ultralytics 8.0.41 TF SavedModel and EdgeTPU export (#1034)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Noobtoss <96134731+Noobtoss@users.noreply.github.com> Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
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Glenn Jocher
GitHub
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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.
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At [Ultralytics](https://ultralytics.com), the security of our users' data and systems is of utmost importance. To
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ensure the safety and security of our [open-source projects](https://github.com/ultralytics), we have implemented
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several measures to detect and prevent security vulnerabilities.
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[](https://snyk.io/advisor/python/ultralytics)
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## Snyk Scanning
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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.
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We use [Snyk](https://snyk.io/advisor/python/ultralytics) to regularly scan the YOLOv8 repository for vulnerabilities
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and security issues. Our goal is to identify and remediate any potential threats as soon as possible, to minimize any
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risks to our users.
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## GitHub CodeQL Scanning
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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.
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In addition to our Snyk scans, we also use
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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)
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scans to proactively identify and address security vulnerabilities.
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## Reporting Security Issues
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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.
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If you suspect or discover a security vulnerability in the YOLOv8 repository, please let us know immediately. You can
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reach out to us directly via our [contact form](https://ultralytics.com/contact) or
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via [security@ultralytics.com](mailto:security@ultralytics.com). Our security team will investigate and respond as soon
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as possible.
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We appreciate your help in keeping the YOLOv8 repository secure and safe for everyone.
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<br>
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Welcome to the Ultralytics HUB app for demonstrating YOLOv5 and YOLOv8 models! In this app, available on the [Apple App
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Store](https://apps.apple.com/xk/app/ultralytics/id1583935240) and the
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Store](https://apps.apple.com/xk/app/ultralytics/id1583935240) and the
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[Google Play Store](https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app), you will be able
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to see the power and capabilities of YOLOv5, a state-of-the-art object detection model developed by Ultralytics.
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## Callbacks
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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.
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Ultralytics framework supports callbacks as entry points in strategic stages of train, val, export, and predict modes.
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Each callback accepts a `Trainer`, `Validator`, or `Predictor` object depending on the operation type. All properties of
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these objects can be found in Reference section of the docs.
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## Examples
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### Returning additional information with Prediction
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In this example, we want to return the original frame with each result object. Here's how we can do that
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```python
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def on_predict_batch_end(predictor):
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# results -> List[batch_size]
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```
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## All callbacks
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Here are all supported callbacks.
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### Trainer
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`on_pretrain_routine_start`
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`on_pretrain_routine_end`
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`teardown`
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### Validator
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`on_val_start`
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`on_val_batch_start`
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`on_val_end`
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### Predictor
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`on_predict_start`
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`on_predict_batch_start`
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`on_predict_end`
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### Exporter
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`on_export_start`
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`on_export_end`
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yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640
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yolo detect train resume model=last.pt # resume training
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```
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## Val
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Validate trained YOLOv8n model accuracy on the COCO128 dataset. No argument need to passed as the `model` retains it's
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training `data` and arguments as model attributes.
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!!! example ""
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```bash
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yolo detect val model=yolov8n.pt # val official model
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yolo detect val model=path/to/best.pt # val custom model
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```
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## Predict
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Use a trained YOLOv8n model to run predictions on images.
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yolo detect predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model
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yolo detect predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model
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```
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## Export
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Export a YOLOv8n model to a different format like ONNX, CoreML, etc.
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!!! example ""
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```bash
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yolo export model=yolov8n.pt format=onnx # export official model
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yolo export model=path/to/best.pt format=onnx # export custom trained model
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## 2. Train a Model
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Connect to the Ultralytics HUB notebook and use your model API key to begin training!
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Connect to the Ultralytics HUB notebook and use your model API key to begin training!
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<a href="https://colab.research.google.com/github/ultralytics/hub/blob/master/hub.ipynb" target="_blank">
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<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
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## 3. Deploy to Real World
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Export your model to 13 different formats, including TensorFlow, ONNX, OpenVINO, CoreML, Paddle and many others. Run
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models directly on your [iOS](https://apps.apple.com/xk/app/ultralytics/id1583935240) or
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[Android](https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app) mobile device by downloading
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models directly on your [iOS](https://apps.apple.com/xk/app/ultralytics/id1583935240) or
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[Android](https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app) mobile device by downloading
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the [Ultralytics App](https://ultralytics.com/app_install)!
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## ❓ Issues
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## Visualizing results
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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
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You can use `visualize()` function of `Result` object to get a visualization. It plots all components(boxes, masks,
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classification logits, etc) found in the results object
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```python
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res = model(img)
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res_plotted = res[0].visualize()
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cv2.imshow("result", res_plotted)
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```
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!!! example "`visualize()` arguments"
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`show_conf (bool)`: Show confidence
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