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ultralytics 8.0.178 PyPI deployment fix (#4891)

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Co-authored-by: Eduardo Farinati <afxph8fc@duck.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Glenn Jocher 2023-09-14 01:31:46 +02:00 • committed by GitHub
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12
docs/modes/track.md
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@ -138,7 +138,7 @@ For a comprehensive list of tracking arguments, refer to the [ultralytics/cfg/tr
### Persisting Tracks Loop
Here is a Python script using OpenCV (`cv2`) and YOLOv8 to run object tracking on video frames. This script still assumes you have already installed the necessary packages (`opencv-python` and `ultralytics`).
Here is a Python script using OpenCV (`cv2`) and YOLOv8 to run object tracking on video frames. This script still assumes you have already installed the necessary packages (`opencv-python` and `ultralytics`). The `persist=True` argument tells the tracker than the current image or frame is the next in a sequence and to expect tracks from the previous image in the current image.
!!! example "Streaming for-loop with tracking"
@ -312,3 +312,13 @@ Finally, after all threads have completed their task, the windows displaying the
```
This example can easily be extended to handle more video files and models by creating more threads and applying the same methodology.
## Contribute New Trackers
Are you proficient in multi-object tracking and have successfully implemented or adapted a tracking algorithm with Ultralytics YOLO? We invite you to contribute to our Trackers section in [ultralytics/cfg/trackers](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/trackers)! Your real-world applications and solutions could be invaluable for users working on tracking tasks.
By contributing to this section, you help expand the scope of tracking solutions available within the Ultralytics YOLO framework, adding another layer of functionality and utility for the community.
To initiate your contribution, please refer to our [Contributing Guide](https://docs.ultralytics.com/help/contributing) for comprehensive instructions on submitting a Pull Request (PR) 🛠️. We are excited to see what you bring to the table!
Together, let's enhance the tracking capabilities of the Ultralytics YOLO ecosystem 🙏!

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docs/modes/train.md
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@ -189,7 +189,7 @@ Training settings for YOLO models refer to the various hyperparameters and confi
| `project` | `None` | project name |
| `name` | `None` | experiment name |
| `exist_ok` | `False` | whether to overwrite existing experiment |
| `pretrained` | `False` | whether to use a pretrained model |
| `pretrained` | `True` | (bool | str) whether to use a pretrained model (bool) or a model to load weights from (str) |
| `optimizer` | `'auto'` | optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto] |
| `verbose` | `False` | whether to print verbose output |
| `seed` | `0` | random seed for reproducibility |

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docs/usage/cfg.md
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@ -88,7 +88,7 @@ The training settings for YOLO models encompass various hyperparameters and conf
| `project` | `None` | project name |
| `name` | `None` | experiment name |
| `exist_ok` | `False` | whether to overwrite existing experiment |
| `pretrained` | `False` | whether to use a pretrained model |
| `pretrained` | `True` | (bool | str) whether to use a pretrained model (bool) or a model to load weights from (str) |
| `optimizer` | `'auto'` | optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto] |
| `verbose` | `False` | whether to print verbose output |
| `seed` | `0` | random seed for reproducibility |