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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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4
docs/datasets/detect/sku-110k.md
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@ -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.

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docs/datasets/detect/xview.md
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@ -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.

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docs/datasets/pose/coco.md
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@ -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

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docs/hub/app/index.md
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@ -7,7 +7,7 @@ keywords: Ultralytics, HUB App, YOLOv5, YOLOv8, mobile AI, real-time object dete
# Ultralytics HUB App
<a href="https://bit.ly/ultralytics_hub" target="_blank">
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a>
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png" alt="Ultralytics HUB preview image"></a>
<br>
<div align="center">
<a href="https://github.com/ultralytics" style="text-decoration:none;">

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docs/hub/index.md
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@ -7,7 +7,7 @@ keywords: Ultralytics HUB, YOLOv5, YOLOv8, model training, model deployment, pre
# Ultralytics HUB
<a href="https://bit.ly/ultralytics_hub" target="_blank">
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a>
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png" alt="Ultralytics HUB preview image"></a>
<br>
<br>
<div align="center">

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docs/hub/models.md
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@ -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)

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docs/hub/projects.md
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@ -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"

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docs/index.md
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@ -38,7 +38,7 @@ Explore the YOLOv8 Docs, a comprehensive resource designed to help you understan
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> How to Train a YOLOv8 model on Your Custom Dataset in Google Colab.
<strong>Watch:</strong> How to Train a YOLOv8 model on Your Custom Dataset in <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb" target="_blank">Google Colab</a>.
</p>
## YOLO: A Brief History

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docs/integrations/index.md
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@ -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.
<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">
<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" alt="Ultralytics YOLO ecosystem and integrations">
## Datasets Integrations

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docs/integrations/ray-tune.md
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@ -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.

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docs/modes/benchmark.md
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@ -6,7 +6,7 @@ keywords: Ultralytics, YOLOv8, benchmarking, speed profiling, accuracy profiling
# Model Benchmarking with Ultralytics YOLO
<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">
<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" alt="Ultralytics YOLO ecosystem and integrations">
## Introduction

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docs/modes/export.md
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@ -6,7 +6,7 @@ keywords: YOLO, YOLOv8, Ultralytics, Model export, ONNX, TensorRT, CoreML, Tenso
# Model Export with Ultralytics YOLO
<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">
<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" alt="Ultralytics YOLO ecosystem and integrations">
## Introduction

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docs/modes/index.md
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@ -6,7 +6,7 @@ keywords: Ultralytics, YOLOv8, Machine Learning, Object Detection, Training, Val
# Ultralytics YOLOv8 Modes
<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">
<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" alt="Ultralytics YOLO ecosystem and integrations">
## Introduction

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docs/modes/predict.md
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@ -6,7 +6,7 @@ keywords: Ultralytics, YOLOv8, predict mode, inference sources, prediction tasks
# Model Prediction with Ultralytics YOLO
<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">
<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" alt="Ultralytics YOLO ecosystem and integrations">
## Introduction

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docs/modes/train.md
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@ -6,7 +6,7 @@ keywords: Ultralytics, YOLOv8, YOLO, object detection, train mode, custom datase
# Model Training with Ultralytics YOLO
<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">
<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" alt="Ultralytics YOLO ecosystem and integrations">
## Introduction

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docs/modes/val.md
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@ -6,7 +6,7 @@ keywords: Ultralytics, YOLO Docs, YOLOv8, validation, model evaluation, hyperpar
# Model Validation with Ultralytics YOLO
<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">
<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" alt="Ultralytics YOLO ecosystem and integrations">
## Introduction

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docs/tasks/index.md
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@ -7,7 +7,7 @@ keywords: Ultralytics, YOLOv8, Detection, Segmentation, Classification, Pose Est
# Ultralytics YOLOv8 Tasks
<br>
<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-tasks.png">
<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-tasks.png" alt="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.

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docs/zh/index.md
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@ -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 &nbsp; [: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 &nbsp; [: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 &nbsp; [: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 &nbsp; [: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 &nbsp; [:material-magnify-expand: 探索任务](https://docs.ultralytics.com/tasks/){ .md-button }
<p align="center">
@ -40,7 +40,7 @@ keywords: Ultralytics, YOLOv8, 目标检测, 图像分割, 机器学习, 深度
allowfullscreen>
</iframe>
<br>
<strong>观看：</strong> 在Google Colab中如何训练您的自定义数据集上的YOLOv8模型。
<strong>观看：</strong> 在<a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb" target="_blank">Google Colab</a>中如何训练您的自定义数据集上的YOLOv8模型。
</p>
## YOLO：简史