diff --git a/docs/en/help/privacy.md b/docs/en/help/privacy.md index 567a72aea5..fc669286d9 100644 --- a/docs/en/help/privacy.md +++ b/docs/en/help/privacy.md @@ -153,7 +153,8 @@ Ultralytics collects three primary types of data using Google Analytics: - **Usage Metrics**: These include how often and in what ways the YOLO Python package is used, preferred features, and typical command-line arguments. - **System Information**: General non-identifiable information about the computing environments where the package is run. - **Performance Data**: Metrics related to the performance of models during training, validation, and inference. - This data helps us enhance user experience and optimize software performance. Learn more in the [Anonymized Google Analytics](#anonymized-google-analytics) section. + +This data helps us enhance user experience and optimize software performance. Learn more in the [Anonymized Google Analytics](#anonymized-google-analytics) section. ### How can I disable data collection in the Ultralytics YOLO package? diff --git a/docs/en/help/security.md b/docs/en/help/security.md index 39fe3829ff..73d5e99c4d 100644 --- a/docs/en/help/security.md +++ b/docs/en/help/security.md @@ -17,7 +17,7 @@ We utilize [Snyk](https://snyk.io/advisor/python/ultralytics) to conduct compreh Our security strategy includes GitHub's [CodeQL](https://docs.github.com/en/code-security/code-scanning/introduction-to-code-scanning/about-code-scanning-with-codeql) scanning. CodeQL delves deep into our codebase, identifying complex vulnerabilities like SQL injection and XSS by analyzing the code's semantic structure. This advanced level of analysis ensures early detection and resolution of potential security risks. -[![CodeQL](https://github.com/ultralytics/ultralytics/actions/workflows/codeql.yaml/badge.svg)](https://github.com/ultralytics/ultralytics/actions/workflows/codeql.yaml) +[![CodeQL](https://github.com/ultralytics/ultralytics/actions/workflows/github-code-scanning/codeql/badge.svg)](https://github.com/ultralytics/ultralytics/actions/workflows/github-code-scanning/codeql) ## GitHub Dependabot Alerts diff --git a/docs/en/integrations/kaggle.md b/docs/en/integrations/kaggle.md index 920c5dbc84..cee6b847c9 100644 --- a/docs/en/integrations/kaggle.md +++ b/docs/en/integrations/kaggle.md @@ -127,7 +127,8 @@ Kaggle offers unique features that make it an excellent choice: - **Free Access to TPUs**: Speed up training with powerful TPUs without extra costs. - **Comprehensive History**: Track changes over time with a detailed history of notebook commits. - **Resource Availability**: Significant resources are provided for each notebook session, including 12 hours of execution time for CPU and GPU sessions. - For a comparison with Google Colab, refer to our [Google Colab guide](./google-colab.md). + +For a comparison with Google Colab, refer to our [Google Colab guide](./google-colab.md). ### How can I revert to a previous version of my Kaggle notebook? diff --git a/docs/en/models/yolo-nas.md b/docs/en/models/yolo-nas.md index 5523cb1b32..394bc83197 100644 --- a/docs/en/models/yolo-nas.md +++ b/docs/en/models/yolo-nas.md @@ -149,7 +149,8 @@ YOLO-NAS introduces several key features that make it a superior choice for obje - **Quantization-Friendly Basic Block:** Enhanced architecture that improves model performance with minimal [precision](https://www.ultralytics.com/glossary/precision) drop post quantization. - **Sophisticated Training and Quantization:** Employs advanced training schemes and post-training quantization techniques. - **AutoNAC Optimization and Pre-training:** Utilizes AutoNAC optimization and is pre-trained on prominent datasets like COCO, Objects365, and Roboflow 100. - These features contribute to its high accuracy, efficient performance, and suitability for deployment in production environments. Learn more in the [Key Features](#key-features) section. + +These features contribute to its high accuracy, efficient performance, and suitability for deployment in production environments. Learn more in the [Key Features](#key-features) section. ### Which tasks and modes are supported by YOLO-NAS models? diff --git a/docs/en/models/yolov7.md b/docs/en/models/yolov7.md index 1ba9dc271b..78fbbfa10c 100644 --- a/docs/en/models/yolov7.md +++ b/docs/en/models/yolov7.md @@ -151,4 +151,5 @@ YOLOv7 offers several key features that revolutionize real-time object detection - **Dynamic Label Assignment**: Uses a coarse-to-fine lead guided method to assign dynamic targets for outputs across different branches, improving accuracy. - **Extended and Compound Scaling**: Efficiently utilizes parameters and computation to scale the model for various real-time applications. - **Efficiency**: Reduces parameter count by 40% and computation by 50% compared to other state-of-the-art models while achieving faster inference speeds. - For further details on these features, see the [YOLOv7 Overview](#overview) section. + +For further details on these features, see the [YOLOv7 Overview](#overview) section. diff --git a/docs/en/modes/benchmark.md b/docs/en/modes/benchmark.md index 6680b8ce77..587462dfee 100644 --- a/docs/en/modes/benchmark.md +++ b/docs/en/modes/benchmark.md @@ -156,7 +156,8 @@ Exporting YOLO11 models to different formats such as ONNX, TensorRT, and OpenVIN - **ONNX:** Provides up to 3x CPU speedup. - **TensorRT:** Offers up to 5x GPU speedup. - **OpenVINO:** Specifically optimized for Intel hardware. - These formats enhance both the speed and accuracy of your models, making them more efficient for various real-world applications. Visit the [Export](../modes/export.md) page for complete details. + +These formats enhance both the speed and accuracy of your models, making them more efficient for various real-world applications. Visit the [Export](../modes/export.md) page for complete details. ### Why is benchmarking crucial in evaluating YOLO11 models? @@ -166,7 +167,8 @@ Benchmarking your YOLO11 models is essential for several reasons: - **Resource Allocation:** Gauge the performance across different hardware options. - **Optimization:** Determine which export format offers the best performance for specific use cases. - **Cost Efficiency:** Optimize hardware usage based on benchmark results. - Key metrics such as mAP50-95, Top-5 accuracy, and inference time help in making these evaluations. Refer to the [Key Metrics](#key-metrics-in-benchmark-mode) section for more information. + +Key metrics such as mAP50-95, Top-5 accuracy, and inference time help in making these evaluations. Refer to the [Key Metrics](#key-metrics-in-benchmark-mode) section for more information. ### Which export formats are supported by YOLO11, and what are their advantages? @@ -176,7 +178,8 @@ YOLO11 supports a variety of export formats, each tailored for specific hardware - **TensorRT:** Ideal for GPU efficiency. - **OpenVINO:** Optimized for Intel hardware. - **CoreML & [TensorFlow](https://www.ultralytics.com/glossary/tensorflow):** Useful for iOS and general ML applications. - For a complete list of supported formats and their respective advantages, check out the [Supported Export Formats](#supported-export-formats) section. + +For a complete list of supported formats and their respective advantages, check out the [Supported Export Formats](#supported-export-formats) section. ### What arguments can I use to fine-tune my YOLO11 benchmarks? @@ -189,4 +192,5 @@ When running benchmarks, several arguments can be customized to suit specific ne - **int8:** Activate INT8 quantization for edge devices. - **device:** Specify the computation device (e.g., "cpu", "cuda:0"). - **verbose:** Control the level of logging detail. - For a full list of arguments, refer to the [Arguments](#arguments) section. + +For a full list of arguments, refer to the [Arguments](#arguments) section. diff --git a/docs/en/usage/cfg.md b/docs/en/usage/cfg.md index a7fef9e2ac..95dc8b46f2 100644 --- a/docs/en/usage/cfg.md +++ b/docs/en/usage/cfg.md @@ -186,7 +186,8 @@ Default inference settings include: - **IoU Threshold (`iou=0.7`)**: For Non-Maximum Suppression (NMS). - **Image Size (`imgsz=640`)**: Resizes input images prior to inference. - **Device (`device=None`)**: Selects CPU or GPU for inference. - For a comprehensive overview, visit the [Predict Settings](#predict-settings) section and the [Predict Guide](../modes/predict.md). + +For a comprehensive overview, visit the [Predict Settings](#predict-settings) section and the [Predict Guide](../modes/predict.md). ### Why should I use mixed precision training with YOLO models?