@ -59,7 +59,9 @@ To allow your work to be integrated as seamlessly as possible, we advise you to:
### Docstrings
Not all functions or classes require docstrings but when they do, we follow [google-style docstrings format](https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings). Here is an example:
Not all functions or classes require docstrings but when they do, we
@ -222,7 +222,10 @@ See [Classification Docs](https://docs.ultralytics.com/tasks/classification/) fo
## <divalign="center">Ultralytics HUB</div>
Experience seamless AI with [Ultralytics HUB](https://bit.ly/ultralytics_hub) ⭐, the all-in-one solution for data visualization, YOLOv5 and YOLOv8 (coming soon) 🚀 model training and deployment, without any coding. Transform images into actionable insights and bring your AI visions to life with ease using our cutting-edge platform and user-friendly [Ultralytics App](https://ultralytics.com/app_install). Start your journey for **Free** now!
Experience seamless AI with [Ultralytics HUB](https://bit.ly/ultralytics_hub) ⭐, the all-in-one solution for data
visualization, YOLOv5 and YOLOv8 (coming soon) 🚀 model training and deployment, without any coding. Transform images
into actionable insights and bring your AI visions to life with ease using our cutting-edge platform and
user-friendly [Ultralytics App](https://ultralytics.com/app_install). Start your journey for **Free** now!
@ -250,7 +253,9 @@ YOLOv8 is available under two different licenses:
## <divalign="center">Contact</div>
For YOLOv8 bug reports and feature requests please visit [GitHub Issues](https://github.com/ultralytics/ultralytics/issues) or the [Ultralytics Community Forum](https://community.ultralytics.com/).
For YOLOv8 bug reports and feature requests please
visit [GitHub Issues](https://github.com/ultralytics/ultralytics/issues) or
the [Ultralytics Community Forum](https://community.ultralytics.com/).
At [Ultralytics](https://ultralytics.com), the security of our users' data and systems is of utmost importance. To ensure the safety and security of our [open-source projects](https://github.com/ultralytics), we have implemented several measures to detect and prevent security vulnerabilities.
At [Ultralytics](https://ultralytics.com), the security of our users' data and systems is of utmost importance. To
ensure the safety and security of our [open-source projects](https://github.com/ultralytics), we have implemented
several measures to detect and prevent security vulnerabilities.
We use [Snyk](https://snyk.io/advisor/python/ultralytics) to regularly scan the YOLOv8 repository for vulnerabilities and security issues. Our goal is to identify and remediate any potential threats as soon as possible, to minimize any risks to our users.
We use [Snyk](https://snyk.io/advisor/python/ultralytics) to regularly scan the YOLOv8 repository for vulnerabilities
and security issues. Our goal is to identify and remediate any potential threats as soon as possible, to minimize any
risks to our users.
## GitHub CodeQL Scanning
In addition to our Snyk scans, we also use GitHub's [CodeQL](https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/about-code-scanning-with-codeql) scans to proactively identify and address security vulnerabilities.
scans to proactively identify and address security vulnerabilities.
## Reporting Security Issues
If you suspect or discover a security vulnerability in the YOLOv8 repository, please let us know immediately. You can reach out to us directly via our [contact form](https://ultralytics.com/contact) or via [security@ultralytics.com](mailto:security@ultralytics.com). Our security team will investigate and respond as soon as possible.
If you suspect or discover a security vulnerability in the YOLOv8 repository, please let us know immediately. You can
reach out to us directly via our [contact form](https://ultralytics.com/contact) or
via [security@ultralytics.com](mailto:security@ultralytics.com). Our security team will investigate and respond as soon
as possible.
We appreciate your help in keeping the YOLOv8 repository secure and safe for everyone.
Ultralytics framework supports callbacks as entry points in strategic stages of train, val, export, and predict modes. Each callback accepts a `Trainer`, `Validator`, or `Predictor` object depending on the operation type. All properties of these objects can be found in Reference section of the docs.
Ultralytics framework supports callbacks as entry points in strategic stages of train, val, export, and predict modes.
Each callback accepts a `Trainer`, `Validator`, or `Predictor` object depending on the operation type. All properties of
these objects can be found in Reference section of the docs.
## Examples
### Returning additional information with Prediction
In this example, we want to return the original frame with each result object. Here's how we can do that
```python
def on_predict_batch_end(predictor):
# results -> List[batch_size]
@ -19,8 +24,11 @@ for (result, frame) in model.track/predict():
```
## All callbacks
Here are all supported callbacks.
### Trainer
`on_pretrain_routine_start`
`on_pretrain_routine_end`
@ -50,6 +58,7 @@ Here are all supported callbacks.
`teardown`
### Validator
`on_val_start`
`on_val_batch_start`
@ -59,6 +68,7 @@ Here are all supported callbacks.
`on_val_end`
### Predictor
`on_predict_start`
`on_predict_batch_start`
@ -70,6 +80,7 @@ Here are all supported callbacks.
@ -96,12 +96,15 @@ Class reference documentation for `Results` module and its components can be fou
## Visualizing results
You can use `visualize()` function of `Result` object to get a visualization. It plots all components(boxes, masks, classification logits, etc) found in the results object
You can use `visualize()` function of `Result` object to get a visualization. It plots all components(boxes, masks,
classification logits, etc) found in the results object
This repository is based on OpenCVs dnn API to run an ONNX exported model of either yolov5/yolov8 (In theory should work for yolov6 and yolov7 but not tested). Note that for this example the networks are exported as rectangular (640x480) resolutions, but it would work for any resolution that you export as although you might want to use the letterBox approach for square images depending on your use-case.
This repository is based on OpenCVs dnn API to run an ONNX exported model of either yolov5/yolov8 (In theory should work
for yolov6 and yolov7 but not tested). Note that for this example the networks are exported as rectangular (640x480)
resolutions, but it would work for any resolution that you export as although you might want to use the letterBox
approach for square images depending on your use-case.
The **main** branch version is based on using Qt as a GUI wrapper the main interest here is the **Inference** class file which shows how to transpose yolov8 models to work as yolov5 models.
The **main** branch version is based on using Qt as a GUI wrapper the main interest here is the **Inference** class file
which shows how to transpose yolov8 models to work as yolov5 models.
Glenn Jocher
2 years ago
committed by
GitHub