assigned to multiple gts, the one with the highest IoU will be selected.
 
         Args:
             mask_pos (Tensor): shape(b, n_max_boxes, h*w)


Test patch: