diff --git a/README.md b/README.md index 1a9d3785..5ee71729 100644 --- a/README.md +++ b/README.md @@ -109,7 +109,7 @@ path = model.export(format="onnx") # export the model to ONNX format YOLOv8 [Detect](https://docs.ultralytics.com/tasks/detect), [Segment](https://docs.ultralytics.com/tasks/segment) and [Pose](https://docs.ultralytics.com/tasks/pose) models pretrained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco) dataset are available here, as well as YOLOv8 [Classify](https://docs.ultralytics.com/tasks/classify) models pretrained on the [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet) dataset. [Track](https://docs.ultralytics.com/modes/track) mode is available for all Detect, Segment and Pose models. - +Ultralytics YOLO supported tasks All [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use. @@ -243,7 +243,7 @@ Our key integrations with leading AI platforms extend the functionality of Ultra Experience seamless AI with [Ultralytics HUB](https://bit.ly/ultralytics_hub) ⭐, the all-in-one solution for data visualization, YOLOv5 and YOLOv8 🚀 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! - +Ultralytics HUB preview image ##
Contribute
diff --git a/README.zh-CN.md b/README.zh-CN.md index 95593a05..98ec523a 100644 --- a/README.zh-CN.md +++ b/README.zh-CN.md @@ -109,7 +109,7 @@ success = model.export(format="onnx") # 将模型导出为 ONNX 格式 在[COCO](https://docs.ultralytics.com/datasets/detect/coco)数据集上预训练的YOLOv8 [检测](https://docs.ultralytics.com/tasks/detect)，[分割](https://docs.ultralytics.com/tasks/segment)和[姿态](https://docs.ultralytics.com/tasks/pose)模型可以在这里找到，以及在[ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet)数据集上预训练的YOLOv8 [分类](https://docs.ultralytics.com/tasks/classify)模型。所有的检测，分割和姿态模型都支持[追踪](https://docs.ultralytics.com/modes/track)模式。 - +Ultralytics YOLO supported tasks 所有[模型](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models)在首次使用时会自动从最新的Ultralytics [发布版本](https://github.com/ultralytics/assets/releases)下载。 @@ -242,7 +242,7 @@ success = model.export(format="onnx") # 将模型导出为 ONNX 格式 体验 [Ultralytics HUB](https://bit.ly/ultralytics_hub) ⭐ 带来的无缝 AI，这是一个一体化解决方案，用于数据可视化、YOLOv5 和即将推出的 YOLOv8 🚀 模型训练和部署，无需任何编码。通过我们先进的平台和用户友好的 [Ultralytics 应用程序](https://ultralytics.com/app_install)，轻松将图像转化为可操作的见解，并实现您的 AI 愿景。现在就开始您的**免费**之旅！ - +Ultralytics HUB preview image ##
贡献
diff --git a/docs/datasets/detect/sku-110k.md b/docs/datasets/detect/sku-110k.md index bfa2b49f..8e6aac13 100644 --- a/docs/datasets/detect/sku-110k.md +++ b/docs/datasets/detect/sku-110k.md @@ -8,7 +8,7 @@ keywords: SKU-110k dataset, object detection, retail shelf images, Ultralytics, The [SKU-110k](https://github.com/eg4000/SKU110K_CVPR19) dataset is a collection of densely packed retail shelf images, designed to support research in object detection tasks. Developed by Eran Goldman et al., the dataset contains over 110,000 unique store keeping unit (SKU) categories with densely packed objects, often looking similar or even identical, positioned in close proximity. -![Dataset sample image](https://github.com/eg4000/SKU110K_CVPR19/raw/master/figures/benchmarks_comparison.jpg) +![Dataset sample image](https://user-images.githubusercontent.com/26833433/277141199-e7cdd803-237e-4b4a-9171-f95cba9388f9.jpg) ## Key Features @@ -67,7 +67,7 @@ To train a YOLOv8n model on the SKU-110K dataset for 100 epochs with an image si The SKU-110k dataset contains a diverse set of retail shelf images with densely packed objects, providing rich context for object detection tasks. Here are some examples of data from the dataset, along with their corresponding annotations: -![Dataset sample image](https://user-images.githubusercontent.com/26833433/238215979-1ab791c4-15d9-46f6-a5d6-0092c05dff7a.jpg) +![Dataset sample image](https://user-images.githubusercontent.com/26833433/277141197-b63e4aa5-12f6-4673-96a7-9a5207363c59.jpg) - **Densely packed retail shelf image**: This image demonstrates an example of densely packed objects in a retail shelf setting. Objects are annotated with bounding boxes and SKU category labels. diff --git a/docs/datasets/detect/xview.md b/docs/datasets/detect/xview.md index e47268d2..51e88645 100644 --- a/docs/datasets/detect/xview.md +++ b/docs/datasets/detect/xview.md @@ -69,7 +69,7 @@ To train a model on the xView dataset for 100 epochs with an image size of 640, The xView dataset contains high-resolution satellite images with a diverse set of objects annotated using bounding boxes. Here are some examples of data from the dataset, along with their corresponding annotations: -![Dataset sample image](https://github-production-user-asset-6210df.s3.amazonaws.com/26833433/238799379-bb3b02f0-dee4-4e67-80ae-4b2378b813ad.jpg) +![Dataset sample image](https://user-images.githubusercontent.com/26833433/277141257-ae6ba4de-5dcb-4c76-bc05-bc1e386361ba.jpg) - **Overhead Imagery**: This image demonstrates an example of object detection in overhead imagery, where objects are annotated with bounding boxes. The dataset provides high-resolution satellite images to facilitate the development of models for this task. diff --git a/docs/datasets/pose/coco.md b/docs/datasets/pose/coco.md index 899a667c..b9221d22 100644 --- a/docs/datasets/pose/coco.md +++ b/docs/datasets/pose/coco.md @@ -8,7 +8,7 @@ keywords: Ultralytics YOLO, COCO-Pose Dataset, Deep Learning, Pose Estimation, T The [COCO-Pose](https://cocodataset.org/#keypoints-2017) dataset is a specialized version of the COCO (Common Objects in Context) dataset, designed for pose estimation tasks. It leverages the COCO Keypoints 2017 images and labels to enable the training of models like YOLO for pose estimation tasks. -![Pose sample image](https://user-images.githubusercontent.com/26833433/239691398-d62692dc-713e-4207-9908-2f6710050e5c.jpg) +![Pose sample image](https://user-images.githubusercontent.com/26833433/277141128-cd62d09e-1eb0-4d20-9938-c55239a5cb76.jpg) ## Key Features diff --git a/docs/hub/app/index.md b/docs/hub/app/index.md index dd44d954..581125fa 100644 --- a/docs/hub/app/index.md +++ b/docs/hub/app/index.md @@ -7,7 +7,7 @@ keywords: Ultralytics, HUB App, YOLOv5, YOLOv8, mobile AI, real-time object dete # Ultralytics HUB App - + Ultralytics HUB preview image
diff --git a/docs/hub/index.md b/docs/hub/index.md index 76bf9e6d..6f428602 100644 --- a/docs/hub/index.md +++ b/docs/hub/index.md @@ -7,7 +7,7 @@ keywords: Ultralytics HUB, YOLOv5, YOLOv8, model training, model deployment, pre # Ultralytics HUB - + Ultralytics HUB preview image

diff --git a/docs/hub/models.md b/docs/hub/models.md index 64e51f87..a302cd3e 100644 --- a/docs/hub/models.md +++ b/docs/hub/models.md @@ -174,7 +174,7 @@ Now, anyone who has the direct link to your model can view it. ??? tip "Tip" - You can easily click on the models's link shown in the **Share Model** dialog to copy it. + You can easily click on the model's link shown in the **Share Model** dialog to copy it. ![Ultralytics HUB screenshot of the Share Model dialog with an arrow pointing to the model's link](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_share_model_4.jpg) diff --git a/docs/hub/projects.md b/docs/hub/projects.md index 7dfd45f8..b11aee62 100644 --- a/docs/hub/projects.md +++ b/docs/hub/projects.md @@ -110,7 +110,7 @@ Navigate to the Project page of the project you want to delete, open the project !!! warning "Warning" - When deleting a project, the the models inside the project will be deleted as well. + When deleting a project, the models inside the project will be deleted as well. ??? note "Note" diff --git a/docs/index.md b/docs/index.md index cf391a50..77e463f7 100644 --- a/docs/index.md +++ b/docs/index.md @@ -38,7 +38,7 @@ Explore the YOLOv8 Docs, a comprehensive resource designed to help you understan allowfullscreen>
- Watch: How to Train a YOLOv8 model on Your Custom Dataset in Google Colab. + Watch: How to Train a YOLOv8 model on Your Custom Dataset in Google Colab.

## YOLO: A Brief History diff --git a/docs/integrations/index.md b/docs/integrations/index.md index 4446756a..3ee24ef0 100644 --- a/docs/integrations/index.md +++ b/docs/integrations/index.md @@ -8,7 +8,7 @@ keywords: Ultralytics integrations, Roboflow, Neural Magic, ClearML, Comet ML, D Welcome to the Ultralytics Integrations page! This page provides an overview of our partnerships with various tools and platforms, designed to streamline your machine learning workflows, enhance dataset management, simplify model training, and facilitate efficient deployment. - +Ultralytics YOLO ecosystem and integrations ## Datasets Integrations diff --git a/docs/integrations/ray-tune.md b/docs/integrations/ray-tune.md index ec7817c0..46eb5ffb 100644 --- a/docs/integrations/ray-tune.md +++ b/docs/integrations/ray-tune.md @@ -120,11 +120,11 @@ In this example, we demonstrate how to use a custom search space for hyperparame In the code snippet above, we create a YOLO model with the "yolov8n.pt" pretrained weights. Then, we call the `tune()` method, specifying the dataset configuration with "coco128.yaml". We provide a custom search space for the initial learning rate `lr0` using a dictionary with the key "lr0" and the value `tune.uniform(1e-5, 1e-1)`. Finally, we pass additional training arguments, such as the number of epochs directly to the tune method as `epochs=50`. -# Processing Ray Tune Results +## Processing Ray Tune Results After running a hyperparameter tuning experiment with Ray Tune, you might want to perform various analyses on the obtained results. This guide will take you through common workflows for processing and analyzing these results. -## Loading Tune Experiment Results from a Directory +### Loading Tune Experiment Results from a Directory After running the tuning experiment with `tuner.fit()`, you can load the results from a directory. This is useful, especially if you're performing the analysis after the initial training script has exited. @@ -136,7 +136,7 @@ restored_tuner = tune.Tuner.restore(experiment_path, trainable=train_mnist) result_grid = restored_tuner.get_results() ``` -## Basic Experiment-Level Analysis +### Basic Experiment-Level Analysis Get an overview of how trials performed. You can quickly check if there were any errors during the trials. @@ -147,7 +147,7 @@ else: print("No errors!") ``` -## Basic Trial-Level Analysis +### Basic Trial-Level Analysis Access individual trial hyperparameter configurations and the last reported metrics. @@ -156,7 +156,7 @@ for i, result in enumerate(result_grid): print(f"Trial #{i}: Configuration: {result.config}, Last Reported Metrics: {result.metrics}") ``` -## Plotting the Entire History of Reported Metrics for a Trial +### Plotting the Entire History of Reported Metrics for a Trial You can plot the history of reported metrics for each trial to see how the metrics evolved over time. diff --git a/docs/modes/benchmark.md b/docs/modes/benchmark.md index 431ee917..cdee300f 100644 --- a/docs/modes/benchmark.md +++ b/docs/modes/benchmark.md @@ -6,7 +6,7 @@ keywords: Ultralytics, YOLOv8, benchmarking, speed profiling, accuracy profiling # Model Benchmarking with Ultralytics YOLO - +Ultralytics YOLO ecosystem and integrations ## Introduction diff --git a/docs/modes/export.md b/docs/modes/export.md index 5345172c..d0c2da00 100644 --- a/docs/modes/export.md +++ b/docs/modes/export.md @@ -6,7 +6,7 @@ keywords: YOLO, YOLOv8, Ultralytics, Model export, ONNX, TensorRT, CoreML, Tenso # Model Export with Ultralytics YOLO - +Ultralytics YOLO ecosystem and integrations ## Introduction diff --git a/docs/modes/index.md b/docs/modes/index.md index 315347e9..64dc8d9f 100644 --- a/docs/modes/index.md +++ b/docs/modes/index.md @@ -6,7 +6,7 @@ keywords: Ultralytics, YOLOv8, Machine Learning, Object Detection, Training, Val # Ultralytics YOLOv8 Modes - +Ultralytics YOLO ecosystem and integrations ## Introduction diff --git a/docs/modes/predict.md b/docs/modes/predict.md index be1c71f6..7c928169 100644 --- a/docs/modes/predict.md +++ b/docs/modes/predict.md @@ -6,7 +6,7 @@ keywords: Ultralytics, YOLOv8, predict mode, inference sources, prediction tasks # Model Prediction with Ultralytics YOLO - +Ultralytics YOLO ecosystem and integrations ## Introduction diff --git a/docs/modes/train.md b/docs/modes/train.md index 7bb462e9..e3db6c30 100644 --- a/docs/modes/train.md +++ b/docs/modes/train.md @@ -6,7 +6,7 @@ keywords: Ultralytics, YOLOv8, YOLO, object detection, train mode, custom datase # Model Training with Ultralytics YOLO - +Ultralytics YOLO ecosystem and integrations ## Introduction diff --git a/docs/modes/val.md b/docs/modes/val.md index 1e6648cd..b964ee7b 100644 --- a/docs/modes/val.md +++ b/docs/modes/val.md @@ -6,7 +6,7 @@ keywords: Ultralytics, YOLO Docs, YOLOv8, validation, model evaluation, hyperpar # Model Validation with Ultralytics YOLO - +Ultralytics YOLO ecosystem and integrations ## Introduction diff --git a/docs/tasks/index.md b/docs/tasks/index.md index b43dbd30..c50d164a 100644 --- a/docs/tasks/index.md +++ b/docs/tasks/index.md @@ -7,7 +7,7 @@ keywords: Ultralytics, YOLOv8, Detection, Segmentation, Classification, Pose Est # Ultralytics YOLOv8 Tasks
- +Ultralytics YOLO supported tasks YOLOv8 is an AI framework that supports multiple computer vision **tasks**. The framework can be used to perform [detection](detect.md), [segmentation](segment.md), [classification](classify.md), and [pose](pose.md) estimation. Each of these tasks has a different objective and use case. diff --git a/docs/zh/index.md b/docs/zh/index.md index cd67f0bc..e4ed42f1 100644 --- a/docs/zh/index.md +++ b/docs/zh/index.md @@ -29,7 +29,7 @@ keywords: Ultralytics, YOLOv8, 目标检测, 图像分割, 机器学习, 深度 - **安装** `ultralytics` 并通过 pip 在几分钟内开始运行   [:material-clock-fast: 开始使用](https://docs.ultralytics.com/quickstart/){ .md-button } - **预测** 使用YOLOv8预测新的图像和视频   [:octicons-image-16: 在图像上预测](https://docs.ultralytics.com/predict/){ .md-button } -- **训练** 在您自己的自定义数据集上训练新的YOLOv8模型   [:fontawesome-solid-brain: 训练模型](https://docs.ultralytics.com/train/){ .md-button } +- **训练** 在您自己的自定义数据集上训练新的YOLOv8模型   [:fontawesome-solid-brain: 训练模型](https://docs.ultralytics.com/modes/train/){ .md-button } - **探索** YOLOv8的任务，如分割、分类、姿态和跟踪   [:material-magnify-expand: 探索任务](https://docs.ultralytics.com/tasks/){ .md-button }

@@ -40,7 +40,7 @@ keywords: Ultralytics, YOLOv8, 目标检测, 图像分割, 机器学习, 深度 allowfullscreen>
- 观看： 在Google Colab中如何训练您的自定义数据集上的YOLOv8模型。 + 观看： 在Google Colab中如何训练您的自定义数据集上的YOLOv8模型。

## YOLO：简史 diff --git a/mkdocs.yml b/mkdocs.yml index 848e9733..12a352ab 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -67,7 +67,7 @@ extra: analytics: provider: google property: G-2M5EHKC0BH - alternate: # language drop-down + alternate: # language drop-down - name: English link: / lang: en @@ -110,8 +110,8 @@ markdown_extensions: - pymdownx.snippets: base_path: ./ - pymdownx.emoji: - emoji_index: !!python/name:materialx.emoji.twemoji # noqa - emoji_generator: !!python/name:materialx.emoji.to_svg + emoji_index: !!python/name:material.extensions.emoji.twemoji + emoji_generator: !!python/name:material.extensions.emoji.to_svg - pymdownx.tabbed: alternate_style: true